Documents published in 2020 : Scipedia

Research on Joint Control of On-Ramp Metering and Mainline Speed Guidance in the Urban Expressway Based on MPC and Connected Vehicles
Y. Ci, W. Qi, Y. Sun, L. Wu

Collection of open articles in transport research (2020). Vol. 2020, 338

Abstract
The traffic operational efficiency of the urban expressway system will affect one of the entire cities. Moreover, the idea that traffic control can improve the traffic operational efficiency of the urban expressway system has been fully confirmed. At present, the main control methods include on-ramp metering and speed guidance control. However, there is a gap in using these two control methods together, such as unclear application conditions and unsystematic methods. In this paper, on-ramp metering and speed guidance control are combined effectively. Based on the research of METANET macroscopic traffic flow model and model predictive control (MPC), a novel joint control method based on MPC and connected vehicles (CVs) for on-ramp metering and speed guidance control of the urban expressway is proposed. Finally, the simulation results show that the proposed control method can effectively improve the traffic efficiency and traffic safety. Document type: Article
Abstract
The traffic operational efficiency of the urban expressway system will affect one of the entire cities. Moreover, the idea that traffic control can improve the traffic operational [...]
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A Model for Sustainable Courier Services: Vehicle Routing with Exclusive Lanes
K. Lee, D. Choi, B. Song, J. Chae

Collection of open articles in transport research (2020). Vol. 2020, 337

Abstract
In Southeast Asian cities, it is common for logistic companies to operate a heterogeneous fleet of delivery vehicles with motorcycles being the preferred vehicle to handle the final phase of delivery. In such scenarios, heterogeneous fleet vehicle routing problem (HFVRP) is generally applied to plan an optimal delivery. However, in many downtown cores of large and rapidly developing Southeast Asian cities, HFVRP is neither viable nor reliable because of road usage restrictions. The purpose of this article is to develop and test a different approach that accurately takes these restrictions into account and provides viable and more sustainable results. Restrictions in this paper refer to situations of urban areas in Vietnam where (i) certain vehicle types are prohibited in specified areas or where narrow alleyways limit the utilization of vehicles that exceed the road capacity and (ii) certain roads are exclusive to certain vehicle types. In networks, limited access and exclusive lanes are represented as links, or arcs, exclusive to one or another. Taking these limitations into consideration, we have developed a unique model, which we have termed Vehicle Routing Problem with Exclusive Links (VRP-EL). The model was validated and tested for its performance on scenarios with varying ratios of exclusive links. Scenarios up to 500 customers were tested on a meta-heuristic algorithm, simulated annealing. VRP-EL produces realistic outcomes. Limiting certain links to be selected according to vehicle types increases overall travel distance. However, this increase outweighs the cost of re-planning and rerouting had they not been constrained initially. The reduction in traveling distance leads to fossil fuel reduction for the overall system. The estimation of reduced carbon emissions through applying the proposed model is presented. Considering the severe traffic congestion and carbon emissions caused by motorcycles in Vietnam, the proposed model leads to a sustainable road environment. Document type: Article
Abstract
In Southeast Asian cities, it is common for logistic companies to operate a heterogeneous fleet of delivery vehicles with motorcycles being the preferred vehicle to handle [...]
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DroneBased Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
H. Jung, H. Moon, J. Kim

Collection of open articles in transport research (2020). Vol. 2020, 336

Abstract
In general, the demand for delivery cannot be fulfilled efficiently due to the excessive traffic in dense urban areas. Therefore, many innovative concepts for intelligent transportation of freight have recently been developed. One of these concepts relies on drone-based parcel delivery using rooftops of city buildings. To apply drone logistics system in cities, the operation design should be adequately prepared. In this regard, a mixed integer programming model for drone operation planning and a heuristic based on block stacking are newly proposed to provide solutions. Additionally, numerical experiments with three different problem sizes are conducted to check the feasibility of the proposed model and to assess the performance of the proposed heuristic. The experimental results show that the proposed model seems to be viable and that the developed heuristic provides very good operation plans in terms of the optimality gap and the computation time. Document type: Article
Abstract
In general, the demand for delivery cannot be fulfilled efficiently due to the excessive traffic in dense urban areas. Therefore, many innovative concepts for intelligent [...]
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Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data
Q. Tang, X. Hu, M. Li, R. He, Y. Deng

Collection of open articles in transport research (2020). Vol. 2020, 334

Abstract
Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters’ route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi’an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters’ route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters’ route choice behaviors, when compared with the traditional MNL model. Document type: Article
Abstract
Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused [...]
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Features of Earthquake-Induced Seabed Liquefaction and Mitigation Strategies of Novel Marine Structures
Y. Huang, X. Han

Collection of open articles in transport research (2020). Vol. 2020, 333

Abstract
With the accelerated development of marine engineering, a growing number of marine structures are being constructed (e.g., seabed pipelines, drilling platforms, oil platforms, wind turbines). However, seismic field investigations over recent decades have shown that many marine structures were damaged or destroyed due to liquefaction. Seismic liquefaction in marine engineering can have huge financial repercussions as well as a devastating effect on the marine environment, which merits our great attention. As the effects of seawater and the gas component in the seabed layers are not negligible, the seabed soil layers are more prone to liquefaction than onshore soil layers, and the liquefied area may be larger than when liquefaction occurs on land. To mitigate the impact of liquefaction events on marine engineering structures, some novel liquefaction-resistant marine structures have been proposed in recent years. This paper reviews the features of earthquake-induced liquefaction and the mitigation strategies for marine structures to meet the future requirements of marine engineering. Document type: Article
Abstract
With the accelerated development of marine engineering, a growing number of marine structures are being constructed (e.g., seabed pipelines, drilling platforms, oil platforms, [...]
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Fuel Economy in Truck Platooning: A Literature Overview and Directions for Future Research
X. Ma, F. Chen, X. Pan, L. Zhang

Collection of open articles in transport research (2020). Vol. 2020, 332

Abstract
A truck platoon is a set of virtually linked trucks that travel in tandem with small intervehicle distances. Several studies have proved that traveling in platoons can significantly improve fuel economy due to the reduced aerodynamic drag. However, most literature only provides scattered pieces of information regarding fuel economy in truck platoons. Therefore, a literature survey is needed to understand what has been studied and what problems remain to be further addressed. This paper presents an overview of existing studies to illustrate the state of the art about fuel savings for truck platooning. Specifically, it summarized the methodologies, the contributing factors of fuel consumption, the coordination methods to improve the platooning rate, and the look-ahead control strategies to generate fuel-efficient speed profiles for each vehicle driving in a platoon over different road grades. After that, the autonomous truck platooning was introduced, and we raised and discussed a couple of outstanding questions to be addressed in future work. Document type: Article
Abstract
A truck platoon is a set of virtually linked trucks that travel in tandem with small intervehicle distances. Several studies have proved that traveling in platoons can significantly [...]
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Profitability of Frequency Regulation by Electric Vehicles in Denmark and Japan Considering Battery Degradation Costs
M. Marinelli, L. Calearo

Collection of open articles in transport research (2020). Vol. 2020, 330

Abstract
This paper determines the profitability of the primary frequency regulation (FR) service considering the wear of the electric vehicle (EV) battery as a cost. To evaluate the profitability of the FR service, the cost of degradation from FR provision is separated from the degradation caused by driving usage. During FR, the power response is proportional to the frequency deviation with full activation power of 9.2 kW, when deviations are larger than 100 mHz. The degradation due to FR is found to be an additional 1–2% to the 7–12% capacity reduction of a 40 kWh Lithium-ion NMC battery pack over 5 years. The overall economic framework is applied in Denmark, both DK1 and DK2, and Japan, by considering historical frequencies. The DK2 FR market framework is taken as reference also for the Japanese and the DK1 cases. Electricity prices and charger efficiency are the two main parameters that affect the profitability of the service. Indeed, with domestic prices there is no profitability, whereas with industrial prices, despite differences between the frequencies, the service is similarly profitable with approx. 3500€ for a five-year period. Document type: Article
Abstract
This paper determines the profitability of the primary frequency regulation (FR) service considering the wear of the electric vehicle (EV) battery as a cost. To evaluate the [...]
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Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles
S. Faas, A. Kao, M. Baumann, S. Mattes

Collection of open articles in transport research (2020). Vol. 2020, 328

Abstract
With self-driving vehicles (SDVs), pedestrians can no longer rely on a human driver. Previous research suggests that pedestrians may benefit from an external Human–Machine Interface (eHMI) displaying information to surrounding traffic participants. This paper introduces a natural methodology to compare eHMI concepts from a pedestrian’s viewpoint. To measure eHMI effects on traffic flow, previous video-based studies instructed participants to indicate their crossing decision with interfering data collection devices, such as pressing a button or slider. We developed a quantifiable concept that allows participants to naturally step off a sidewalk to cross the street. Hidden force-sensitive resistor sensors recorded their crossing onset time (COT) in response to real-life videos of approaching vehicles in an immersive crosswalk simulation environment. We validated our method with an initial study of N = 34 pedestrians by showing (1) that it is able to detect significant eHMI effects on COT as well as subjective measures of perceived safety and user experience. The approach is further validated by (2) replicating the findings of a test track study and (3) participants’ reports that it felt natural to take a step forward to indicate their street crossing decision. We discuss the benefits and limitations of our method with regard to related approaches. Document type: Article
Abstract
With self-driving vehicles (SDVs), pedestrians can no longer rely on a human driver. Previous research suggests that pedestrians may benefit from an external Human–Machine [...]
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Evaluating Cyclists’ Route Preferences with Respect to Infrastructure
M. Hardinghaus, P. Papantoniou

Collection of open articles in transport research (2020). Vol. 2020, 327

Abstract
Providing a sufficiently appropriate route environment is crucial to ensuring fair and safe biking, thus encouraging cycling as a sustainable mode of transport. At the same time, better understanding of cyclists&rsquo preferences regarding the features of their routes and their infrastructure requirements is fundamental to evaluating improvement of the current infrastructure or the development of new infrastructure. The present study has two objectives. The first is to investigate cyclists&rsquo route preferences by means of a choice experiment based on a stated preference survey. Subsequently, the second objective is to compare cyclist preferences in two countries with different cycling characteristics (both in infrastructure as well as cyclists&rsquo behavior). For this purpose, a graphical online stated preferences survey was conducted in Greece and Germany. Within the framework of statistical analyses, multinomial mixed logit discrete choice models were developed that allow us to quantify the trade-offs of interest, while distinguishing between the preferences of different user groups. In addition, user requirements in Greece, as a country with a low cycling share and very little dedicated bike infrastructure, were compared to the requirements in Germany, where cycling is popular and the infrastructure is well developed. The results over the whole sample indicate that subgroups value infrastructure differently according to their specific needs. When looking at country specifics, users from Greece are significantly more willing to accept longer travel times in return for higher-quality facilities. The utility of low speed limits in mixed traffic is also different. In Germany, low speed limits offset the disturbance caused by motorized traffic, but in Greece they do not. Consequently, the results help to asses which types of infrastructure are most sustainable from a user perspective and help to set priorities when the aim is to adapt the road infrastructure efficiently in a stable strategy. Document type: Article
Abstract
Providing a sufficiently appropriate route environment is crucial to ensuring fair and safe biking, thus encouraging cycling as a sustainable mode of transport. At the same [...]
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Evaluating the impact of new aircraft separation minima on available airspace capacity and arrival time delay
E. Itoh, M. Mitici

Collection of open articles in transport research (2020). Vol. 2020, 325

Abstract
Although the application of new, reduced aircraft separation minima can directly increase runway throughput, the impact thereof on the traffic flow of aircraft arriving at the destination airport has not been discussed yet. This paper proposes a data-driven and queue-based modeling approach and presents an analysis of the impact on the delay time of arriving aircraft in the airspace within a radius of 100 nautical miles around an airport. The parameters of our queuing model were estimated by analysing the data contained in the radar tracks and flight plans for flights that arrived at Tokyo International Airport during the 2 years of 2016 and 2017. The results clarified the best arrival strategy according to the distance from the arrival airport: The combination of airspace capacity control and reduction of the flight time and separation variance is the most powerful solution to mitigate delays experienced by arriving traffic while also allowing an increase in the amount of arrival traffic. The application of new wake vortex categories would enable us to increase the arrival traffic to 120%. In addition, the arrival delay time could be minimised by implementing the proposed arrival traffic strategies together with automation support for air traffic controllers. Document type: Article
Abstract
Although the application of new, reduced aircraft separation minima can directly increase runway throughput, the impact thereof on the traffic flow of aircraft arriving at [...]
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Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand
Y. Yang, Y. Xu, X. Wang, Q. Huang

Collection of open articles in transport research (2020). Vol. 2020, 324

Abstract
With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate. Document type: Article
Abstract
With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually [...]
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Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection
T. Kundinger, A. Riener, N. Sofra

Collection of open articles in transport research (2020). Vol. 2020, 323

Abstract
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (> 92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human&ndash machine interaction in a car and especially for driver state monitoring in the field of automated driving. Document type: Article
Abstract
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces [...]
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CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data
J. Choi, Y. Chung, J. Park

Collection of open articles in transport research (2020). Vol. 2020, 322

Abstract
A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot. Document type: Article
Abstract
A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such [...]
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Does a Confidence Level for Automated Driving Time Estimations Improve the Subjective Evaluation of an Automation HMI?
A. Feierle, S. Danner, K. Bengler, T. Hecht

Collection of open articles in transport research (2020). Vol. 2020, 321

Abstract
Current research in human factors and automated driving is increasingly focusing on predictable transitions instead of urgent and critical take-overs. Predictive human–machine interface (HMI) elements displaying the remaining time until the next request to intervene were identified as a user need, especially when the user is engaging in non-driving related activities (NDRA). However, these estimations are prone to errors due to changing traffic conditions and updated map-based information. Thus, we investigated a confidence display for Level 3 automated driving time estimations. Based on a preliminary study, a confidence display resembling a mobile phone connectivity symbol was developed. In a mixed-design driving simulator study with 32 participants, we assessed the impact of the confidence display concept (within factor) on usability, frustration, trust and acceptance during city and highway automated driving (between factor). During automated driving sections, participants engaged in a naturalistic visual NDRA to create a realistic scenario. Significant effects were found for the scenario: participants in the city experienced higher levels of frustration. However, the confidence display has no significant impact on the subjective evaluation and most participants preferred the baseline HMI without a confidence symbol. Document type: Article
Abstract
Current research in human factors and automated driving is increasingly focusing on predictable transitions instead of urgent and critical take-overs. Predictive human–machine [...]
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A framework for describing interaction between human operators and autonomous, automated, and manual control systems
B. Johansson, J. Lundberg

