Scipedia: Documents published in 2020

Design and Experimental Investigation of a Hybrid Rotor Permanent Magnet Modular Machine with 3D Flux Paths Accounting for Recyclability of Permanent Magnet Material

Scipedia content — Mon, 15 Feb 2021 12:25:32 +0100

Rare-earth metals used for manufacturing Permanent Magnets (PMs) remain classified as critical raw materials by the European Commission. In order to secure the supply of electrical machines due to the increasing demand of Hybrid and Full Electrical Vehicles ((H)EVs), recycling has emerged as a valuable alternative. Hence, this paper presents the concept of a modular PM machine with a hybrid rotor and 3D flux paths, for application in ((H)EVs). The proposed machine topology is intended to facilitate the extraction of PM material towards a recycling process. The selection of a machine for prototyping is carried out by investigating the effect of the variation of the number of rotor teeth and stator modules on various parameters, with models developed in Finite Element (FE). Finally, the models developed of the selected combination were validated with a detailed experimental evaluation of the prototype.

Document type: Article

Corrigendum: A Systems Thinking Approach to Exploring the Influence of the Media on How Publics Engage With and Develop Dialogues Relating to Electric Vehicles

Scipedia content — Mon, 15 Feb 2021 12:21:45 +0100

Document type: Article

Transferring Urban Mobility Studies in Tehran, Istanbul, and Cairo to Other Large MENA Cities: Steps toward Sustainable Transport

Scipedia content — Mon, 15 Feb 2021 12:20:55 +0100

The number of urban mobility studies and projects in the three large metropoles of the Middle East and North Africa (MENA) region, Tehran, Istanbul, and Cairo, is growing while other large cities do not enjoy a large share. It would be efficient for those other large cities to adapt the experiences, projects, and studies of Tehran, Istanbul, and Cairo to their own contexts. This paper can help facilitate that adaptation. It investigates the transferability and generalisability of the findings of a recent publication by the lead author on mobility choices in Tehran, Istanbul, and Cairo to some other large cities of more than one million inhabitants in the MENA region. The discussion provided here can provide decision-makers in the MENA region with guidance on how to utilise the findings from a recent study on Tehran/Istanbul/Cairo in their own contexts. T-tests were conducted to test the comparability of the three base cities with a sample 57 others with populations of over one million people. The results show that it would be possible to adapt the urban mobility studies of the three base megacities to 3 to 27 cities based on different criteria. Key suggestions identified by this study include providing local accessibility, neighbourhood facilities, and cycling facilities as well as removing social and legal constraints to cycling, advertising cycling, informing people about the harm arising from the overuse of cars, and increasing street connectivity by adding intersections. According to the findings, these evidence-based recommendations can enhance sustainable mobility for the inhabitants of up to 27 large cities.

Document type: Article

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

Scipedia content — Mon, 15 Feb 2021 12:20:28 +0100

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

Document type: Article

Railway Polygonized Wheel Detection Based on Numerical Time-Frequency Analysis of Axle-Box Acceleration

Scipedia content — Mon, 15 Feb 2021 12:19:41 +0100

The increasing need for repairs of polygonized wheels on high-speed railways in China is becoming problematic. At high speeds, polygonized wheels cause abnormal vibrations at the wheel-rail interface that can be detected via axle-box accelerations. To investigate the quantitative relationship between axle-box acceleration and wheel polygonization in both the time and frequency domains and under high-speed conditions, a dynamics model was developed to simulate the vehicle-track coupling system and that considers both wheel and track flexibility. The calculated axle-box accelerations were analyzed by using the improved ensemble empirical mode decomposition and Wigner-Ville distribution time-frequency method. The numerical results show that the maximum axle-box accelerations and their frequencies are quantitatively related to the harmonic order and out-of-roundness amplitude of polygonized wheels. In addition, measuring the axle-box acceleration enables both the detection of wheel polygonization and the identification of the degree of damage.

Document type: Article

Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry

Scipedia content — Mon, 15 Feb 2021 12:17:39 +0100

Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented.