Collection of open articles in transport research (2020). Vol. 2020, 320

Abstract
This paper addresses how to describe critical episodes of interaction between human operators and autonomous, automated, and manual control systems. The first part of the paper poses three questions: (1) what levels of cognitive control are important to include in a descriptive framework for joint human-autonomy in process control; (2) how should one describe temporal developments in joint socio-technical systems; and (3) how does one analyse communication and control at the system joints. The paper proceeds by proposing a new framework for description and analysis, the Joint Control Framework (JCF), with a simple notation, the Score (JCF-S). It allows descriptions of the three previously mentioned aspects through three analytical activities: process mapping (PM), analysis of Levels of Autonomy in Cognitive Control (LACC), and temporal descriptions of human-machine interaction (T-HMI) through the Score notation. This facilitates analyses across cases and domains. The framework is discussed based on an analysis of two episodes; one work episode (from an air traffic control tower simulator); and one work procedure (from an unmanned traffic management system design concept). "p"Funding Agencies|Linkoping University - Swedish Transport Administration; Air Navigation Services of Sweden Document type: Article
Abstract
This paper addresses how to describe critical episodes of interaction between human operators and autonomous, automated, and manual control systems. The first part of the [...]
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Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data
C. Wu, H. Zhang, S. Li, Y. Wang, Q. Zhang

Collection of open articles in transport research (2020). Vol. 2020, 319

Abstract
Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway. Document type: Article
Abstract
Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash [...]
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Maritime Transport in a Life Cycle Perspective: How Fuels, Vessel Types, and Operational Profiles Influence Energy Demand and Greenhouse Gas Emissions
D. Giannopoulos, A. Bonou, G. Seithe, C. Georgopoulou, M. Founti

Collection of open articles in transport research (2020). Vol. 2020, 318

Abstract
&ldquo Well-to-Propeller&rdquo Life Cycle Assessment of maritime transport was performed with a European geographical focus. Four typical types of vessels with specific operational profiles were assessed: a container vessel and a tanker (both with 2-stroke engines), a passenger roll-on/roll-off (Ro-Pax) and a cruise vessel (both with 4-stroke engines). All main engines were dual fuel operated with Heavy Fuel Oil (HFO) or Liquefied Natural Gas (LNG). Alternative onshore and offshore fuel supply chains were considered. Primary energy use and greenhouse gas emissions were assessed. Raw material extraction was found to be the most impactful life cycle stage (~90% of total energy use). Regarding greenhouse gases, liquefaction was the key issue. When transitioning from HFO to LNG, the systems were mainly influenced by a reduction in cargo capacity due to bunkering requirements and methane slip, which depends on the fuel supply chain (onshore has 64% more slip than offshore) and the engine type (4-stroke engines have 20% more slip than 2-stroke engines). The combination of alternative fuel supply chains and specific operational profiles allowed for a complete system assessment. The results demonstrated that multiple opposing drivers affect the environmental performance of maritime transport, a useful insight towards establishing emission abatement strategies. Document type: Article
Abstract
&ldquo Well-to-Propeller&rdquo Life Cycle Assessment of maritime transport was performed with a European geographical focus. Four typical types of vessels with specific [...]
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Smart Edge Broker for Location-Based Transfer between Services and Distributed Data in IoT Smart Services
J. Ahn, B. Lee

Collection of open articles in transport research (2020). Vol. 2020, 317

Abstract
Various kinds of smart sensors are being developed and released with the growth of sensor technology and Internet of Things (IoT) technology. IoT smart service can provide convenience in our daily lives with these smart sensors. However, the increasing number of mobile sensors and amount of data may increase latency. It may cause network congestion on a particular network. Therefore, we propose a Smart Edge Broker (SEB) to intelligently transmit data traffic generated by a smart city in this paper. SEB can prevent the traffic congestion from being transmitted or bypassed to a location where traffic is not necessary. In addition, SEB can prevent overload in a specific area between services through a location-based transfer. Plus, SEB is suitable to operate it as a fog computing model by placing it at the edge of a smart city network. We conducted a latency measurement experiment and load measurement experiment to evaluate the effectiveness of the proposed Smart Edge Broker. As a result, we found that the latency was reduced by 72%, and the CPU usage was reduced by 63% compared to when the Smart Edge Broker was not used. Document type: Article
Abstract
Various kinds of smart sensors are being developed and released with the growth of sensor technology and Internet of Things (IoT) technology. IoT smart service can provide [...]
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Modified Maintenance Network Model for Urban Rail Transit Systems Based on the Variable Coverage Radius: Evidence from Changchun City in China
X. Mao, X. Mao, W. Cui, B. Sun, Y. Jiang, L. Zheng

Collection of open articles in transport research (2020). Vol. 2020, 316

Abstract
Network-wide maintenance lacks strong theoretical support and practical cases. However, research on this topic has entered an extensive exploratory stage; for example, new network design methods are being sought, and successful practices from traditional maintenance by line and by profession are being incorporated. This paper proposes a novel set-covering model with the variable coverage radius for the maintenance network of urban rail transit systems in the context of network-wide maintenance. The concepts of network-wide maintenance follow principles that are similar to those of bio-geography-based optimization (BBO), i.e., patterns of migration, variation, and extinction of different populations in different habitats. Therefore, a BBO algorithm is implemented with combinatorial optimization programming. Experiments from Changchun city in China show that the proposed model and algorithm are effective in fulfilling network-wide requirements through a direct tradeoff between the coverage radius and maintenance response time. In addition, the maintenance capacity and variable coverage radius of each maintenance point influence both the maintenance timeliness and resource utilization of maintenance units. Document type: Article
Abstract
Network-wide maintenance lacks strong theoretical support and practical cases. However, research on this topic has entered an extensive exploratory stage; for example, new [...]
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Evaluating pedestrian and cyclist behaviour at a level crossing
C. Kaufmann, D. Schreiber, M. Urban, T. Landgraf, C. Erbsmehl, L. Hartwig, R. Risser

Collection of open articles in transport research (2020). Vol. 2020, 315

Abstract
The article gives an overview about the results of the use of a newly developed measurement technique in order to analyse the behaviour of road users at level crossings. For the test of the measurement technique a before/after design was chosen in order to evaluate the effects of a speed bump on the behaviour of road users and on safety at level crossings. The measurement technique combines the use of (1) an infrared camera and (2) conventional video recording. The use of an infrared camera allows the registration of variables such as speed, position, distance and acceleration independent of lighting conditions. A conventional camera includes recording of road users' head movements on the basis of which one can conclude where their attention is directed. Besides the analysis of changes in motorists’ behaviour due to installation of a speed bump, data on the behaviour of vulnerable road users was also collected. The article concentrates especially on these results. It shows different forms of misconduct amongst the vulnerable road users that stems from different sources of error and could potentially lead to accidents. Crossings against red were observed as well as a negative effect of group size on the frequency of looking to the left and/or right. Generally vulnerable road users were crossing the railway mostly alone followed by crossing in pairs. Bigger groups were also observed but were more an exception like school classes or groups of recreational cyclists. The gaze behaviour of the vulnerable road users to the left and right hardly changed over the time and about two thirds of the pedestrians and cyclists were looking in at least one direction before crossing the railway. During the period without the speed bump eleven pedestrians and five cyclists were crossing the rail road while the red light was activated (none within the period where the speed bump was implemented – which might be due to the speed bump itself but cannot be ascribe to it with certainty). It can be concluded that the presented measurement technique could be suitable to be included in evaluations of the behaviour of vulnerable road users to achieve a more comprehensive format, which includes night time observations, observation of vulnerable road user behaviour and estimation of the effect of a safety measure. Document type: Article
Abstract
The article gives an overview about the results of the use of a newly developed measurement technique in order to analyse the behaviour of road users at level crossings. For [...]
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Assessing Gender Gaps in Educational Provision, Research and Employment Opportunities in the Transport Sector at the European Level
M. Pirra, A. Carboni, M. Diana

Collection of open articles in transport research (2020). Vol. 2020, 314

Abstract
Serious gaps are found when evaluating the recognition and inclusion of gender aspects in transport strategies, research and innovation. Similar issues can be spotted in the transport labor market, where only 22% of workers are women at the European level. The roots of these limitations are in the low participation of women in Science, Technology, Engineering and Maths (STEM) studies and, therefore, in the traditionally male-dominated transport field occupations. Stemming from the European project TInnGO, the current paper proposes a descriptive analysis to evaluate the gender gaps in educational provision and research in ten European countries. Specific indicators, such as percentages in the gender composition or the presence of university courses dealing with mobility and transport, have been defined and their availability in different countries is verified. In addition, a desktop review of practices for encouraging and supporting women in STEM studies is operated, underling characteristics such as the kind of initiative, the methods and tools used, the target group or the type of promoter. The results of this activity show that a wide network of associations and mentoring operates in various European nations, mostly targeting secondary school students, trying to make females aware of their potentialities in a deeply gender-biased field like the STEM one. Document type: Article
Abstract
Serious gaps are found when evaluating the recognition and inclusion of gender aspects in transport strategies, research and innovation. Similar issues can be spotted in the [...]
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A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
S. Dong, J. Zhou

Collection of open articles in transport research (2020). Vol. 2020, 313

Abstract
The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections. Document type: Article
Abstract
The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant [...]
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A Novel Algorithm for Controlling Active and Reactive Power Flows of Electric Vehicles in Buildings and Its Impact on the Distribution Network
C. El-Bayeh, B. Brahmi, K. Alzaareer, M. Zellagui

Collection of open articles in transport research (2020). Vol. 2020, 312

Abstract
In the literature, many optimization algorithms were developed to control electrical loads, especially Electric Vehicles (EVs) in buildings. Despite the success of the existing algorithms in improving the power profile of charging EVs and reducing the total electricity bill of the end-users, these algorithms didn&rsquo t show significant contribution in improving the voltage profile on the network, especially with the existence of highly inductive loads. The control of the active power may not be sufficient to regulate the voltage, even if sophisticated optimization algorithms and control strategies are used. To fill the gap in the literature, we propose a new algorithm that is able to control both the active and reactive power flows using electric vehicles in buildings and homes. The algorithm is composed of two parts the first part uses optimization to control the active power and minimize the electricity bill, while the second part controls the reactive power using the bidirectional converter in the EV in a way that the voltage profile on the distribution transformer respects its limits. The new approach is validated through a comparative study of four different scenarios, (i) without EV, (ii) with EV using uncoordinated charging, (iii) with EV using coordinated charging, (iv) with EV using our proposed algorithm. Results show that our algorithm has maintained the voltage within the recommended limits, and it has minimized the peak load, the electricity cost, and the techno-economic losses on the network. Document type: Article
Abstract
In the literature, many optimization algorithms were developed to control electrical loads, especially Electric Vehicles (EVs) in buildings. Despite the success of the existing [...]
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Design of Real-Time Dynamic Reversible Lane in Intelligent Cooperative Vehicle Infrastructure System
L. Mao, J. Dai, P. Hu, W. Li, I. Zhang, G. Zhou

Collection of open articles in transport research (2020). Vol. 2020, 311

Abstract
The rapidly growing traffic demand and the slowly increasing traffic supply have produced an mounting contradiction, which is mainly manifested in cities as road congestion and unbalanced bidirectional traffic flow. Most of the reversible lanes are implemented on fixed sections and fixed times and are mainly guided by ground markings, road signs, railings, and traffic police officer. It requires a lot of human and material costs. And, the control effect is lagging and inaccurate. Aiming at these problems, a real-time dynamic reversible lane scheme in the Intelligent Cooperative Vehicle Infrastructure System (CVIS) was proposed. Traffic information was collected in real time through the CVIS, and a reversible lane scheme was established based on the real-time service level "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mi"V"/mml:mi""mml:mo"/"/mml:mo""mml:mi"C"/mml:mi""/mml:math" and BRP functions. A lane change control model was applied to determine the number of lanes and the timing of lane changes. Then, the reversible lanes were managed in real time through intelligent road stud lights and light curtain walls. Buffer sections and no-entry sections were set to ensure reversible lanes operating safely and efficiently. VISSIM simulation was used for case analysis, and the results showed that compared with the traditional time-controlled reversible lane scheme, the real-time dynamic reversible lane scheme could reduce the average vehicle delay by 27.4% and decrease the vehicle VOC, CO and NO"jats:sub"X"/jats:sub" emissions by 13.5%. Document type: Article
Abstract
The rapidly growing traffic demand and the slowly increasing traffic supply have produced an mounting contradiction, which is mainly manifested in cities as road congestion [...]
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The Shape of a Vehicle Windshield Affects Reaction Time and Brain Activity During a Target Detection Task
J. Pernia, J. Lu, T. Yoshida, S. Content, T. Sasaoka, M. Chiba, Y. Okamoto, M. Machizawa, I. Koji, T. Nouzawa, N. Michida, K. Atsuhide, K. Nishikawa, S. Yamawaki

Collection of open articles in transport research (2020). Vol. 2020, 310

Abstract
Background: Achieving clear visibility through a windshield is one of the crucial factors in manufacturing a safe and comfortable vehicle. The optic flow (OF) through the windshield has been reported to divert attention and could impair visibility. Although a growing number of behavioral and neuroimaging studies have assessed drivers' attention in various driving scenarios, there is still little evidence of a relationship between OF, windshield shape, and driver's attentional efficacy. The purpose of this research was to examine this relationship. Methods: First, we quantified the OF across the windshield in a simulated driving scenario with either of two types of the windshield (a tilted or vertical pillar) at different speeds (60 km/h or 160 km/h) and found more upward OF along the tilted pillar than along the vertical pillar. Therefore, we hypothesized that the predominance of upward OF around the windshield along a tilted pillar could distract a driver and that we could observe the corresponding neural activity. Magnetic resonance scans were then obtained while the subjects performed a visual detection task while watching the driving scene used in the OF analysis. The subjects were required to press a button as rapidly as possible when a target appeared at one of five positions (leftmost, left, center, right, and rightmost). Results: We found that the reaction time (RT) on exposure to a tilted pillar was longer than that on exposure to a vertical pillar in the leftmost and rightmost conditions. Furthermore, there was more brain activity in the precuneus when the pillar was tilted than when it was vertical in the rightmost condition near the pillar. In a separate analysis, activation in the precuneus was found to reflect relative changes in the amount of upward OF when the target was at the rightmost position. Conclusions: Overall, these observations suggest that activation in the precuneus may reflect extraneous cognitive load driven by upward OF along the pillar and could distract visual attention. The findings of this study highlight the value of a cognitive neuroscientific approach to research and development in the motor vehicle manufacturing industry. Document type: Article
Abstract
Background: Achieving clear visibility through a windshield is one of the crucial factors in manufacturing a safe and comfortable vehicle. The optic flow (OF) through the [...]
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How Much Space Is Required? Effect of Distance, Content, and Color on External Human–Machine Interface Size
K. Bengler, J. Schulze, M. Rettenmaier