Document type: Article

Gender-Responsive Public Transportation in the Dammam Metropolitan Region, Saudi Arabia

Scipedia content — Mon, 15 Feb 2021 12:17:04 +0100

The limited availability of public transportation in Saudi Arabia leads to an increased demand for private vehicles. An increase in using private cars does not meet the global sustainability goals, e.g., reducing energy consumption and improving the air quality. Road users should be encouraged to use sustainable mobility modes, particularly public transportation, equally accessible to both men and women However, women’s mobility has been somewhat limited and challenged in spatio-temporal terms, and partly due to socio-cultural barriers. This study attempts to understand the gender experience of a sample of public transport users and consider their aspirations and needs into daily mobility. A survey campaign (structured interviews and online questionnaires) was launched in the Dammam Metropolitan Region (DMR), taking four different types of respondents into account. The results suggest a predominant preference for taxis for shopping and leisure activities due to a poor public transport service, pivotally characterized by limited operational routes, hours, and infrastructure. This study ponders upon the adequacy of the supporting infrastructures and interior design of the public buses to women’s needs and compare them with global best practices. The results suggest that, due to the absence of a gender-responsive design and infrastructure, women are forced to use taxis, although privacy and a sense of insecurity often become concerns when traveling alone or with children. The study results allow future research to be expanded, considering women’s mobility patterns, needs, and embedded barriers by comparing the results with current transport policies, plans, and practices.

Document type: Article

Causes and Treatment Measures of Submarine Pipeline Free-Spanning

Scipedia content — Mon, 15 Feb 2021 12:16:38 +0100

Submarine pipelines, as arteries for offshore oil and gas transportation, play a particularly important role in the exploitation of offshore oil and gas resources. Since the world’s first submarine pipelines were laid in the Gulf of Mexico, numerous failures have been caused by pipeline free-spanning. This paper provides a review of the causes and treatment measures for the free span of submarine pipeline. Various factors cause the free span of submarine pipelines, including wave flow scouring, fluctuations in seabed topography, residual stress or thermal stress of pipelines, and human activities. The scour of the wave current is the main factor affecting free span; the research on sediment starting and equilibrium depth during scour is reviewed in-depth. For the span treatment of submarine pipelines, the main measures available at present include the re-digging trench burying, structural support, covering bionic water plants, and choke plate self-burying. For each, the principle, advantages, disadvantages, and research are discussed. This review provides a convenient resource for understanding the causes of submarine free-spanning pipelines and choosing suitable treatment measures.

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A Collaborative Approach for an Integrated Modeling of Urban Air Transportation Systems

Scipedia content — Mon, 15 Feb 2021 12:14:33 +0100

The current push in automation, communication, and electrical energy storage technologies has the potential to lift urban mobility into the sky. As several urban air mobility (UAM) concepts are conceivable, all relevant physical effects as well as mutual interrelations of the UAM system have to be addressed and evaluated at a sufficient level of fidelity before implementation. Therefore, a collaborative system of systems modeling approach for UAM is presented. To quickly identify physical effects and cross-disciplinary influences of UAM, a pool of low-fidelity physical analysis components is developed and integrated into the Remote Component Environment (RCE) workflow engine. This includes, i. a., the disciplines of demand forecast, trajectory, vertiport, and cost modeling as well as air traffic flow and capacity management. The definition and clarification of technical interfaces require intensive cooperation between specialists with different areas of expertise. To reduce this communication effort, the Common Parametric Aircraft Configuration Schema (CPACS) is adapted and used as central data exchange format. The UAM system module is initially applied for a 24-hour simulation of three generic networks in Hamburg City. After understanding the basic system-level behavior, higher level analysis components and feedback loops must be integrated in the UAM system module for evaluation and optimization of explicit operating concepts. Document type: Article

Investigation of the frequency of free vibrations for pipelines with different physical and mechanical properties of the material