Collection of open articles in transport research (2020). Vol. 2020, 309

Abstract
The communication of an automated vehicle (AV) with human road users can be realized by means of an external human–machine interface (eHMI), such as displays mounted on the AV’s surface. For this purpose, the amount of time needed for a human interaction partner to perceive the AV’s message and to act accordingly has to be taken into account. Any message displayed by an AV must satisfy minimum size requirements based on the dynamics of the road traffic and the time required by the human. This paper examines the size requirements of displayed text or symbols for ensuring the legibility of a message. Based on the limitations of available package space in current vehicle models and the ergonomic requirements of the interface design, an eHMI prototype was developed. A study involving 30 participants varied the content type (text and symbols) and content color (white, red, green) in a repeated measures design. We investigated the influence of content type on content size to ensure legibility from a constant distance. We also analyzed the influence of content type and content color on the human detection range. The results show that, at a fixed distance, text has to be larger than symbols in order to maintain legibility. Moreover, symbols can be discerned from a greater distance than text. Color had no content overlapping effect on the human detection range. In order to ensure the maximum possible detection range among human road users, an AV should display symbols rather than text. Additionally, the symbols could be color-coded for better message comprehension without affecting the human detection range. Document type: Article
Abstract
The communication of an automated vehicle (AV) with human road users can be realized by means of an external human–machine interface (eHMI), such as displays mounted on [...]
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Assessing the Potential of Short Sea Shipping and the Benefits in Terms of External Costs: Application to the Mediterranean Basin
A. Comi, A. Polimeni

Collection of open articles in transport research (2020). Vol. 2020, 308

Abstract
The paper aims to investigate short sea shipping services as a competitive, sustainable freight transport system which is able to respond to economic, social and environmental needs. An assessment methodology is proposed which considers an aggregate discrete choice model, simulating the split between the competitive transport alternatives in the Mediterranean basin. The proposed methodology was used to assess the potential of short sea shipping (SSS) and the net benefits deriving from lower external costs in the north-western Mediterranean basin. Two future scenarios are considered: introduction of new SSS services as envisaged by current EU projects and plans, and the introduction of new SSS routes and an increase in frequencies of existing services. Significant results were obtained in terms of shifting freight traffic from the road network as well as external benefits. Document type: Article
Abstract
The paper aims to investigate short sea shipping services as a competitive, sustainable freight transport system which is able to respond to economic, social and environmental [...]
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Computationally Efficient Energy Management in Hybrid Electric Vehicles Based on Approximate Pontryagin’s Minimum Principle
S. Coskun, Y. Cui, H. Pang, F. Zhang, L. Wang

Collection of open articles in transport research (2020). Vol. 2020, 307

Abstract
This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology utilizes a piecewise linear approximation of the engine fuel rate and state of charge (SOC) derivative by considering drivability and fuel economy simultaneously. Moreover, battery aging is explicitly studied by introducing a control-oriented model, which aims to investigate the effect of battery aging on the optimization performance in the development of the HEVs. An approximate energy management strategy with piecewise linear models is then formulated by the A-PMP, which targets a better performance for the Hamiltonian optimization. The gearshift map is extracted from the optimal results in the standard PMP to hinder frequent gearshift by considering both drivability and fuel economy. Utilizing an approximated Hamilton function, the torque distribution, gearshift command, and the battery aging degradation are jointly optimized under a unified framework. Simulations are performed for dynamic programming (DP), PMP, and A-PMP to validate the effectiveness of the proposed approach. The results indicate that the proposed methodology achieves a close fuel economy compared with the DP-based optimal solution. Moreover, it improves the computation efficiency by 50% and energy saving by 3.5%, compared with the PMP, while ensuring good drivability and fuel efficiency. Document type: Article
Abstract
This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes [...]
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Depicting corrosion-born defects in pipelines with combined neutron/γ ray backscatter: a biomimetic approach
M. Joyce, H. Parker, F. Cave, M. Aspinall, M. Bandala, G. Taylor, S. Conway, D. Gerta, N. Roberts, M. Licata

Collection of open articles in transport research (2020). Vol. 2020, 306

Abstract
The identification of corrosion, cracks and defects in pipelines used for transporting oil and gas can reduce the possibility of leaks, and consequently, it can limit the extent of an environmental disaster, public hazard and the associated financial impact of such events. Typically, corrosion in oil pipelines is measured with non-destructive ultrasonic or electromagnetic techniques, on the basis that corrosion and defects are often manifest as a change of thickness in the steel from which pipelines are made. However, such approaches are not practical for underground pipelines and their deployment can be complicated for the case of pipelines covered by insulation. In this paper, we present an innovative, non-destructive testing technique, which exploits the backscatter of a combination of fast-neutron and γ radiation from steel samples of a variety of thicknesses consistent with changes that might arise due to corrosion of a pipe wall. Our research demonstrates the potential to measure and characterise different steel thicknesses by detecting both the elastic, fast-neutron backscatter and the Compton-scattered γ radiations, simultaneously. Further, we demonstrate that the presence of insulation yields a consistent and separable influence on the experimental, wall-thickness measurements. The data from experimental measurements are supported by a comprehensive Monte Carlo computer simulation study. Document type: Article
Abstract
The identification of corrosion, cracks and defects in pipelines used for transporting oil and gas can reduce the possibility of leaks, and consequently, it can limit the [...]
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An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle
W. Yang, J. Liu, K. Zhou, Z. Zhang, X. Qu

Collection of open articles in transport research (2020). Vol. 2020, 305

Abstract
Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers. Document type: Article
Abstract
Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential [...]
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Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques
Y. Yang, J. Liu, P. Shang, X. Xu, C. Chen

Collection of open articles in transport research (2020). Vol. 2020, 304

Abstract
At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling. Document type: Article
Abstract
At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the [...]
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Application of Least Squares with Conditional Equations Method for Railway Track Inventory Using GNSS Observations
J. Skibicki, R. Licow, K. Czaplewski, W. Koc, Z. Wisniewski, K. Karwowski, A. Wilk, M. Specht, B. Czaplewski, P. Dabrowski, C. Specht, J. Szmagliński, P. Chrostowski, S. Judek, S. Grulkowski

Collection of open articles in transport research (2020). Vol. 2020, 303

Abstract
Satellite geodetic networks are commonly used in surveying tasks, but they can also be used in mobile surveys. Mobile satellite surveys can be used for trackage inventory, diagnostics and design. The combination of modern technological solutions with the adaptation of research methods known in other fields of science offers an opportunity to acquire highly accurate solutions for railway track inventory. This article presents the effects of work carried out using a mobile surveying platform on which Global Navigation Satellite System (GNSS) receivers were mounted. The satellite observations (surveys) obtained were aligned using one of the methods known from classical land surveying. The records obtained during the surveying campaign on a 246th km railway track section were subjected to alignment. This article provides a description of the surveying campaign necessary to obtain measurement data and a theoretical description of the method employed to align observation results as well as their visualisation. Document type: Article
Abstract
Satellite geodetic networks are commonly used in surveying tasks, but they can also be used in mobile surveys. Mobile satellite surveys can be used for trackage inventory, [...]
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Coordinated Headway-Based Control Method to Improve Public Transit Reliability considering Control Points Layout
S. Liang, J. Zhao, T. Zhao, H. Zhang, S. He

Collection of open articles in transport research (2020). Vol. 2020, 302

Abstract
The headway-based control method is usually used to regulate the bus headways and improve reliability of public transit. In general, the holding control strategy is applied at the control point, because enough space for dwell longer at the control point is required, while the stop-skipping control strategy can be used at any bus stop. However, in the headway-based control method, too much stop-skipping will bring longer waiting time and make the passengers impatient. The number and distribution of control points for stop-skipping are not considered in previous self-equalizing bus headway control works. Therefore, in this paper, the control points selection rules for stop-skipping involving their number and distribution on the bus route are discussed. A second by second discrete system is formulated to describe the bus operation. In the proposed control method, the threshold value for activating stop-skipping strategy is raised, avoiding provoking much additional waiting time because of boarding rejected. In the numerical analysis, a set of cases are conducted to evaluate the performance of control method under different number and distribution of control points for stop-skipping. The numerical results show that distribution of control points for stop-skipping has a greater influence on the public transit than the number. Document type: Article
Abstract
The headway-based control method is usually used to regulate the bus headways and improve reliability of public transit. In general, the holding control strategy is applied [...]
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Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach
M. Benalla, H. Hrimech, B. Achchab

Collection of open articles in transport research (2020). Vol. 2020, 301

Abstract
Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver assistance systems (ADAS), ITS are strongly concerned with data fusion techniques of all kinds of sensors deployed over the traffic network. Driver assistance is devoid of a comprehensive evidential reasoning system on contextual information, more specifically when a combination involves inside and outside sensory information of the driving environment. In this paper, we propose a novel agent-based evidential reasoning system using contextual information. Based on a series of information handling techniques, specifically, the belief functions theory and heuristic inference operations to achieve a consensus about daily driving activity in automatically inferring. That is quite different from other existing proposals, as it deals jointly with the driving behavior and the driving environment conditions. A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research. Document type: Article
Abstract
Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver [...]
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Consequences of Transport Low-Carbon Transitions and the Carbon, Land and Water Footprints of Different Fuel Options in The Netherlands
W. Gerbens-Leenes, K. Holtz

Collection of open articles in transport research (2020). Vol. 2020, 300

Abstract
Transport greenhouse gas emissions are mainly caused by the use of fossil fuels, e.g., gasoline and diesel. This case study for The Netherlands calculates how alternative fuels, e.g., electricity, hydrogen or biofuels, contribute to policy aims to decarbonize transport. Alternative fuels, produced in various ways, have different carbon (CF), land (LFs) and water footprints (WFs). This study assesses CFs, LFs and WFs for fuels (kgCO"sub"2"/sub"e/m"sup"2"/sup"/m"sup"3"/sup" per GJ), showing differences among fuels dependent on primary energy sources. It calculates CFs, LFs and WFs for four scenarios with different fuels. The biofuel scenario is not attractive. CFs slightly decrease, while LFs and WFs increase enormously. The electricity scenario has small CFs and the smallest LFs and WFs, but this is only when using wind or solar energy. If storage is needed and hydrogen is produced using wind energy, CFs double from 3055 to 7074 kg CO"sub"2"/sub"e, LFs increase from 15 × 10"sup"6"/sup" to 43 × 10"sup"6"/sup" m"sup"2"/sup" and WFs from 3 × 10"sup"6"/sup" to 37 × 10"sup"6"/sup" m"sup"3"/sup" compared to the electricity scenario. The case study shows that wise fuel choices contribute to policy aims to decarbonize transport, although LFs and WFs are also important to consider. These case study results are relevant for sustainable transportation transitions worldwide. Document type: Article
Abstract
Transport greenhouse gas emissions are mainly caused by the use of fossil fuels, e.g., gasoline and diesel. This case study for The Netherlands calculates how alternative [...]
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Problems of Sustainable Transport of Large-Sized Roundwood
M. Wieruszewski, K. Mydlarz

Collection of open articles in transport research (2020). Vol. 2020, 299

Abstract
When considering the economic and environmental aspects of forestry, especially the issues related to timber harvesting, emphasis should be placed on the importance of the availability of raw material resources for the sustainable flow of goods. It would also be difficult to disregard certain issues related to transport, which play a key role in the efficient flow of wooden raw materials. It has to be noticed that timber transport options are limited by a number of factors, including the considerable fragmentation of wood resources and the lack of adequately developed railway transport facilities. This paper focuses on issues related to the road transport of timber carried out by transport companies. Observations to date of large-sized roundwood (thicker than 14 cm and longer than 3 m) transport in Poland indicate a relatively frequent occurrence of overloaded vehicles, exceeding the permissible total weight limit. Empirical evidence also suggests that in many cases, it is an effect of improperly endorsed standards with regard to the density of the transported material. Moreover, there is a clear correlation between the loading volume and economic as well as environmental factors. Therefore, the aim of this article was to show both the current situation in the transport of bulky timber and to present the possibilities for its optimization, from the point of view of locational, economic and environmental factors. Document type: Article
Abstract
When considering the economic and environmental aspects of forestry, especially the issues related to timber harvesting, emphasis should be placed on the importance of the [...]
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Intelligent Video Surveillance Technology in Intelligent Transportation
F. He

Collection of open articles in transport research (2020). Vol. 2020, 298

Abstract
Along with the strength of the country’s overall strength, the people’s pockets have become more and more popular, and there have been significant improvements in all aspects of life, especially in terms of travel methods. This reflects the increase in residents’ income, but it also brings huge traffic pressure. In the long run, traffic congestion is not only detrimental to urban development, but frequent traffic accidents threaten residents’ travel safety. Effective monitoring methods are essential to solving these problems, so it is necessary to carry out research on intelligent video monitoring technology in intelligent transportation. The purpose of this article is to solve the current situation of excessive traffic pressure in the city. Through the study of intelligent video surveillance technology in intelligent traffic, the use of constrained least squares algorithm to remove motion blur and apply Kalan filtering to the sharpening process is used to eliminate noise ambiguity and make a brief introduction to various classic moving target detection methods to realize real-time monitoring of intelligent traffic conditions and continuously adjust and verify the monitoring situation, and then establish intelligent video in intelligent traffic monitoring technology research system. The research results show that this kind of intelligent video surveillance technology research in intelligent transportation can effectively increase the awareness of intelligent video surveillance technology and improve the level of intelligent video surveillance technology. The data measurement time has been shortened by one hour, the aggregation time has been changed from three hours to two hours, and the analysis time has been shortened by half. Eased urban traffic road pressure and greatly reduced the incidence of traffic accidents, which is conducive to socialist harmony social construction. Document type: Article
Abstract
Along with the strength of the country’s overall strength, the people’s pockets have become more and more popular, and there have been significant improvements in all [...]
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Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition
Y. Han, Z. He

Collection of open articles in transport research (2020). Vol. 2020, 297

Abstract
A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data. Document type: Article
Abstract
A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide [...]
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Advancing Applied Research in High Volume Transport in Low-Income Countries in Africa and South Asia
B. Obika, B. Thompson

Collection of open articles in transport research (2020). Vol. 2020, 296

Abstract
The Department for International Development (DFID) is funding the High Volume Transport (HVT) Applied Research Programme. This programme is an integral component of the UK response to delivering transport and mobility that is accessible, efficient, safe, and green in the low-income countries (LICs) in Africa and South Asia. The first part of the HVT programme produced an up-to-date and comprehensive state of knowledge on high volume transport in these countries. This Special Issue presents a selection of papers to cover key research priorities identified in road and rail transport, low carbon transport, and gender and inclusive transport. The state of knowledge has produced a sound basis for setting priorities for applied research in the second part of the programme. Applied research is directed to delivering high volume transport that contributes to economic growth and social development, and that is more resilient to the impact of climate change in LICs in Africa and South Asia. Document type: Article
Abstract
The Department for International Development (DFID) is funding the High Volume Transport (HVT) Applied Research Programme. This programme is an integral component of the UK [...]
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Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation
L. Chen, X. Huang, H. Zhang

Collection of open articles in transport research (2020). Vol. 2020, 295

Abstract
The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) is proposed in accordance with the actual data. First and foremost, a data transformation model is established by considering the boundary bias of the symmetric KDE in order to carry out normal transformation on distribution to be estimated from all kinds of dimensions to the utmost extent. Then, a ternary symmetric KDE model and an optimum bandwidth model are established to estimate the transformed data. Moreover, an estimation evaluation model is also built to transform simulated data that are generated on a certain scale with the Monte Carlo method by means of inverse transformation, so that the fitting level of the ternary symmetric KDE model can be estimated. According to simulation results, a higher fitting level can be achieved by the ternary symmetric KDE method proposed in this paper, in comparison to the joint estimation method based on the edge KDE and the ternary t-Copula function. Moreover, data transformation can effectively eliminate the boundary effect of symmetric KDE. Document type: Article
Abstract
The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging [...]
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Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments
S. Wang, P. Zhao, B. Yu, H. Liang, W. Huang

Collection of open articles in transport research (2020). Vol. 2020, 294

Abstract
An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene. Document type: Article
Abstract
An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. [...]
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Comparing of Data Collection for Network Level Pavement Management of Urban Roads and Highways
L. Yue, Z. Xu, H. Wang

Collection of open articles in transport research (2020). Vol. 2020, 292

Abstract
Pavement condition data are collected by agencies to support pavement management system (PMS) for decision-making purpose as well as to construct performance model. The cost of pavement data collection increases with the increase of survey frequencies. However, a lower monitoring frequency could lead to unreliable maintenance decisions. It is necessary to understand the influence of monitoring frequencies on maintenance decision by considering the reliability of performance prediction models. Because of different maintenance conditions of urban roads and highways, their performance show different trends. In this paper, the influence of pavement monitoring frequency on the pavement performance models was investigated. The results indicate that low collection frequencies may result in delay in maintenance action by overestimating pavement performance. The collection frequency for Pavement Condition Index (PCI) can be reduced without compromising the accuracy of performance model, more work should be done to ensure the PCI data quality, thus to guarantee the rationality of maintenance decisions. Effect of frequency reduction on pavement performance (IRI) models of urban roads seems greater than on pavement performance (IRI) models of highways, which may lead to heavier monitoring work for urban roads management. This paper provided an example which demonstrated how a comparative analysis can be performed to determine whether the current data collection plan can provide sufficient data for time series analysis. Document type: Article
Abstract
Pavement condition data are collected by agencies to support pavement management system (PMS) for decision-making purpose as well as to construct performance model. The cost [...]
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Analyzing Household Vehicle Ownership in the Japanese Local City: Case Study in Toyota City
Y. Nishihori, H. Kato, R. Ando, J. Yang

Collection of open articles in transport research (2020). Vol. 2020, 291

Abstract
This study aims to understand the crucial factors affecting vehicle ownership in the local city, Japan. 14,855 household sample data in Toyota City are used as the research sample. The sample data are extracted from the 5th Person Trip Survey data in the Chukyo region. First, the unknown annual income is complemented by using an ordered probit model. Then, a trivariate ordered probit model is utilized to analyze ownership of light motor vehicles, ordinary motor vehicles, and small trucks simultaneously. To estimate unknown parameters effectively and efficiently, one type of Markov Chain Monte Carlo methods called the Gibbs Sampler algorithm is applied in this study. The significant findings suggest the following: (1) the annual income only affects the ownership of ordinary motor vehicles; (2) a household with a 60-year-old or older householder is more likely to own small trucks, compared to that with a householder below the age of 60; (3) the population density negatively affects the number of light motor vehicles and that of small trucks; (4) there is a substitution effect of vehicle ownership between light motor vehicles and small trucks. Document type: Article
Abstract
This study aims to understand the crucial factors affecting vehicle ownership in the local city, Japan. 14,855 household sample data in Toyota City are used as the research [...]
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Impact of Urban Undersea Tunnel Longitudinal Slope on the Visual Characteristics of Drivers
S. Wang, Z. Du, F. Jiao, L. Yang, Y. Ni

Collection of open articles in transport research (2020). Vol. 2020, 290

Abstract
This study aims to investigate the impact of the urban undersea tunnel longitudinal slope on the visual characteristics of drivers. 20 drivers were enrolled to conduct the real vehicle test of the urban undersea tunnel. First, the data of average fixation time and visual lobe were collected by an eye tracker. The differential significance was tested using the one-way repeated measures analysis of variance (ANOVA). Then, the difference between the up-and-down slope (direction) factor and the longitudinal slope (percent) factor on the two indexes were analyzed using the two-way repeated measures ANOVA. Second, by constructing a Lorentz model, the impact of the longitudinal slope on the average fixation time and the visual lobe were analyzed. Besides, a three-dimensional model of the longitudinal slope, average fixation time, and visual lobe was quantified. The results showed that the average fixation time and visual lobe under different longitudinal slopes markedly differed when driving on the uphill and downhill sections. The average fixation time and visual lobe under two factors were markedly different. Moreover, with an increase in the longitudinal slope, the average fixation time exhibited a trend of increasing first then decreasing; the visual lobe exhibited a trend of decreasing first and then increasing. The average fixation time reached the minimum and maximum value when the slope was 2.15% and 4.0%, whereas the visual lobe reached the maximum and minimum value when the slope was 2.88% and 4.0%. Overall, the longitudinal slope exerted a great impact on the visual load of the driver. Document type: Article
Abstract
This study aims to investigate the impact of the urban undersea tunnel longitudinal slope on the visual characteristics of drivers. 20 drivers were enrolled to conduct the [...]
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Comparing Spatial Accessibility and Travel Time Prediction to Commercial Centres by Private and Public Transport: A Case Study of Oforikrom District
W. Osei-wusu, J. Quaye-Ballard, N. Quaye-Ballard, A. Arko-Adjei, T. Antwi

Collection of open articles in transport research (2020). Vol. 2020, 288

Abstract
The relevance of accessibility in shaping transport planning has often been neglected, hampering on decisions to improve transport efficiency. This is increasingly becoming problematic, as they often impede on economic and technological developments. Many studies on accessibility assert that it is easier for public transport to reach an activity centre than it is for private transport. For this reason, the research compares travel time forecast and accessibility levels with private and public transports en route to commercial centres. The research involves a 21-day transport survey for private cars and public shuttles in Oforikrom district using Global Positioning System (GPS) probe to record the traffic performance indicators to be analyzed in a GIS environment. The results of the study display on a map the level of accessibility via the modes, and a comparative line plot of travel time with private and public transport. The study reveals that private cars in the district generally perform better than public shuttles on the level of accessibility, and travel time. The execution of the research shows that the convergence of choice of transport mode and travel time dynamics is crucial for policymakers to implement diverse transport modes and commuters to choose a mode that has low accessibility cost. Document type: Article
Abstract
The relevance of accessibility in shaping transport planning has often been neglected, hampering on decisions to improve transport efficiency. This is increasingly becoming [...]
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Chats About Daily Traffic: Agentic Adaptability of Jabodetabek Commuters to Minimize Stress and Transcend Everyday Lives
E. Poerwandari

Collection of open articles in transport research (2020). Vol. 2020, 287

Abstract
With the increasing number of people invading Greater Jakarta and the ongoing improvement of infrastructure in Greater Jakarta or Jabodetabek, one of the challenges faced by the community is traffic congestion. Previous studies revealed the negative impact of traffic congestion on community welfare and mental health. Thus, the present study aims to shed light on how Jabodetabek inhabitants and commuters experience, make sense, and adapt to daily traffic congestion. Data were obtained from discussions in six WhatsApp groups, which included a total of 160 respondents. The study supports the findings of previous studies, that is, traffic congestion leads to increased costs and losses and causes distress. However, the subjective perception of time management enables commuters to adapt to the situation. Nevertheless, the paper presents a novel finding: agentic adaptability of Jabodetabek commuters, which is evidenced by the ability to discover various means of adjusting to traffic congestion. Although effective policies are required to mitigate congestion, the good news is that many opportunities for adjustment await the community in terms of being stuck in traffic. Document type: Article
Abstract
With the increasing number of people invading Greater Jakarta and the ongoing improvement of infrastructure in Greater Jakarta or Jabodetabek, one of the challenges faced [...]
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Evidence from Nanjing, China
T. Feng, Z. Shi, Y. Liu, Y. Ji

Collection of open articles in transport research (2020). Vol. 2020, 286

Abstract
Promoting a transition in individuals&rsquo travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro&ndash bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots (2) the distribution of cycling times between origins/destinations and metro stations and (3) the times when metro&ndash bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro&ndash bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers&rsquo use frequency of integrated metro&ndash bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems. Document type: Article
Abstract
Promoting a transition in individuals&rsquo travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that [...]
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Are Existing Battery Electric Vehicles Adoption Studies Able to Inform Policy? A Review for Policymakers
D. Potoglou, R. Song

Collection of open articles in transport research (2020). Vol. 2020, 285

Abstract
Accelerating the adoption of electric vehicles provides a rare historic opportunity for reducing the dependence on fossil fuel and decarbonising road networks in the field of transport. Many countries have introduced various policy packages on both national and local levels to encourage electric vehicle adoption, but their market shares remain low. For better understanding the reasons behind this evidence, exploring the determinants that influence consumers’ adoption intentions is significant. Previous literature reviews have made clear and elaborated syntheses of influential factors; however, a summary of how evidence can be translated into policy through these factors is lacking. In response, this paper synthesises the main policies of various countries, summarises the previous research results, and forms corresponding policy tools, which can provide a reference to policymakers and guide the policy-making process. Document type: Article
Abstract
Accelerating the adoption of electric vehicles provides a rare historic opportunity for reducing the dependence on fossil fuel and decarbonising road networks in the field [...]
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Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment
Q. Lv, M. Xu, G. Xu, Y. Wang, Y. Liu

Collection of open articles in transport research (2020). Vol. 2020, 284

Abstract
Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation. Document type: Article
Abstract
Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate [...]
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Li-Ion Batteries: A Review of a Key Technology for Transport Decarbonization
M. Noussan, P. Raimondi, D. Stampatori

Collection of open articles in transport research (2020). Vol. 2020, 283

Abstract
Lithium ion batteries are experiencing an increased success thanks to their interesting performances, in particular for electric vehicles applications. Their continuous technological improvements in the last years are providing higher energy density and lower manufacturing costs. However, the environmental performance of their supply chain is of paramount importance to guarantee a cleaner alternative to fossil-based solutions on the entire life cycle of the applications. This paper carries out a comprehensive review on the main aspects related to Li-ion batteries manufacturing, to support the readers in understanding the complexity of the subject and the main challenges and opportunities for the future developments of this technology. The paper discusses the expected future demand of batteries; the main aspects related to the supply chain, including existing assets, input materials and alternative technologies; the end-of-life of batteries; the environmental impacts; and the main geopolitical implications. Document type: Article
Abstract
Lithium ion batteries are experiencing an increased success thanks to their interesting performances, in particular for electric vehicles applications. Their continuous technological [...]
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Assessing the Impacts of Autonomous Bus-on-Demand Based on Agent-Based Simulation: A Case Study of Fuyang, Zhejiang, China
Z. Zhai, Y. Yang, Y. Shen, Y. Ji, Y. Du

Collection of open articles in transport research (2020). Vol. 2020, 282

Abstract
This paper envisions and assesses the performance of an autonomous bus-on-demand (ABoD) system. We take Fuyang, Zhejiang, China, as the study area to investigate the spatiotemporal distribution of bus travel demand during workdays, and we propose replacing inefficient bus routes with the ABoD system. Agent-based models with various bus dispatching and operation control strategies are constructed to evaluate the performance of the ABoD system. The behaviors and interactions of the agents, passengers, autonomous buses, and a control center are designed. After the verification of the simulated bus travel demand with real-world demand, a series of scenarios with various ABoD operation strategies are simulated. The simulation results show that, in comparison with both current fixed-schedule bus services and the optimized bus dispatching strategies, the ABoD system occupies fewer road resources and utilizes bus vehicles more efficiently. Besides, the system is adaptive to the sudden surge in bus travel demand and is economically sustainable. Document type: Article
Abstract
This paper envisions and assesses the performance of an autonomous bus-on-demand (ABoD) system. We take Fuyang, Zhejiang, China, as the study area to investigate the spatiotemporal [...]
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Exploring everyday mobility in a living lab based on economic interventions
T. Ringenson, A. Kramers, M. Sjöman

Collection of open articles in transport research (2020). Vol. 2020, 281

Abstract
New mobility solutions, such as Mobility as a Service, have been suggested to have the potential to reduce car ownership and be part of a transition towards a more sustainable transportation system. However, research suggests that governance measures such as taxation and policies will be needed to ensure sustainability aspects. This paper explores everyday mobility by use of interventions in people’s everyday lives. The focus is on identifying underlying factors that may motivate or hinder changes that are positive from a sustainability perspective. This is then put in the perspective of new mobility services and policy making. Our findings support the view that privately owned cars are hard to replace with new mobility services that contribute to sustainability and are not based on individual cars. Economic interventions for increased sustainability will likely have limited effects, since the alternatives do not offer what car owners value most. Also, limited understanding of the car’s full costs may make the new services appear comparatively more expensive. Furthermore, urban planning to reduce the need for travel, and the capacity of the physical public transport infrastructure will continue to be important. Long vacation trips and “medium sized flows” are identified as opportunities for further research and for new solutions to support sustainable mobility transitions. Document type: Article
Abstract
New mobility solutions, such as Mobility as a Service, have been suggested to have the potential to reduce car ownership and be part of a transition towards a more sustainable [...]
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Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China
Q. Zhou, C. Wong, X. Su

Collection of open articles in transport research (2020). Vol. 2020, 280

Abstract
Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity management using OFO bike operation data in Shenzhen. First, two clustering algorithms were used to identify the bicycle gathering area, and the available bike number and coefficient of available bike number variation were analyzed in each bicycle gathering area’s type. Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. Finally, the application of the knowledge gained from the existing dockless bicycle operation data to guide the number planning and management of public bicycles was explored. We found the following. (1) There were 492 OFO bicycle gathering areas that can be divided into four types: high inefficient, normal inefficient, high efficient, and normal efficient. The high inefficient and normal inefficient areas gathered about 110,000 bicycles with low usage. (2) More types of bicycle gathering area will affect the accuracy of the classification algorithm. The random forest classification had the best performance in identifying bicycle gathering area types in five classification algorithms with an accuracy of more than 75%. (3) There were obvious differences in the characteristics of 25 impact factors in four types of bicycle gathering areas. It is feasible to use these factors to predict area type to optimize the number of available bicycles, reduce operating costs, and improve utilization efficiency. This work helps operators and government understand the characteristics of dockless PBS and contributes to promoting long-term sustainable development of the system through a machine learning approach. Document type: Article
Abstract
Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity [...]
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Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms
S. Park, J. Choi