Scipedia content — Mon, 15 Feb 2021 12:11:04 +0100

This paper raises the question of a new approach to the dynamic calculation of thin-walled underground pipelines of large diameter, which is based on the application of the Vlasov-Novozhilov half-time theory of medium-bend shells, which ignores the M1 moments that bend the cylindrical shell in the longitudinal direction, since they are much smaller than the M2 moments that bend it in the transverse direction. The resolving equation for this approach is a homogeneous 4th-order differential equation that uses two boundary conditions at each end to solve it. The resulting equation takes into account the parameter of the longitudinal force, the value of the internal pressure, the coefficient of elastic resistance of the soil, the parameter of thinness, as well as the attached mass of the soil. Based on the data obtained from the derived formulas, the frequency characteristics of thin-walled underground pipelines of large diameter with different physical and mechanical properties are determined depending on the length of the element, as well as ground conditions. It is established that the minimum frequencies for the shell form of vibrations in various ground conditions are realized only for steel pipes, and for polyethylene and fiberglass pipes, depending on the coefficient of elastic resistance of the soil, they can be realized both in the rod and shell form. At the same time, using a dynamic stability criterion, derived expressions to determine the critical external pressure, taking into account the pipe length and the number of half waves in the cross section in which there is a constructive denial of the pipeline. Based on this expression, a formula for determining the critical depth of laying for thin-walled pipelines is obtained.

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An Analysis of the Interactions between Adjustment Factors of Saturation Flow Rates at Signalized Intersections

Scipedia content — Mon, 15 Feb 2021 12:09:38 +0100

n insufficient functional relationship between adjustment factors and saturation flow rate (SFR) in the U.S. Highway Capacity Manual (HCM) method increases an additional prediction bias. The error of SFR predictions can reach 8%&ndash

10%. To solve this problem, this paper proposes a comprehensive adjusted method that considers the effects of interactions between factors. Based on the data from 35 through lanes in Beijing and 25 shared through and left-turn lanes in Washington, DC, the interactions between lane width and percentage of heavy vehicles and proportion of left-turning vehicles were analyzed. Two comprehensive adjustment factor models were established and tested. The mean absolute percentage error (MAPE) of model 1 (considering the interaction between lane width and percentage of heavy vehicles) was 4.89% smaller than the MAPE of Chinese National Standard method (Standard Number is GB50647) at 13.64%. The MAPE of model 2 (considering the interaction between lane width and proportion of left-turning vehicles was 33.16% smaller than the MAPE of HCM method at 14.56%. This method could improve the accuracy of SFR prediction, provide support for traffic operation measures, alleviate the traffic congestion, and improve sustainable development of cities.

Document type: Article

Sustainability of the Air Cargo Handling Process in the Context of Safety and Environmental Aspects

Scipedia content — Mon, 15 Feb 2021 12:08:41 +0100

In addition to passenger traffic, air cargo business is an important business for a global air transport industry. This means that Air Cargo Handling Process (ACHP) is important for any airport or cargo handling agent who provides cargo handling services. To qualitatively manage the ACHP, certain prerequisites must be met, such as competent cargo staff, procedures, cargo information system, infrastructure with enough capacity, and process management. The objective of research presented in this paper is ACHP and its complex structure but in the context of safety and sustainability. Using of several scientific methods of cognition, the authors research the structure of ACHP and safety, and ecological aspects of the process, too. The result of this research is the safety and environmental aspects of the process which are of significant importance for process functionality as well as for the quality level of service that meets customer requirements and to the sustainability of process. Results show that there is a significant impact of the environmental and safety aspects specific to particular activities in ACHP and that they affect the reliability and functionality of the whole process, its profitability, and competitiveness. This points to the need for ACHP to be viewed in context and to understand ACHP sustainability matters and sustainability components.

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The Impact of E-Commerce-Related Last-Mile Logistics on Cities: A Systematic Literature Review

Scipedia content — Mon, 15 Feb 2021 12:08:25 +0100

E-commerce-related last-mile logistics have a great impact on cities. Recent years have seen sustained growth in e-commerce in most developed countries, a trend that has only been reinforced by the COVID-19 pandemic. The perceived impact of this phenomenon varies depending upon the perspective of the players involved: individual members of the public, companies, or the public administrations. Tackling the issue from these perspectives, the goal of this article is to explore the kinds of impact this phenomenon has and will have. We use as the basis for their classification the so-called triple bottom line (TBL) of sustainability, encompassing people, planet, and profit; we complement this with the impact classification used by the European Science Foundation’s impact assessment working group. After performing a systematic review of the literature following PRISMA guidelines, our results show that, albeit to different degrees, the four impact dimensions analyzed (economic, social, environmental, and technological) have only received incipient coverage in the existing literature. Given its ever-growing importance, we believe that greater attention needs to be paid to this phenomenon, especially with regard to those aspects having the greatest impact upon urban systems and the different stakeholders involved. Only in this way can the public policies needed to mitigate these externalities be properly implemented.