Collection of open articles in transport research (2020). Vol. 2020, 279

Abstract
The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms. Document type: Article
Abstract
The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, [...]
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A Tabu Search-Based Algorithm for Airport Gate Assignment: A Case Study in Kunming, China
J. Bi, Z. Wu, L. Wang, D. Xie, X. Zhao

Collection of open articles in transport research (2020). Vol. 2020, 278

Abstract
An airport gate is the core resource of an airport operation, which is an important place for passengers to get on and off the aircraft and for maintaining aircraft. It is the prerequisite for other related dispatch. Effective and reasonable allocation of gates can reduce airport operating costs and increase passenger satisfaction. Therefore, an airport gate assignment problem (AGAP) needs to be urgently solved in the actual operation of the airport. In this paper, considering the actual operation of the airport, we formulate an integer programming model for AGAP by considering multiple constraints. The model aims to maximize the number of passengers on flights parked at the gate. A tabu search-based algorithm is designed to solve the problem. In the process of algorithm design, an effective initial solution is obtained. A unique neighborhood structure and search strategy for tabu search are designed. The algorithm can adapt to the dynamic scheduling of airports. Finally, tests are performed using actual airport data selected from Kunming Changshui International Airport in China. The experimental results indicate that the proposed method can enhance the local search ability and global search ability and get satisfactory results in a limited time. These results provide an effective support for the actual gate assignment in airport operations. Document type: Article
Abstract
An airport gate is the core resource of an airport operation, which is an important place for passengers to get on and off the aircraft and for maintaining aircraft. It is [...]
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Optimization of a physical internet based supply chain using reinforcement learning
G. Bohács, E. Puskás, Á. Budai

Collection of open articles in transport research (2020). Vol. 2020, 276

Abstract
Physical Internet based supply chains create open, global logistics systems that enable new types of collaboration among participants. The open system allows the logistical examination of vehicle technology innovations such as the platooning concept. This article explores the multiple platoon collaboration. For the reconfiguration of two platoons a heuristic and a reinforcement learning (RL) based models have been developed. To our knowledge, this work is the first attempt to apply an RL-based decision model to solve the problem of controlling platoon cooperation. Vehicle exchange between platoons is provided by a virtual hub. Depending on the various input parameters, the efficiency of the model was examined through numerical examples in terms of the target function based on the transportation cost. Models using platoon reconfiguration are also compared to the cases where no vehicle exchange is implemented. We have found that a reinforcement learning based model provides a more efficient solution for high incoming vehicle numbers and low dispatch interval, although for low vehicle numbers heuristics model performs better. Document type: Article
Abstract
Physical Internet based supply chains create open, global logistics systems that enable new types of collaboration among participants. The open system allows the logistical [...]
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A Cost and Passenger Responsible Optimization Method for the Operation Plan of Additional High-Speed Trains in a Peak Period
C. Cao, Y. Liu, Y. Zhou, Z. Feng

Collection of open articles in transport research (2020). Vol. 2020, 275

Abstract
In the peak period of a railway system, operators typically add additional trains to provide increased capacity to satisfy the increasing passenger demand. The paper proposes a new optimization framework for designing the operation plan, which includes the number of additional trains, train type, stop plan, and timetable, for additional trains in a peak period. A space-time network representation is used to obtain a feasible primary operation plan by finding a set of feasible space-time paths in the space-time network. Considering simultaneously the passenger demand and the trains’ total travel times, we formulate a biobjective integer programming model for generating a cost and passenger responsible primary operation plan. A set of loading capacity constraints are formulated in the model to guarantee a suitable loading capacity for each station’s passenger demand and better service for passengers. The CPLEX solver is used to solve the proposed model and to generate the optimal operation plan. Two sets of numerical experiments are conducted on a small-scale rail corridor and on the Wuhan-Guangzhou rail corridor to evaluate the performance of the proposed method. The results of the experiments show that the primary operation plan can be obtained within an acceptable computation time. Document type: Article
Abstract
In the peak period of a railway system, operators typically add additional trains to provide increased capacity to satisfy the increasing passenger demand. The paper proposes [...]
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A Theoretical Research Framework of Future Sustainable Urban Freight Transport for Smart Cities
H. Haasis, Z. He

Collection of open articles in transport research (2020). Vol. 2020, 274

Abstract
This paper aims to construct a theoretical research framework for sustainable urban freight transport (SUFT) from the perspectives of future urban development and distribution innovations, and appropriate research methods are discussed, as well. Urban freight transport plays a critical role in the promotion of sustainable and livable cities. According to the literature review, considerable research on SUFT has focused on resolving some specific problems with a short-term perspective. The existence of an urban freight transport strategy is noted, which should be embedded in an overall sustainable development strategy with a long-term perspective (approximately 20&ndash 30 years). Nevertheless, considerable research has paid scant attention to the long-term planning of SUFT. Given this, this paper contributes to the closure of this gap. First, this paper presents a systematic literature review (SLR) to highlight published papers involving foresight research within the past 16 years (2003&ndash 2018). This step contributes to the understanding of research methods that can be used in foresight research. Subsequently, this paper discusses the impacts of both urban development and distribution innovations on future SUFT, and these effects are used to select the appropriate methods to construct the theoretical research framework. Finally, the theoretical research framework of long-term planning for SUFT is developed on the basis of two future perspectives: the trends of urban development and the application of urban distribution innovations. This framework is intended to provide an approach to designing sustainable urban logistics, taking into account urban development and distribution innovations. Document type: Article
Abstract
This paper aims to construct a theoretical research framework for sustainable urban freight transport (SUFT) from the perspectives of future urban development and distribution [...]
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Efficiency of Telematics Systems in Management of Operational Activities in Road Transport Enterprises
A. Kalinichenko, R. Miler, A. Brzozowska, M. Kisielewski

Collection of open articles in transport research (2020). Vol. 2020, 273

Abstract
Implemented in road transport enterprises (RTEs) on a large scale, telematics systems are dedicated both to the particular aspects of their operation and to the integrated fields of the total operational functioning of such entities. Hence, a research problem can be defined as the identification of their efficiency levels in the context of operational activities undertaken by RTEs (including more holistic effects, e.g., lowering fuel/energy consumption and negative environmental impacts). Current research studies refer to the efficiency of some particular modules, but there have not been any publications focused on describing the efficiency of telematics systems in a more integrated (holistic) way, due to the lack of a universal tool that could be applied to provide this type of measurement. In this paper, an attempt at filling the identified cognitive gap is presented through empirical research analysing the original matrix developed by the authors that refers to the efficiency rates of organisational activities undertaken by RTEs. The purpose of this paper is to present a tool that has been designed to provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The results are presented in a form of an individual (ontogenetic) matrix of the analysed companies, for which a determinant was calculated with the use of Sarrus’ rule. Obtained in such a way, the set of values identified for the determinants of the subsequent ontogenetic matrices came as an arithmetic progression that characterised the scope and the level of the influence exerted by the implemented IT (information technology) systems on the organisational efficiency of operational activities undertaken by the analysed RTEs. We present a hypothesis stating that the originally developed matrix can be viewed as a reliable tool used for comparative analysis in the field of efficiency of telematics systems in RTEs, and this hypothesis was positively verified during the research. The obtained results prove the significant potential for the wide application of the discussed matrix, which can be used as a universal tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by RTEs. Document type: Article
Abstract
Implemented in road transport enterprises (RTEs) on a large scale, telematics systems are dedicated both to the particular aspects of their operation and to the integrated [...]
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Evaluating Active Traffic Management (ATM) Strategies under Non-Recurring Congestion: Simulation-Based with Benefit Cost Analysis Case Study
F. Outay, M. El-Hansali, S. Farrag, A. Yasar

Collection of open articles in transport research (2020). Vol. 2020, 272

Abstract
Dynamic hard shoulder running and ramp closure are two active traffic management (ATM) strategies that are used to alleviate highway traffic congestion. This study aims to evaluate the effects of these two strategies on congested freeways under non-recurring congestion. The study’s efforts can be considered in two parts. First, we performed a detailed microsimulation analysis to quantify the potential benefits of these two ATM strategies in terms of safety, traffic operation, and environmental impact. Second, we evaluated the implementation feasibility of these two strategies. The simulation results indicated that the implementation of the hard shoulder showed a 50%–57% reduction in delay, a 41%–44% reduction in fuel consumption and emissions, and a 15%–18% increase in bottleneck throughput. By contrast, the implementation of ramp closure showed a 20%–34% decrease in travel time, a 6%–9% increase in bottleneck throughput, and an 18%–32% reduction in fuel consumption and emissions. Eventually, both strategies were found to be economically feasible. Document type: Article
Abstract
Dynamic hard shoulder running and ramp closure are two active traffic management (ATM) strategies that are used to alleviate highway traffic congestion. This study aims to [...]
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Vehicle Trajectory Prediction and Collision Warning via Fusion of Multisensors and Wireless Vehicular Communications
D. Choi, S. Lee, M. Baek, D. Jeong

Collection of open articles in transport research (2020). Vol. 2020, 271

Abstract
Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle surroundings and predict collisions, on-board sensors such as radar, lidar, and cameras are often used. However, the driving environment perception based on these sensors can be adversely affected by a number of factors such as weather and solar irradiance. In addition, potential dangers cannot be detected if the target is located outside the limited field-of-view of the sensors, or if the line of sight to the target is occluded. In this paper, we propose an approach for designing a vehicle collision warning system based on fusion of multisensors and wireless vehicular communications. A high-level fusion of radar, lidar, camera, and wireless vehicular communication data was performed to predict the trajectories of remote targets and generate an appropriate warning to the driver prior to a possible collision. We implemented and evaluated the proposed vehicle collision system in virtual driving environments, which consisted of a vehicle&ndash vehicle collision scenario and a vehicle&ndash pedestrian collision scenario. Document type: Article
Abstract
Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce [...]
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Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania
D. Toma-Danila, A. Tiganescu, I. Armaş

Collection of open articles in transport research (2020). Vol. 2020, 270

Abstract
Abstract. Due to their widespread and continuous expansion, transportation networks are considerably exposed to natural hazards such as earthquakes, floods, landslides or hurricanes. The vulnerability of specific segments and structures among bridges, tunnels, pumps or storage tanks can translate not only into direct losses but also into significant indirect losses at the systemic level. Cascading effects such as post-event traffic congestion, building debris or tsunamis can contribute to an even greater level of risk. To support the effort of modeling the natural hazards' implications at the full transportation network scale, we developed a new applicable framework, relying on (i) GIS to define, analyze and represent transportation networks; (ii) methods for determining the probability of network segments to fail due to natural-hazard effects; (iii) Monte Carlo simulation for multiple scenario generation; (iv) methods to analyze the implications of connectivity loss on emergency intervention times and transit disruption; and (v) correlations with other vulnerability and risk indicators. Currently, the framework is integrated into ArcGIS Desktop as a toolbox entitled “Network-risk”, which makes use of the ModelBuilder functions and is free to download and modify. Network-risk is an attempt to bring together interdisciplinary research with the goal of creating an automated solution to deliver insights on how a transportation network can be affected by natural hazards, directly and indirectly, assisting in risk evaluation and mitigation planning. In this article we present and test Network-risk at the full urban scale for the road network of Bucharest. This city is one of Europe's most exposed capitals to earthquakes, with high seismic-hazard values and a vulnerable building stock but also significant traffic congestion problems not yet accounted for in risk analyses and risk reduction strategies. Document type: Article
Abstract
Abstract. Due to their widespread and continuous expansion, transportation networks are considerably exposed to natural hazards such as earthquakes, floods, landslides or [...]
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Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect
A. Khattak, A. Khattak, X. Zhao, C. Liu, T. Li, T. Li, F. Hui, F. Hui

Collection of open articles in transport research (2020). Vol. 2020, 269

Abstract
The existing model of sudden acceleration changes, referred to as the traffic jerk effect, is mostly based on theoretical hypotheses, and previous research has mainly focused on traditional traffic flow. To this end, this paper investigates the change in the traffic jerk effect between inactive and active vehicle-to-vehicle (V2V) communications based on field experimental data. Data mining results show that the correlation between the jerk effect and the driving behavior increases by 50.6% on average when V2V messages are received. In light of the data analysis results, a new car-following model is proposed to explore the jerk effect in a connected environment. The model parameters are calibrated, and the results show that the standard deviation between the new model simulation data and the observed data decreases by 38.2% compared to that of the full velocity difference (FVD) model. Linear and nonlinear analyses of the calibrated model are then carried out to evaluate the connected traffic flow stability. Finally, the theoretical analysis is verified by simulation experiments. Both the theoretical and simulation results show that the headway amplitude and velocity fluctuations are reduced when considering the jerk effect in a connected environment, and the traffic flow stability is improved. Document type: Article
Abstract
The existing model of sudden acceleration changes, referred to as the traffic jerk effect, is mostly based on theoretical hypotheses, and previous research has mainly focused [...]
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A Proposal for Modeling Indoor–Outdoor Spaces through IndoorGML, Open Location Code and OpenStreetMap
A. Ruiz, J. López, J. Chaparro, D. Alises, M. Romero, R. Navarro, F. Molina

Collection of open articles in transport research (2020). Vol. 2020, 268

Abstract
Traditionally, the standards of spatial modeling are oriented to represent the quantitative information of space. However, in recent years an increasingly common challenge is appearing: flexibly and appropriately integrating quantitative information that goes beyond the purely geometric. This problem has been aggravated due to the success of new paradigms such as the Internet of Things. This adds an additional challenge to the representation of this information due to the need to represent characteristic information of the space from different points of view in a model, such as WiFi coverage, dangerous surroundings, etc. While this problem has already been addressed in indoor spaces with the IndoorGML standard, it remains to be solved in outdoor and indoor&ndash outdoor spaces. We propose to take the advantages proposed in IndoorGML, such as cellular space or multi-layered space model representation, to outdoor spaces in order to create indoor&ndash outdoor models that enable the integration of heterogeneous information that represents different aspects of space. We also propose an approach that gives more flexibility in spatial representation through the integration of standards such as OpenLocationCode for the division of space. Further, we suggest a procedure to enrich the resulting model through the information available in OpenStreetMap. Document type: Article
Abstract
Traditionally, the standards of spatial modeling are oriented to represent the quantitative information of space. However, in recent years an increasingly common challenge [...]
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Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior
T. Zhang, Z. Ziqi, X. Huang, X. Xu, C. Zhong, Q. Xing, H. Wang

Collection of open articles in transport research (2020). Vol. 2020, 267

Abstract
Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the &lsquo Electric Vehicles&ndash Power Grid&ndash Traffic Network&rsquo fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners&rsquo charging behavior. Document type: Article
Abstract
Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real [...]
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Acceptable Automobility through Automated Driving. Insights into the Requirements for Different Mobility Configurations and an Evaluation of Suitable Use Cases
K. Stark, A. Galich