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Assessment of Priorities of Construction of High-Speed Rail in the Czech Republic in Terms of Impacts on Internal and External Integration

Scipedia content — Mon, 15 Feb 2021 12:07:56 +0100

The priorities assessment for the planned construction of high-speed rail/HSR in the Czech Republic in terms of impacts on internal and external integration is a though-provoking topic not only from the technical and economic, but also from the social and geographical point of view. Its primary basis is the application of the gravity model, according to which the planned route C Prague-Wien has the most significant potential in passenger transport. Then following routes are A Prague-Berlin, B Prague-München, and D Brno-Katowice. Subsequently, the likely impacts generated by a significant improvement in the quality parameters and hence the competitive position of rail transport were assessed, including the potential for shifting part of the demand from the road and air transport to HSR. Overall, however, it can be stated that the potential impacts of the HSR on the growth of passenger transport in the Czech Republic will not be essential. To perceive the regional impacts of HSR construction, analyses of selected indicators (population density per km"jats:sup"2"/jats:sup", GDP per capita, unemployment rate) by NUTS 3 regions for the period 2007-2017 were also carried out. From the strategic point of view, the research results did not confirm that the planned construction of the HSR primarily stimulates convergence tendencies in regional development as the main priority of EU regional policy. Rather, it seems more likely that the HSR will stimulate the extraction of economic activity from “rural” regions in favour of metropolitan regions.

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Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model

Scipedia content — Mon, 15 Feb 2021 12:03:22 +0100

The economic and health impacts resulting from the greenhouse effect is a major concern in many countries. The transportation sector is one of the major contributors to greenhouse gas (GHG) emissions worldwide. Almost 15 percent of the global GHG and over 20 percent of energy-related CO"sub"2"/sub" emissions are produced by the transportation sector. Quantifying GHG emissions from the road transport sector assists in assessing the existing vehicles’ energy consumptions and in proposing technological interventions for enhancing vehicle efficiency and reducing energy-supply greenhouse gas intensity. This paper aims to develop a model for the projection of GHG emissions from the road transport sector. We consider the Vehicle-Kilometre by Mode (VKM) to Number of Transportation Vehicles (NTV) ratio for the six different modes of transportation. These modes include motorcycles, passenger cars, tractors, single-unit trucks, buses and light trucks data from the North American Transportation Statistics (NATS) online database over a period of 22 years. We use multivariate regression and double exponential approaches to model the projection of GHG emissions. The results indicate that the VKM to NTV ratio for the different transportation modes has a significant effect on GHG emissions, with the coefficient of determination adjusted R"sup"2"/sup" and R"sup"2"/sup" values of 89.46% and 91.8%, respectively. This shows that VKM and NTV are the main factors influencing GHG emission growth. The developed model is used to examine various scenarios for introducing plug-in hybrid electric vehicles and battery electric vehicles in the future. If there will be a switch to battery electric vehicles, a 62.2 % reduction in CO"sub"2"/sub" emissions would occur. The results of this paper will be useful in developing appropriate planning, policies, and strategies to reduce GHG emissions from the road transport sector.

Document type: Article

Sociodemography, Geography, and Personality as Determinants of Car Driving and Use of Public Transportation

Scipedia content — Mon, 15 Feb 2021 12:03:03 +0100

To address the sustainability challenges related to travel behavior, technological innovations will not be enough. Behavioral changes are also called for. The aim of the present study is to examine the influence of sociodemography, geography, and personality on car driving and use of public transportation. Sociodemographic factors have been defined by age, gender, income, and education. Geographic factors have been studied through residential area (e.g., rural and urban areas). Personality has been studied through the Five-Factor-Model of personality&mdash

degree of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The analysis is based on a survey with 1812 respondents, representative for the Swedish population. Regarding sociodemographic factors, car driving is explained by being male, higher age, higher income, while use of public transportation is explained by lower age and higher education. The user profile of a car driver is the opposite to that of a public transport passenger when it comes to geographic factors