Collection of open articles in transport research (2020). Vol. 2020, 266

Abstract
It is hoped that Automated Driving (AD) will make alternatives to the private car more attractive and facilitate the transition to sustainable transport. However, this expectation may underestimate both the resistance of private automobility and the unintended consequences of automated driving. Whether AD will contribute to sustainable mobility depends largely on its implementation and how its risks are prevented. This paper provides empirical insights into the design of acceptable forms of AD by investigating specific use cases with respect to the requirements of different mobility configurations. We pay special attention to people who travel with children. Our use cases comprise three probable types, covering the spectrum from demand-responsive transport (DRT) to private vehicles. Our results include the identification of mobility configurations and an analysis of AD use cases considering several empirically derived criteria: improved accessibility, ease of daily life and well-being, and improvement of the traffic situation and the transport system. Our analysis is based on a qualitative study in the Berlin area, Germany. The discussion focuses on the usefulness of AD against the background of different user perspectives, sustainability, and societal requirements, as well as an evaluation of AD in terms of its acceptability. We conclude that automated mobility use cases should meet the requirements of different mobility configurations to promote the transformation from private to shared automobility and, eventually, less automobility overall. Document type: Article
Abstract
It is hoped that Automated Driving (AD) will make alternatives to the private car more attractive and facilitate the transition to sustainable transport. However, this expectation [...]
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Assessment of the Economic and Social Impact of Shared Parking in Residential Areas
T. Wang, J. Xie, X. Ye, J. Chen, X. Yan, X. Sui

Collection of open articles in transport research (2020). Vol. 2020, 265

Abstract
Shared parking schemes are not commonly implemented in residential areas due to the uncertainty and conflicts associated with the benefits of such schemes for stakeholders, namely, parking suppliers, parking managers, and the public. To evaluate the economic and social impacts of shared parking in residential areas on its stakeholders, the risk and benefit factors were determined through influential analysis and a questionnaire. A risk–benefit model was established to quantify the risks and benefits for stakeholders. The social return on investment and sensitivity analysis were applied to estimate the economic feasibility of shared parking in residential areas. The methodology combined the use of qualitative, quantitative, and financial information gathered and analyzed to estimate the “value” of shared parking, including its risks, benefits, management pressure, and social benefit. The model was calibrated using the survey data collected from the city of Ningbo in China. The results showed that: (1) The net present value was negative, indicating that the benefits of shared parking were lower than the risks, and thus this scheme would not be economically feasible in residential areas. (2) The cost of purchasing new equipment and rebuilding parking lots had the greatest impact on the benefits of shared parking in residential areas, with a sensitivity coefficient of 4.396, followed by the income from shared parking charges (3.885), and the salary of parking managers (3.619). (3) If the income from parking charges and the salary of parking managers were more than 69,408.5 and 31,091.1 yuan per month, respectively, and the cost of improving parking infrastructure was less than 14,003.2 yuan per month, residential areas could obtain additional benefits due to the acceptance of a shared parking scheme. This study provides theoretical support for the reasonable determination of the costs, risks, and benefits associated with participating in a shared parking scheme in a residential area. Document type: Article
Abstract
Shared parking schemes are not commonly implemented in residential areas due to the uncertainty and conflicts associated with the benefits of such schemes for stakeholders, [...]
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The Influence of Jam Density and Merging Cyclists on the Queue Discharge Rate
V. Knoop, M. Wierbos, S. Hoogendoorn, B. Goñi-Ros

Collection of open articles in transport research (2020). Vol. 2020, 264

Abstract
An increasing number of people use the bicycle for urban trips resulting in local congestion at intersections, especially during peak hours. Understanding the queue dynamics is key to find the correct measures that can reduce the delays for cyclists without affecting other traffic modes. To this end, the discharge process of bicycle queues is studied, focusing on the impact of jam density on the queue discharge rate and how this process is affected by cyclists that merge into the queue during the discharge phase. The impact of merging cyclists is captured by a newly introduced bicycle equivalent (BE) value. This direction-specific BE value is used to convert a merging cyclist into a cyclist that is waiting in the original queue. Results show that the queue discharge rate increases with increasing density of the queue. Furthermore, cyclists that merge by overtaking contribute to the queue discharge rate, while cyclists who merge from a perpendicular direction hinder the discharge process, thereby decreasing the bicycle flow at the intersection. The insights can be used to develop measures which minimise delay at intersections and to design efficient infrastructure for bicyclists. Document type: Article
Abstract
An increasing number of people use the bicycle for urban trips resulting in local congestion at intersections, especially during peak hours. Understanding the queue dynamics [...]
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Aberrant Driving Behaviours on Risk Involvement among Drivers in China
J. Yin, H. Shao, Q. Wang, W. Yu

Collection of open articles in transport research (2020). Vol. 2020, 263

Abstract
The purpose of this study is to validate the version of Driver Behaviour Questionnaire (DBQ) by considering distractions, fatigue, and drunk driving, the main reasons for accidents in China, as independent parts of violations and errors and further explore the effects of demographic/driving variables and all factors on risk involvement (accident involvement and penalized points). 241 drivers filled in a self-completion questionnaire with 28 items conducted in Xi’an in August 2018. Exploratory factor analysis confirmed a five-factor structure, including violations, distracted driving, errors, drunk driving, and fatigued driving. The frequency of aberrant driving behaviours indicated that distractions were the most prevalent behaviours followed by fatigue. The results showed that drivers with lower education and longer annual mileages were positive with accident involvement while there was no significance in penalized points. Violations and distractions were important factors causing both accidents and penalized points. Therefore, it is effective to reduce accident involvement by establishing educational training and related laws or installing intelligent monitor vehicle equipment to warn drivers to improve safety. Document type: Article
Abstract
The purpose of this study is to validate the version of Driver Behaviour Questionnaire (DBQ) by considering distractions, fatigue, and drunk driving, the main reasons for [...]
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Potential for, and drivers of, private voluntary initiatives for the decarbonisation of short sea shipping: evidence from a Swedish ferry line
A. Christodoulou, K. Cullinane

Collection of open articles in transport research (2020). Vol. 2020, 262

Abstract
The aim of this paper is to analyse shipping firms’ reactions to environmental challenges and identify how significant reductions in energy consumption and air emissions can come about by implementing a variety of voluntary initiatives. The paper focuses on the various sustainability initiatives implemented by the Swedish short sea shipping operator, Stena Line, either on a purely voluntary basis as part of their corporate social responsibility (CSR) strategy or as their chosen route for compliance with regulations. A conceptual model is developed based on stakeholder theory, the theory of planned behaviour and resource dependence theory to understand the main drivers of the firm’s adoption of sustainability initiatives and the factors affecting the integration of CSR in maritime companies. According to our findings, the company operates within a strongly enforced regulatory environmental framework and needs to exceed this framework to differentiate its service and strengthen its relationships with its customers by addressing their social and environmental concerns. As the firm’s competitive strategy focuses on service differentiation, a large pool of complementary resources is available for CSR and the implementation of sustainability practices. The results of this paper bring new insights to the potential of local private voluntary initiatives for the reduction of maritime air emissions. These include the provision of onshore power supply, the conversion of vessels to use methanol, ferry electrification, the construction of larger RoPax vessels and the implementation of an energy-saving program that focuses on crew involvement and continuous training. The environmental outcomes derived from a combination of local operational and technical energy efficiency measures are found to be significant and can contribute to the efforts for the achievement of sustainable maritime transport undertaken by international and regional organisations. The main barriers for the adoption of voluntary sustainability initiatives in the maritime sector are economic and technological. To encourage the wider adoption of these initiatives, the provision of economic incentives at national or regional level is crucial, as such initiatives usually imply high initial installation costs that should be somehow compensated for both vessels and terminals. Document type: Article
Abstract
The aim of this paper is to analyse shipping firms’ reactions to environmental challenges and identify how significant reductions in energy consumption and air emissions [...]
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Substantiating The Logistics Chain Structure while Servicing The Flow of Requests for Road Transport Deliveries
V. Naumov

Collection of open articles in transport research (2020). Vol. 2020, 261

Abstract
The selection of the delivery scheme is one of the most complicated problems and the results of its solution condition the sustainable development of the whole market of transportation services. Freight forwarders should consider numerous random parameters characterizing demand and technological processes to choose the proper structure of the logistics chain. The paper aims to propose a method for choosing the logistics chain structure, based on the analysis of the total expenses as a function from the demand parameters characterizing stochastic variables of the consignment weight, the delivery distance, and the time interval between the requests in the flow of queries for cargo delivery. Four basic logistics chain structures, widely used on road transport, are described to demonstrate the selection process. The areas of the most efficient use of the logistics chain structures can be defined for the flow of requests for cargo deliveries. The paper shows such areas on the example of goods delivery by automobile transport. Determining the areas of the most efficient use of the specific logistics chain structures contributes to the effective choice of correct delivery variants by freight forwarders. Document type: Article
Abstract
The selection of the delivery scheme is one of the most complicated problems and the results of its solution condition the sustainable development of the whole market of transportation [...]
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Intra-City Mobility and Characterization in a Fast-growing City of Lagos, Nigeria
S. Okunubi, U. Salisu, S. Fasina, A. Akanmu

Collection of open articles in transport research (2020). Vol. 2020, 260

Abstract
Effective urban transportation no doubt serves as engine room and catalyst for driving national economic development. Significantly, the purpose of urban transport is to provide both passenger and freight mobility over specific parts of urban areas including cities, and its efficiency is characterized upon transporting effectively and achieving economies of scale. Hence, this study examined intra-city mobility and characterization in Lagos, Nigeria. The data was sourced from both primary and secondary sources. Primary data detailed the use of two sets of questionnaires administered to commuters and motorists. 182 copies of questionnaire were randomly administered to commuters, while 60 units of the questionnaire were purposively and conveniently administered to motorists. Descriptive and inferential techniques were used for data analysis. Major findings revealed obvious variations in socio-economic parameters of intra-city trip makers and factors influencing trip making. It was observed that journey to work, school, shopping cum business constituted the major trips characterizing in Lagos. Findings also revealed that high patronage priority was given to most used and preferred means due to vehicle travel speed, trip purpose, and availability than safety and comfortability of modal choice. Regression analysis result revealed that commuters’ modal choice and patronage is statistically influenced by operational attributes of mode (e.g. transit time, delay duration, safety, vehicle condition and transit fare etc.) at Sig. p=0.000 and F"jats:sup"14"/jats:sup""jats:sub"165"/jats:sub" 15.667 which is greater than table value at 5% significant level. The study recommended among others the formulation and implementation of effective policy for urban transport activities; standardization of service operations and expansion of infrastructural facilities including the last-mile in the city. Document type: Article
Abstract
Effective urban transportation no doubt serves as engine room and catalyst for driving national economic development. Significantly, the purpose of urban transport is to provide [...]
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Operators’ Load Monitoring and Management
U. Kale, J. Rohacs, D. Rohacs

Collection of open articles in transport research (2020). Vol. 2020, 259

Abstract
Due to the introduction of highly automated vehicles and systems, the tasks of operators (drivers, pilots, air traffic controllers, production process managers) are in transition from “active control” to “passive monitoring” and “supervising”. As a result of this transition, the roles of task load and workload are decreasing while the role of the mental load is increasing, thereby the new type of loads might be defined as information load and communication load. This paper deals with operators’ load monitoring and management in highly automated systems. This research (i) introduces the changes in the role of operators and requirements in load management, (ii) defines the operators’ models, (iii) describes the possible application of sensors and their integration into the working environment of operators, and (iv) develops the load observation and management concept. There are some examples of analyses of measurements and the concept of validation is discussed. This paper mainly deals with operators, particularly pilots and air traffic controllers (ATCOs). Document type: Article
Abstract
Due to the introduction of highly automated vehicles and systems, the tasks of operators (drivers, pilots, air traffic controllers, production process managers) are in transition [...]
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Identification of distinguishing characteristics of intersections based on statistical analysis and data from video cameras
V. Shepelev, S. Shabiev, A. Glushkov, S. Aliukov

Collection of open articles in transport research (2020). Vol. 2020, 258

Abstract
The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article was to identify the main patterns of traffic at intersections in traffic congestion and to develop an analysis technique to improve traffic management at intersections. In modern conditions, there is a sharp increase in the number of vehicles, which leads to negative consequences, such as an increase in travel time, additional fuel consumption, increased risk of traffic accidents and others. To solve the problem of improving traffic control at intersections, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The purpose of this article is to determine the most important traffic characteristics that affect the throughput of intersections. As a criterion for the cross-pass ability of the intersection, the actual number of passing cars during the permission signal of the torch light is taken. Using multivariate regression analysis, a model was developed to predict intersection throughput taking into account the most important traffic characteristics. Analysis of the throughput of intersections using the fuzzy logic method confirmed the correctness of the developed model. In addition, based on the results of processing information collected at 20 intersections and including 597 observations, a methodology was developed for determining the similarity of traffic intersections. This allows us to identify homogeneous types of intersections and to develop typical traffic management techniques in the city, instead of individually managing each node of the city’s transport network individually. The results obtained lead to a significant reduction in costs for the organization of rational traffic flows. Document type: Article
Abstract
The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article [...]
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Effect of Synthetic Fibers and Hydrated Lime in Porous Asphalt Mixture Using Multi-Criteria Decision-Making Techniques
M. Calzada-Pérez, P. Lastra-González, C. Slebi-Acevedo, D. Castro-Fresno

Collection of open articles in transport research (2020). Vol. 2020, 257

Abstract
Porous asphalt is a type of mixture characterized by having high air void percentages that offers multiple benefits when used in wearing courses in terms of driving safety, water flow management, and noise reduction. However, the durability of porous asphalt (PA) mixtures is significantly shorter when compared to dense-graded asphalt mixtures. This study investigated the impact of polyolefin-aramid fibers and hydrated lime in the functional and mechanical performance of porous asphalt mixtures. A parametric study based on the concept of design of experiments was carried out through the Taguchi methodology. Accordingly, an experimental design was conducted based on the L18 full factorial orthogonal array. Three control factors-fiber content, binder content, and filler type- were included at various levels, and multiple responses including total air voids, interconnected air voids, particle loss in dry conditions, particle loss in wet conditions, and binder drainage were assessed experimentally. Signal-to-noise ratios were calculated to determine the optimal solution levels for each control factor for the multiple responses. In the second phase of the research, multi-criteria decision-making techniques -namely, criteria importance through inter-criteria correlation and weighted aggregated sum product assessment- were used to transform the multiple-response optimization problem into a single-unique optimization problem and to elaborate a preference ranking among all the mixture designs. The most significant levels for acquiring the optimum overall response value were found to be 0.05% for fiber content and 5.00% for binder content and mixed filler with hydrated lime Funding: TheFORESEEproject has received funding from the European Union?s Horizon 2020 research and innovation program under grant agreement No769373. Document type: Article
Abstract
Porous asphalt is a type of mixture characterized by having high air void percentages that offers multiple benefits when used in wearing courses in terms of driving safety, [...]
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Project O2 - Macroscopic Fundamental Diagram Based Discrete Transportation Network Design
G. Hu, W. Lu, F. Wang, R. Whalin