urban residential area explains public transportation while rural area explains car driving. Some personality factors are also opposites

a low degree of Openness and a high degree of Extraversion explain car driving, while a high degree of Openness and a low degree of Extraversion explain use of public transportation. Moreover, car driving is explained by a low degree of Neuroticism, while use of public transportation is explained by a low degree of Conscientiousness and a high degree of Agreeableness. Since sociodemography, geography, and personality influence how people process information and evaluate market propositions (e.g., products and services), the findings presented here are useful for policymakers and transportations planners who would like to change behavior from car driving to public transportation use. Caution should be taken in interpreting the relationship between personality traits and transportation modes, since the personality traits are measured by a short scale (i.e., Big Five Inventory (BFI)-10), with limitations in the factor structure for a representative sample of the Swedish population.

Document type: Article

Dynamic Model for Scheduling Crew Shifts

Scipedia content — Mon, 15 Feb 2021 12:02:29 +0100

In regular as well as nonscheduled air transport, extraordinary situations occasionally occur, which may fundamentally disrupt the flight schedule. Fundamental disruptions of flight schedules affect not only passengers but also the airline. One of the areas that are negatively affected by the disruption is the crew plan. Due to extraordinary events, it happens that a flight is delayed, and the crew will not be at the destination airport at the prescribed time and the airline will not be able to assign it on further flights according to the original plan. Such situations can be resolved either by deploying any other available crew or by delaying the flight appropriately until the previously planned crew is available. Assigning a new crew entails additional costs for the airline, as it has to assign more flight staff than had been originally planned. Furthermore, delayed flights lead to paying passengers financial compensation, incurring additional costs for airlines. Therefore, it is important that the airline is able to resolve any irregularity situations so that the additional costs incurred to deal with the irregularity situations are kept at a minimum. The paper presents one possible approach, a mathematical model that can be used to solve such a situation. The presented mathematical model may be the basis for the decision support system of the operations center worker who is responsible for the operational management of flight crews. The model will primarily aim at smaller airlines that cannot afford expensive software and often rely on manual solutions. However, a manual solution may not always be the best, as the operator, who plans the processes, may not consider all the constraints. Another important factor that makes the decision processes more difficult is that it is usually necessary to decide in a short period of time. The solution proposed in this paper will allow the operator to make a quick decision that will also be the most advantageous for the airline. This is because the proposed method is an exact approach, which guarantees finding the optimum solution. In this article, we are only dealing with pilot crews. Web of Science 2020 art. no. 5372567

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Effect of Concentric Annular Gap Flow on Wall Shear Stress of Stationary Cylinder Pipe Vehicle under Different Reynolds Numbers

Scipedia content — Mon, 15 Feb 2021 12:02:11 +0100

The tube-contained raw material pipeline hydraulic transportation technology is an optimization and improvement of traditional hydraulic capsule pipeline (HCP) transport. It has the advantages of lower resource consumption, environmental protection, and less demand for human resources and has the ability to directly transport solids, liquids, and gases. The cylinder pipe vehicle is the core component of tube-contained raw material pipeline hydraulic transportation; its motion characteristics and energy consumption are affected by wall shear stress. When the cylinder pipe vehicle is stationary, the annular gap flow will affect the wall shear stress. This paper studies the wall shear stress and annular flow field distribution of a stationary cylinder pipe vehicle under different Reynolds numbers. The results show that as the Reynolds number increases, both the wall shear stress and the annular gap flow velocity show a gradually increasing trend. The wall shear stress and the velocity of the annular gap flow show some correlation, but there are differences in the trend of axial and circumferential wall shear stress along the length of the cylinder pipe vehicle. The research in this article will further improve the theoretical system of hydraulic conveyance of barrel-loading pipelines and provide a theoretical basis for the realization of industrial applications as soon as possible.