Collection of open articles in transport research (2020). Vol. 2020, 256

Abstract
The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and "jats:italic"k"/jats:italic"-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results. Document type: Article
Abstract
The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the [...]
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Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America
Y. Kachba, J. Belotti, T. Alves, Y. Tadano, H. Siqueira, D. Chiroli

Collection of open articles in transport research (2020). Vol. 2020, 255

Abstract
The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations&rsquo health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations&rsquo health related to road transport variables for Sã o Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron&mdash MLP, Extreme Learning Machines&mdash ELM, and Echo State Neural Networks&mdash ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity&mdash hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3&mdash good health and well-being, 7&mdash affordable and clean energy, and 11&mdash sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system. Document type: Article
Abstract
The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of [...]
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Does Information on Automated Driving Functions and the Way of Presenting It before Activation Influence Users’ Behavior and Perception of the System?
S. Danner, M. Pfromm, K. Bengler

Collection of open articles in transport research (2020). Vol. 2020, 254

Abstract
Information on automated driving functions when automation is not activated but is available have not been investigated thus far. As the possibility of conducting non-driving related activities (NDRAs) is one of the most important aspects when it comes to perceived usefulness of automated cars and many NDRAs are time-dependent, users should know the period for which automation is available, even when not activated. This article presents a study (N = 33) investigating the effects of displaying the availability duration before&mdash versus after&mdash activation of the automation on users&rsquo activation behavior and on how the system is rated. Furthermore, the way of addressing users regarding the availability on a more personal level to establish &ldquo sympathy&rdquo with the system was examined with regard to acceptance, usability, and workload. Results show that displaying the availability duration before activating the automation reduces the frequency of activations when no NDRA is executable within the automated drive. Moreover, acceptance and usability were higher and workload was reduced as a result of this information being provided. No effects were found with regard to how the user was addressed. Document type: Article
Abstract
Information on automated driving functions when automation is not activated but is available have not been investigated thus far. As the possibility of conducting non-driving [...]
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A Study on Public Adoption of Robo-Taxis in China
K. Hu, M. Liu, C. Zhu, J. Wu

Collection of open articles in transport research (2020). Vol. 2020, 253

Abstract
Autonomous vehicles (AVs) have been reported to improve road safety, reduce traffic congestion, and increase urban mobility. However, the high price of AVs is currently a challenge for most consumers. Robo-taxi services, with ride-sharing services and AVs, are regarded as a good approach to solving this problem. As some companies have started testing Robo-taxis on the actual road, it has become important to investigate public adoption of Robo-taxi services before they are more widely introduced to the market. This study aims to explain and predict users’ acceptance of Robo-taxis by extending the Technology Acceptance Model by including the construct of social influence. Data were collected from an online survey in China and analyzed using linear regression models. The results indicate that perceived usefulness, perceived ease of use, and social influence have significant positive correlations with people’s behavior intentions to use Robo-taxis. Perceived ease of use further has an indirect effect on intention to use via perceived usefulness. The results of this study can serve as good references for policymakers, operators, and future transport researchers. Document type: Article
Abstract
Autonomous vehicles (AVs) have been reported to improve road safety, reduce traffic congestion, and increase urban mobility. However, the high price of AVs is currently a [...]
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Data for Heuristic Optimization of Electric Vehicles’ Charging Configuration Based on Loading Parameters
S. Haider, P. Schegner

Collection of open articles in transport research (2020). Vol. 2020, 252

Abstract
This dataset includes multiple files related to optimization of electric vehicles to minimize overloading in low voltage grids by varying the locations available to charge the EVs. The data include lognormally sampled hourly sorted scenarios across 11 charging locations for a stochastics-based Monte Carlo simulation. This simulation runs through 2 million scenarios based on actual probabilities to incorporate most possible situations. It also includes samples from normally distributed household electricity use scenarios based on agent-based modeling. The article includes the test grid parameters for simulation, which were used to create a benchmark grid in DigSilent Powerfactory software, as well as intermediate outputs defining worst case scenarios when electric vehicles were charged and results from three different optimization approaches involving a reduction in voltage drops, cable overloading and total line losses. The outputs from the benchmark grid were used to train a machine learning algorithm, the weights and codes for which are also attached. This trained network acted as the grid for subsequent iterative optimization procedures. Outputs are presented as a comparison between pre-optimization and post-optimization scenarios. The above dataset and procedure were repeated while varying the number of EVs between 0 and 100 in increments of 20, data for which are also attached. The data article supports a related submission titled “Minimization of Overloading Caused by Electric Vehicle (EV) Charging in Low Voltage Networks”. Document type: Article
Abstract
This dataset includes multiple files related to optimization of electric vehicles to minimize overloading in low voltage grids by varying the locations available to charge [...]
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An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model
M. Raza, C. Qi, M. Asif, M. Khan

Collection of open articles in transport research (2020). Vol. 2020, 251

Abstract
License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions. Document type: Article
Abstract
License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed [...]
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Experimental and Numerical Analyses on Mixing Uniformity of Water and Saline in Pipe Flow
Q. Liu, Y. Lu, H. Fang, B. Sun, J. Zhang, C. Zhang

Collection of open articles in transport research (2020). Vol. 2020, 250

Abstract
Liquid—liquid mixing is commonly observed in many applications such as the chlorination of water supplies and the agricultural fertigation. In order to study the mixing law of water-chlorine or water-fertilizer in a turbulent pipeline, saline was selected as a tracer injected into the pipeline. In this paper, the computational fluid dynamics (CFD) software was employed to study flow fields in water-saline pipelines. Four variates (mixing ratio "i"δ"/i", pipe diameter "i"D"/i", volume flow rate in the main pipe "i"Q, "/i"saline density "i"ρ"sub"s"/sub""/i") were considered to investigate the effects of multiple variates on mixing uniformity. The coefficient of variation (COV) was selected as the evaluation index of mixing uniformity, effective mixing length ("i"L"sub"EML"/sub""/i", the distance from the saline inlet to the fully mixed position) was chosen to quantitatively analyze the fully mixed position of water and saline in pipelines. The results of this numerical model agree well with experimental measurements and it shows that this model can effectively predict the concentration field of water and saline in the pipeline. Based on the experimental and simulated results, it was found that for the fixed mixing ratio, saline density and volume flow rate, the values of "i"L"sub"EML"/sub""/i" increased significantly with increasing pipe diameters. Furthermore, dimensional analysis (D-A) was adopted to examine the influences of the four variates on "i"L"sub"EML"/sub""/i", and their correlation coefficient of the curve-fitting equation was calculated to be 0.996. Document type: Article
Abstract
Liquid—liquid mixing is commonly observed in many applications such as the chlorination of water supplies and the agricultural fertigation. In order to study the mixing [...]
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Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors
J. Ma, W. Li, C. Jia, C. Zhang, Y. Zhang

Collection of open articles in transport research (2020). Vol. 2020, 249

Abstract
Encounter risk prediction is critical for safe ship navigation, especially in congested waters, where ships sail very near to each other during various encounter situations. Prior studies on the risk of ship collisions were unable to address the uncertainty of the encounter process when ignoring the complex motions constituting the dynamic ship encounter behavior, which may seriously affect the risk prediction performance. To fill this gap, a novel AIS data-driven approach is proposed for ship encounter risk prediction by modeling intership behavior patterns. In particular, multidimensional features of intership behaviors are extracted from the AIS trace data to capture spatial dependencies between encountering ships. Then, the challenging task of risk prediction is to discover the complex and uncertain relationship between intership behaviors and future collision risk. To address this issue, we propose a deep learning framework. To represent the temporal dynamics of the encounter process, we use the sliding window technique to generate the sequences of behavioral features. The collision risk level at a future time is taken as the class label of the sequence. Then, the long short-term memory network, which has a strong ability to model temporal dependency and complex patterns, is extended to establish the relationship. The benefit of our approach is that it transforms the complex problem for risk prediction into a time series classification task, which makes collision risk prediction reliable and easier to implement. Experiments were conducted on a set of naturalistic data from various encounter scenarios in the South Channel of the Yangtze River Estuary. The results show that the proposed data-driven approach can predict future collision risk with high accuracy and efficiency. The approach is expected to be applied for the early prediction of encountering ships and as decision support to improve navigation safety. Document type: Article
Abstract
Encounter risk prediction is critical for safe ship navigation, especially in congested waters, where ships sail very near to each other during various encounter situations. [...]
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Collective Risk Ranking of Highway Segments on the Basis of Severity-Weighted Crash Rates
D. Li, M. Al-Mahamda

Collection of open articles in transport research (2020). Vol. 2020, 248

Abstract
This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each model besides the abstraction of new features to check if there are unobserved correlations affecting the independent variables, such as accounting for the number and weight of crashes within 1 km2 area by implementing the Getis-Ord Gi∗ index. Moreover, to calculate the collective risk (CR) score, crash rates were adjusted to incorporate crash severity weights (cost per severity type) and regression-to-the-mean (RTM) bias via Empirical Bayes (EB) method. Finally, segments were ranked according to their CR score. Document type: Article
Abstract
This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) [...]
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Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory
C. Aswakul, H. Ochiai, E. Mon, C. Saivichit

Collection of open articles in transport research (2020). Vol. 2020, 247

Abstract
The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors. Document type: Article
Abstract
The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion [...]
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Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data
H. Chen, H. Chen, Z. Liu, X. Sun, R. Zhou

Collection of open articles in transport research (2020). Vol. 2020, 246

Abstract
The research and development of autonomous vehicle (AV) technology have been gaining ground globally. However, a few studies have performed an in-depth exploration of the contributing factors of crashes involving AVs. This study aims to predict the severity of crashes involving AVs and analyze the effects of the different factors on crash severity. Crash data were obtained from the AV-related crash reports presented to the California Department of Motor Vehicles in 2019 and included 75 uninjured and 18 injured accident cases. The points-of-interest (POI) data were collected from Google Map Application Programming Interface (API). Descriptive statistics analysis was applied to examine the features of crashes involving AVs in terms of collision type, crash severity, vehicle movement preceding the collision, and degree of vehicle damage. To compare the classification performance of different classifiers, we use two different classification models: eXtreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART). The result shows that the XGBoost model performs better in identifying the injured crashes involving AVs. Compared with the original XGBoost model, the recall and G-mean of the XGBoost model combining POI data improved by 100% and 11.1%, respectively. The main features that contribute to the severity of crashes include weather, degree of vehicle damage, accident location, and collision type. The results indicate that crash severity significantly increases if the AVs collided at an intersection under extreme weather conditions (e.g., fog and snow). Moreover, an accident resulting in injuries also had a higher probability of occurring in areas where land-use patterns are highly diverse. The knowledge gained from this research could ultimately contribute to assessing and improving the safety performance of the current AVs. Document type: Article
Abstract
The research and development of autonomous vehicle (AV) technology have been gaining ground globally. However, a few studies have performed an in-depth exploration of the [...]
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Revisiting Accessibility in a Context of Sustainable Transport: Capabilities and Inequalities in Bogotá
L. Guzman, D. Oviedo

Collection of open articles in transport research (2020). Vol. 2020, 245

Abstract
This research sets out to examine the interaction between accessibility, social inequalities and sustainability by revisiting a well-documented case of inequalities in access in Global South cities: Bogotá . Our analysis builds on the concept of accessibility, focusing on the role of transport as an enabler of opportunities for social interactions, healthcare and leisure, which are essential to the full participation in society. The research applies a methodological framework for accessibility developed and tested in the Latin American context, though it has not been previously applied to non-commuting travel. Our paper contributes to the existing evidence base showing the relevance of non-commuting travel to sustainable transport assessment in contexts of high inequality, which can be scaled up and applied in other contexts with various levels of social and environmental inequalities. The empirical analysis is based on data from Bogotá &rsquo s household travel survey. Using this dataset, we measure accessibility using a gravitational type of metric that is calibrated based on observed travel behavior. Accessibility levels were estimated by car and public transport for every zonal planning unit in the Bogotá Region. Accessibility indices are analyzed from an equity perspective using metrics such as the Palma ratio for differences of income and socioeconomic positions. Results show that on average, low- and middle-income areas have higher accessibility than high-income areas by both private and public transport. Accessibility conditions are discussed considering a framework of transitions to sustainable urban mobility, reflecting on various drivers and consequences of barriers to access in different areas and social groups. Our findings provide insights to support actions that redistribute accessibility of opportunities beyond the job market, questioning the applicability of accessibility measures to discuss equity and sustainability in cities such as Bogotá . Document type: Article
Abstract
This research sets out to examine the interaction between accessibility, social inequalities and sustainability by revisiting a well-documented case of inequalities in access [...]
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Pedidos y transporte: Una unidad estratégica de estudio en indicadores logísticos de la nueva era
S. Chaluisa-Chaluisa, C. Pico-Cherres, M. Mancheno-Saá

Collection of open articles in transport research (2020). Vol. 2020, 244

Abstract
La presente investigación tiene como objetivo analizar las tendencias principales que comprende los pedidos y el transporte, para ello se ha considerado como referencia tres fuentes de análisis: 1) Investigación de Campo, en el sector comercial de la zona 3 del Ecuador. 2) Análisis gráfico descriptivo del mercado 3) Análisis estadístico inferencial mediante la prueba de Friedman. La metodología se enmarcó en un análisis descriptivo explicativo, el instrumento de investigación fue una encuesta aplicado a una muestra conformada de 373 empresas comerciales. Los resultados obtenidos reflejaron un nivel de significancia asintótica de 0.092 mediante la prueba de Friedman para las variables consideradas en comparación con el nivel crítico de 0.05 por lo cual se indica que no hay diferencias estadísticas significativas entre las empresas en relación a las dimensiones pedidos y transporte en las 4 provincias seleccionadas. Document type: Article
Abstract
La presente investigación tiene como objetivo analizar las tendencias principales que comprende los pedidos y el transporte, para ello se ha considerado como referencia tres [...]
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Initial Rotor Position Detection for Permanent Magnet Synchronous Motor Based on High-Frequency Voltage Injection without Filter
Z. Wang, B. Yao, L. Guo, X. Jin, X. Li, H. Wang

Collection of open articles in transport research (2020). Vol. 2020, 243

Abstract
The accurate initial rotor position of a permanent magnet synchronous motor (PMSM) is necessary for starting the motor, and for the position sensorless control method adopted by a PMSM control system under some working conditions. This paper presents a new method to detect the initial rotor position of a permanent magnet synchronous motor (PMSM). The method does not need a low-pass filter, and has strong robustness and a simple calculation method. According to the relationship between high-frequency current response and rotor position angle θ, the rotor position angle can be obtained by arctangent and linear formulae. Finally, the magnetic polarity of the rotor is distinguished according to the change of inductance. In this method, the arctangent function is used to eliminate the filtering process and reduce the influence of the parameter deviation of the motor system on the detection accuracy of the initial position. The experimental results verify the correctness of the theoretical analysis and the effectiveness of the method. Document type: Article
Abstract
The accurate initial rotor position of a permanent magnet synchronous motor (PMSM) is necessary for starting the motor, and for the position sensorless control method adopted [...]
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Public Transport Quality, Safety, and Perceived Accessibility
M. Friman, L. Olsson, K. Lättman