Document type: Article

Macroscopic Traffic Flow Characterization at Bottlenecks

Scipedia content — Mon, 15 Feb 2021 12:00:37 +0100

Traffic congestion is a significant issue in urban areas. Realistic traffic flow models are crucial for understanding and mitigating congestion. Congestion occurs at bottlenecks where large changes in density occur. In this paper, a traffic flow model is proposed which characterizes traffic at the egress and ingress to bottlenecks. This model is based on driver response which includes driver reaction and traffic stimuli. Driver reaction is based on time headway and driver behavior which can be classified as sluggish, typical or aggressive. Traffic stimuli are affected by the transition width and changes in the equilibrium velocity distribution. The explicit upwind difference scheme is used to evaluate the Lighthill, Whitham, and Richards (LWR) and proposed models with a continuous injection of traffic into the system. A stability analysis of these models is given and both are evaluated over a road of length 10 km which has a bottleneck. The results obtained show that the behavior with the proposed model is more realistic than with the LWR model. This is because the LWR model cannot adequately characterize driver behavior during changes in traffic flow.

Document type: Article

Effects of Vehicle Restriction Policies on Urban Travel Demand Change from a Built Environment Perspective

Scipedia content — Mon, 15 Feb 2021 11:58:11 +0100

License plate restriction (LPR) policy presents the most straightforward way to reduce road traffic and emissions worldwide. However, in practice, it has aroused great controversy. This policy broke the original structure of the urban transportation mode, which needed some matching strategies to adapt to this change. Investigating this travel demand change is a challenging task because it is greatly influenced by features of the local built environment. Fourteen variables from four dimensions, location, land-use diversity, distance to transit, and street design, are used to depict the built environment; moreover, the severe collinearity underlies these feature variables. To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. Travel demand changes are assessed by the relative variation in taxi ridership in each traffic analysis zone based on the taxi GPS data. Built environment variables are measured using the transportation network data and the Baidu Map Service points of interest (POI) data. The results show that regions with a higher level of public transportation service and a higher degree of the land mix have a stronger resilience to the vehicle restriction policy. Besides, the contribution rate of public transportation is stable as a whole, while the contribution rate of richness depends on specific types of land use. The conclusions in this study can provide in-depth insights into the influence of the LPR policy and underpin traffic complementary policies to ensure the effectiveness of LPR.

Document type: Article

MPP: A Novel Algorithm for Estimating Vehicle Space Headways from a Single Image

Scipedia content — Mon, 15 Feb 2021 11:57:45 +0100

Vehicle space headway, also called spacing, is an important and basic traffic parameter. Traditional space headway calculation methods are facing the problems of large errors and high costs. This paper presents a novel algorithm based on measurement point pairs (MPPs) to estimate the real-time microcosmic vehicle space headway from single images in existing traffic surveillance videos and images without any additional equipment. First, the camera is calibrated with road markings to obtain the relationship between the image coordinates and the world coordinates. Second, vehicle pairs of two successive vehicles in the image are established, measurement points on each vehicle are selected by video intelligence analysis technologies, and their world coordinates are calculated by camera calibration results. Finally, the measurement points of the preceding and following vehicles are matched to obtain the MPPs, followed by the calculation of the weighted space headway. By using the measurement point information, one of the most difficult problems in image distance measurement, the lack of height information, is solved. The main factors causing estimation errors are fully addressed and the range and trend of errors under certain conditions are given by virtual simulation. Two real-world experiments are used to prove the accuracy and usability of the MPP in common video scenes: the simulation experiment indicates that the MPP algorithm achieves a high accuracy with estimation error less than ±0.1 m and the relative error within 1.1%; the application experiment shows that the MPP-based calculation is more accurate and stable than the state-of-the-art distance measurement algorithm and that the convenience of the proposed MPP algorithm is higher than that of traditional methods of space headway estimation.

Document type: Article

A New Production-Splitting Method for the Multi-Well-Monitor System

Scipedia content — Mon, 15 Feb 2021 11:56:29 +0100

In order to reduce the cost of wellheads, the production rate of the gas wells in the Hechuan Gas Field are mostly measured in groups, which raises a stringent barrier for industries to determine the production rate of each single well. The technique for determining the production of a single well from the production of the well-group can be called the production splitting method (PSM). In this work, we proposed a novel PSM for the multi-well-monitor system (MWMS) on the basis of the Beggs and Brill (BB) correlation. This proposed method can account for the multi-phase flow together with the features of the pipelines. Specifically, we discretize the pipeline into small segments and recognize the flow pattern in each segment. The pressure drop along the pipeline is calculated with the Beggs and Brill correlation, and the production of each well is subsequently determined with a trial method. We also applied this proposed method to a field case, and the calculated results show that the results from this work undergo an excellent agreement with the field data.

Document type: Article

Performance evaluation of lossy quality compression algorithms for RNA-seq data

Scipedia content — Mon, 15 Feb 2021 11:33:37 +0100

Background Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. Results In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. Conclusions Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results.

Document type: Article

Effect of High-Altitude Environment on Driving Safety: A Study on Drivers’ Mental Workload, Situation Awareness, and Driving Behaviour

Scipedia content — Mon, 15 Feb 2021 11:33:21 +0100

This study aims to analyze the effect of high-altitude environment on drivers’ mental workload (MW), situation awareness (SA), and driving behaviour (DB), and to explore the relationship among those driving performances. Based on a survey, the data of 356 lowlanders engaging in driving activities at Tibetan Plateau (high-altitude group) and 341 lowlanders engaging in driving activities at low altitudes (low-altitude group) were compared and analyzed. The results suggest that the differences between the two groups are noteworthy. Mental workload of high-altitude group is significantly higher than that of low-altitude group, and their situation awareness is lower significantly. The possibility of risky driving behaviours for high-altitude group, especially aggressive violations, is higher. For the high-altitude group, the increase of mental workload can lead to an increase on aggressive violations, and the situation understanding plays a full mediating effect between mental workload and aggressive violations. Measures aiming at the improvement of situation awareness and the reduction of mental workload can effectively reduce the driving risk from high-altitude environment for lowlanders.

Document type: Article

The Dynamical Decision Model of Intersection Congestion Based on Risk Identification

Scipedia content — Mon, 15 Feb 2021 11:32:31 +0100

The paper focuses on the problem of traffic congestion at intersection based on the mechanism of risk identification. The main goal of this study is to explore a new methodology for identifying and predicting the intersection congestion. Considering all the factors influencing the traffic status of intersection congestion, an integrated evaluation index system is constructed. Then, a detailed dynamic decision model is proposed for identifying the risk degree of the traffic congestion and predicting its influence on future traffic flow, which combines the traffic flow intrinsic properties with the basic model of the Risking Dynamic Multi-Attribute Decision-Making theory. A case study based on a real-world road network in Baoji, China, is implemented to test the efficiency and applicability of the proposed modeling. The evaluation result is in accord with the actual condition and shows that the approach proposed can determine the likelihood and risk degree of the traffic congestion occurring in the intersection, which can be used as a tool to help transport managers make some traffic control measures in advance.

Document type: Article

A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems

Scipedia content — Mon, 15 Feb 2021 11:32:08 +0100

A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution.

Document type: Article

Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

Scipedia content — Mon, 15 Feb 2021 11:31:04 +0100

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

Document type: Article

Assessing Sustainable Mobility Measures Applying Multicriteria Decision Making Methods

Scipedia content — Mon, 15 Feb 2021 11:30:45 +0100

n increasing number of recent discussions have focused on the need for designing transport systems in consonance with the importance of the environment, thus promoting investment in the growth of non-motorized transport infrastructure. Under such conditions, the demand for implementing the most effective infrastructure measures has a profoundly positive impact, and requires the least possible financial and human resources. The development of the concept of sustainable mobility puts emphasis on the integrated planning of transport systems, and pays major attention to the expansion of non-motorized and public transport, and different sharing systems, as well as to effective traffic management involving intelligent transport systems. The development of transport infrastructure requires massive investment, and hence the proper use of mobility measures is one of the most important objectives for the rational planning of sustainable transport systems. To achieve this established goal, this article examines a compiled set of mobility measures and identifies the significance of the preferred tools, which involve sustainable mobility experts. The paper also applies multicriteria decision making methods in assessing urban transport systems and their potential in terms of sustainable mobility. Multicriteria decision making methods have been successfully used for assessing the effectiveness of sustainable transport systems, and for comparing them between cities. The proposed universal evaluation model is applied to similar types of cities. The article explores the adaptability of the model by assessing big Lithuanian cities.