Collection of open articles in transport research (2020). Vol. 2020, 242

Abstract
Service quality in public transport is proposed as a key determinant of perceived accessibility, the ease to live the life one wants with the help of the transport system, as low service quality may be a barrier for use, decreasing the ease to participate in daily activities. The first aim was to validate the direct relationship between public transport quality and perceived accessibility. Secondly, we analyzed the mediating role of safety perceptions to better explain the link between service quality and perceived accessibility. Public transport travelers (n = 4944) from five northern European cities were surveyed. Results from PLS-SEM modeling show that service quality has a significant and direct relationship with perceived accessibility, especially regarding functionality. An indirect relationship through travel safety perceptions was also observed, highlighting information and comfort as main drivers. High car use, low public transport use, increasing age, and being a woman were also associated with greater perceived accessibility. City comparisons yielded a number of significant differences. Our results contribute to the research literature by highlighting the importance of service quality in public transport for perceptions of accessibility in daily travel. In particular, we argue that functionality is the core attribute to focus on, and that attributes related to travel safety perceptions should be carefully considered when planning for sustainable transport. Document type: Article
Abstract
Service quality in public transport is proposed as a key determinant of perceived accessibility, the ease to live the life one wants with the help of the transport system, [...]
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An Approach for Handling Uncertainties Related to Behaviour and Vehicle Mixes in Traffic Simulation Experiments with Automated Vehicles
M. Friedrich, J. Lohmiller, A. Alessandrini, J. Olstam, F. Johansson, P. Sukennik

Collection of open articles in transport research (2020). Vol. 2020, 241

Abstract
The introduction of automated vehicles is expected to affect traffic performance. Microscopic traffic simulation offers good possibilities to investigate the potential effects of the introduction of automated vehicles. However, current microscopic traffic simulation models are designed for modelling human-driven vehicles. Thus, modelling the behaviour of automated vehicles requires further development. There are several possible ways to extend the models, but independent of approach a large problem is that the information available on how automated vehicles will behave is limited to todays partly automated vehicles. How future generations of automated vehicles will behave will be unknown for some time. There are also large uncertainties related to what automation functions are technically feasible, allowed, and actually activated by the users, for different road environments and at different stages of the transition from 0 to 100% of automated vehicles. This article presents an approach for handling several of these uncertainties by introducing conceptual descriptions of four different types of driving behaviour of automated vehicles (Rail-safe, Cautious, Normal, and All-knowing) and presents how these driving logics can be implemented in a commonly used traffic simulation program. The driving logics are also linked to assumptions on which logic that could operate in which environment at which part of the transition period. Simulation results for four different types of road facilities are also presented to illustrate potential effects on traffic performance of the driving logics. The simulation results show large variations in throughput, from large decreases to large increases, depending on driving logic and penetration rate. "p"Funding Agencies|European UnionEuropean Union (EU) [H2020-ART-2016-2017, 723201] Document type: Article
Abstract
The introduction of automated vehicles is expected to affect traffic performance. Microscopic traffic simulation offers good possibilities to investigate the potential effects [...]
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A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)
P. Huang, X. Zhang, X. Chen, P. Saini, D. Yan, Y. Wu, B. Copertaro

Collection of open articles in transport research (2020). Vol. 2020, 240

Abstract
The deployment of solar photovoltaics (PV) and electric vehicles (EVs) is continuously increasing during urban energy transition. With the increasing deployment of energy storage, the development of the energy sharing concept and the associated advanced controls, the conventional solar mobility model (i.e., solar-to-vehicles (S2V), using solar energy in a different location) and context are becoming less compatible and limited for future scenarios. For instance, energy sharing within a building cluster enables buildings to share surplus PV power generation with other buildings of insufficient PV power generation, thereby improving the overall PV power utilization and reducing the grid power dependence. However, such energy sharing techniques are not considered in the conventional solar mobility models, which limits the potential for performance improvements. Therefore, this study conducts a systematic review of solar mobility-related studies as well as the newly developed energy concepts and techniques. Based on the review, this study extends the conventional solar mobility scope from S2V to solar-to-buildings, vehicles and storage (S2BVS). A detailed modeling of each sub-system in the S2BVS model and related advanced controls are presented, and the research gaps that need future investigation for promoting solar mobility are identified. The aim is to provide an up-to-date review of the existing studies related to solar mobility to decision makers, so as to help enhance solar power utilization, reduce buildings’ and EVs’ dependence and impacts on the power grid, as well as carbon emissions. Document type: Article
Abstract
The deployment of solar photovoltaics (PV) and electric vehicles (EVs) is continuously increasing during urban energy transition. With the increasing deployment of energy [...]
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Environmental Sustainability in City Logistics Measures
A. Szmelter-Jarosz, J. Rześny-Cieplińska

Collection of open articles in transport research (2020). Vol. 2020, 239

Abstract
Sustainable urban transport is fundamental not only for economic growth but also for the environmental protection, thus all logistics activities within the cities should be organized in a way to be environmentally friendly. The article aims at presenting the environmental sustainability of city logistics measures from different stakeholder perspectives. In the paper, a multi-method approach was implemented: literature review, text analysis, text mining, and statistical analysis. The paper presents how the stakeholders perceive urban logistics, if they see the need for coordination of its elements, who should be responsible for it, and what areas are the most important for them. The main task of this study is to recognize the priorities of different stakeholders. In consequence, the final effect of this article is an insight that is valuable not only for the local authorities but for many stakeholders, groups operating within the city that are and in many cases cooperating within the framework of creating sustainable urban mobility plans. Document type: Article
Abstract
Sustainable urban transport is fundamental not only for economic growth but also for the environmental protection, thus all logistics activities within the cities should be [...]
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A Novel RC-Based Architecture for Cell Equalization in Electric Vehicles
J. Mendiola-Santibañez, A. Estévez-Bén, M. Martínez-Prado, J. Rodríguez-Reséndiz, A. Alvarez-Diazcomas

Collection of open articles in transport research (2020). Vol. 2020, 238

Abstract
Nowadays, research on electric vehicles is increasing because they have the potential to decrease greenhouse-gas emissions dramatically in the transport sector. For these types of vehicles, the battery is one of the main components. The traction system needs a cell series connection to fulfill the energy requirements. Nevertheless, batteries differ from each other due to a normal dispersion in their capacity, internal resistance, and self-discharge rate. This paper presents a novel battery equalizer circuit using an RC-based topology to equalize two adjacent cells of a battery pack. It has the advantage of merging a resistor-based equalizer, a capacitor-based equalizer, and an RC-based equalizer in one circuit. In this way, it is possible to limit the current stress in the components of the circuit. The proposed method increases the equalization time by 35% for a threshold current of 4 A. However, it is possible design the system for another threshold current. Finally, the complexity of the controller is not compromised in the proposed architecture. The operation, analysis, and design of the architecture are presented and compared to the classic schemes. The theoretical analysis is validated through simulation results. Document type: Article
Abstract
Nowadays, research on electric vehicles is increasing because they have the potential to decrease greenhouse-gas emissions dramatically in the transport sector. For these [...]
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An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning
O. Hansen, H. Friedrich, A. Ludwig, A. Balster

Collection of open articles in transport research (2020). Vol. 2020, 237

Abstract
"jats:p"Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs. Document type: Article
Abstract
"jats:p"Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. [...]
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Fuel Economy of Plug-In Hybrid Electric and Hybrid Electric Vehicles: Effects of Vehicle Weight, Hybridization Ratio and Ambient Temperature
H. Jung

Collection of open articles in transport research (2020). Vol. 2020, 236

Abstract
Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are evolving rapidly since the introduction of Toyota Prius into the market in 1997. As the world needs more fuel-efficient vehicles to mitigate climate change, the role of HEVs and PHEVs are becoming ever more important. While fuel economies of HEVs and PHEVs are superior to those of internal combustion engine (ICE) powered vehicles, they are partially powered by batteries and therefore they resemble characteristics of battery electric vehicles (BEVs) such as dependence of fuel economy on ambient temperatures. It is also important to understand how different extent of hybridization (a.k.a., hybridization ratio) affects fuel economy under various driving conditions. In addition, it is of interest to understand how HEVs and PHEVs compare with BEVs at a similar vehicle weight. This study investigated the relationship between vehicle mass and vehicle performance parameters, mainly fuel economy and driving range of PHEVs focused on 2018 and 2019 model years using the test data available from fuel economy website of the US Environmental Protection Agency (EPA). Previous studies relied on modeling to understand mass impact on fuel economy for HEV as there were not enough number of HEVs in the market to draw a trendline at the time. The study also investigated the effect of ambient temperature for HEVs and PHEVs and kinetic energy recovery of the regenerative braking using the vehicle testing data for model year 2013 and 2015 from Idaho National Lab (INL). The current study assesses current state-of-art for PHEVs. It also provides analysis of experimental results for validation of vehicle dynamic and other models for PHEVs and HEVs. Document type: Article
Abstract
Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are evolving rapidly since the introduction of Toyota Prius into the market in 1997. As the world [...]
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Welfare consequences of request stops at transport services with low demand
G. Solvoll, F. Jørgensen

Collection of open articles in transport research (2020). Vol. 2020, 235

Abstract
Background Demand-responsive transport is an alternative to fixed-route, fixed-scheduled transport services in low-demand areas. Objective This paper discusses the welfare and distributional consequences of the implementation of request stops (RSs) on a scheduled fixed-stop transport service. Method The discussion is based on a general welfare model. The focus is on discussing how the magnitudes of the welfare effects on different groups of travellers are influenced by travel patterns and the characteristics of the travellers involved. Results The effects of implementing RSs are critically dependent on the booking procedure, the variation in demand throughout the day, and the travellers’ time values per hour when on the mode and when arriving at the destinations before having any appointments. Moreover, the benefits for the operators and the authorities depend strongly on the risk profile in the tendering contract. Document type: Article
Abstract
Background Demand-responsive transport is an alternative to fixed-route, fixed-scheduled transport services in low-demand areas. Objective This paper discusses the welfare [...]
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Sustainable Transport Infrastructure and Economic Returns: A Bibliometric and Visualization Analysis
L. Qiao, B. Sun, B. Badassa

Collection of open articles in transport research (2020). Vol. 2020, 234

Abstract
Sustainable transport infrastructure can determine the effect of countries&rsquo transport-driven economic returns. Considering the economic, environmental, and social relevance and growing issues of CO 2 in the countries concerned, this study aims to examine sustainable transport infrastructure related to economic return through a bibliometric and visualization analysis from 2000 to 2019. First, to measure the status of sustainable transport infrastructure literature, we determine the number of publications produced per year. Second, we determine the most frequently cited articles and prominent journals on sustainable transport infrastructure. Third, we examine the co-occurrence of the author&rsquo s keywords below the abstract. Fifth, we describe the bibliometric details in clusters and analyze the network link between reference, sources, and authors&rsquo co-citations, and discuss the characteristics and structures of clusters. Sixth, we discuss the bibliographic relationship between authors, and finally, determine the country and the institutional network of co-authors. The obtained results identify that the most influential articles, journals, and authors that make a significant contribution to sustainable transport infrastructure studies and present the research sub-areas or themes related to sustainable transport infrastructure. Overall, the study found the paradigms of today, key research areas, and the link between the fields of sustainable transport infrastructure studies. In the meantime, this study also reveals the improvements in the main topics and sub-sections over the last 20 years and shows the changes in future areas of research. The study concluded that the findings could provide researchers with some insights and help to advance studies on sustainable transport systems. Document type: Article
Abstract
Sustainable transport infrastructure can determine the effect of countries&rsquo transport-driven economic returns. Considering the economic, environmental, and social relevance [...]
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General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution
A. Montoyo, Y. Almeida-Cruz, Y. Gutiérrez, S. Estevez-Velarde

Collection of closed articles in transport research (2020). Vol. 2020, 54

Abstract
This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community. This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d’Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089).
Abstract
This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed [...]
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The most difficult at-fault fatal crashes to avoid with current active safety technology
R. Utriainen, T. Koisaari, T. Kari, T. Tervo

Collection of closed articles in transport research (2020). Vol. 2020, 53

Abstract
Objective: We studied which current fatal at-fault crashes would occur despite the most advanced current active safety devices (up to SAE level 2 of driving automation) and how frequent these crashes would be. Methods: We carried out a cross-sectional study of passenger cars that were first registered during the period 1st January 2010 to 31st December 2017 in Finland. To gain the true exposure for these cars, we accessed the national Vehicular and Driver Data Register to obtain the mileage information and the registration count for the study period of 2010-17. Similarly, we accessed the registry of Finnish road accident investigation teams and included all fatal at-fault crashes among the cars in our study for the same period. We used a real world reference technology for each active safety system in our analysis and chose one car brand as an example. This gave us exact system specifications and enabled testing the operation of the systems on the road. We performed field tests to gain further information on the precise operation of the safety systems in different operating conditions. Finally, we gathered all information on the studied active safety systems and analyzed the investigated at-fault fatal crashes case-by-case using our four level method. Results: Cars in our study were the primary party in 113 investigated fatal accidents during the years 2010-17. In 87 of the accidents, the leading cause of death was the injuries due to the crash, and these cases were classified as “unavoidable” (n = 58, 67 %), “avoidable” (n = 26, 30 %) or unsolved (n = 3, 3 %). Of the 58 “unavoidable” crashes 21 (36 %) were suicides, 21 (36%) involved active driver input which would have prevented the safety system operation, 15 (17 %) featured circumstances beyond the safety system performance and in one loss-of-control crash the driver had disabled the relevant safety system (electronic stability control). The registration years of the cars in our study (2010-17) totaled 3,772,864 and during this period, the cars travelled 75.9billion kilometers. The crash incidence of the “unavoidable” at-fault fatal crashes was 0.76-0.80 fatal crashes per billion kilometers and 15-16 fatal crashes per million registration years. Conclusions: We calculated a crash incidence for the “unavoidable” crashes which was 20–27% smaller than the observed crash rate of ESC-fitted passenger cars in our previous study. We concluded that suicides, active driver input until the crash, and challenging weather and road conditions are the most difficult factors for current active safety systems. Our analysis did not account for issues such as system usability or driver acceptance and therefore our results should be regarded as something that is currently theoretically achievable. However, the observed incidence is a good reference for automated driving development and the crash rate of automated cars. Peer reviewed
Abstract
Objective: We studied which current fatal at-fault crashes would occur despite the most advanced current active safety devices (up to SAE level 2 of driving automation) and [...]
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