This article belongs to the Special Issue SUMP for Cities’ Sustainable Development

This research was funded by Vilnius Gediminas Technical University

Document type: Article

Nowcast models for train delays based on the railway network status

Scipedia content — Mon, 15 Feb 2021 11:29:42 +0100

Switches and crossings (S&C) or turnouts are one of the important systems in the Swedish railway traffic maintenance planning. For immediate diverting of the trains, they need to be predict the working condition for short time duration, also known as nowcasting and for long time duration, also known as forecasting. The prediction of the condition of turnout is useful for traffic planning without disrupting to the traffic. Hence, the main purpose of this paper is to predict the condition of S&Cs for shorter and longer duration. In order to achieve it, at first, statistical analysis is carried out to find the root causes of failures. Secondly, non-homogenous Poisson process is applied to nowcast and forecast the working condition. The results of this study will guide the train dispatchers to plan the train timetable according the present traffic.

Document type: Article

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Scipedia content — Mon, 15 Feb 2021 11:29:10 +0100

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a "jats:italic"k"/jats:italic"-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.

Document type: Article

Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Scipedia content — Mon, 15 Feb 2021 11:28:49 +0100

In order to improve the driver’s physiological and psychological state, the driver’s mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. The experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver’s spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver’s spatiotemporal data are analyzed to judge the changing trend of the driver’s workload in the tunnel environment. The results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver’s typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. The tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design.

Document type: Article

Efficiency Evaluation of Bus Transport Operations Given Exogenous Environmental Factors

Scipedia content — Mon, 15 Feb 2021 11:28:31 +0100

As a mode of green transport that can effectively alleviate urban traffic congestion and improve air quality, bus transport is highly subsidized by governments at all levels in China. Thus, measuring efficiency in the bus transport sector is particularly important. However, few reports in the literature have taken exogenous environmental factors into consideration to evaluate public transport operation efficiency. This may lead to inaccurate evaluation results. This study employs the three-stage DEA model, which can eliminate the impacts of exogenous environmental factors on public bus transport operation to gain real efficiency results. Meanwhile, to further explore how exogenous environmental factors affect bus transport operations, a tobit model is used to analyse the results. The main results of this paper reveal the following: first, exogenous environmental factors have a significant impact on the operational efficiency of bus transport. It is reasonable and necessary to select the three-stage method to eliminate environmental factors for real bus operation efficiency. Second, the fluctuations of the bus transport efficiency of 30 cities decreased during 2010–2016. The western region has the highest operation efficiency, followed by the eastern and the middle regions. Third, the economic, taxi transport, and urban rail transport have a marked impact on the operational efficiency of bus transport. This paper confirms the important influence of exogenous environmental factors on the efficiency of public transport operations. In addition, this article could help improve the efficiency of urban public transport operations and promote the attractiveness of urban public transport and the amount of green travel.

Document type: Article

An Application of Cluster Analysis Method to Determine Vietnam Airlines’ Ground Handling Service Quality Benchmarks

Scipedia content — Mon, 15 Feb 2021 11:27:45 +0100

This paper recommends that Vietnam Airlines use a pro-offered model to both evaluate and improve its current service network being operated at international airports. The model includes cluster analysis, ANOVA, and Scheffé post hoc to provide service performance insights and to serve as a complementary corporate benchmark for evaluating service potential and for identifying deficient service areas. By means of this model, the managerial board can designate a potent strategy for ground handling service. Additionally, the given model provides expatriate station managers with a clearer viewpoint of the localized productivity level as performed in relation to other airports concomitant within their own clusters.

Document type: Article

Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach

Scipedia content — Mon, 15 Feb 2021 11:07:40 +0100

Electric vehicles (EVs) presence in the power grid can bring about pivotal concerns regarding their energy requirements. EVs charging behaviors can be affected by several aspects including socio-economics, psychological, seasonal among others. This work proposes a case study to analyze seasonal effects on charging patterns, using a public real-world based dataset that contains information from the aggregated load of the total charging stations of Boulder, Colorado. Our approach targets to forecast and recognize EVs demand considering seasonal factors. Principal component analysis (PCA) was used to provide a visual representation of the variables and their contribution and the correlation among them. Then, twelve classification models were trained and tested to discriminate among seasons the charging load of electric vehicles. Later, a benchmark stage is presented for regression as well as for classification results. For regression models, examined through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the random Forest provides better prediction than quasi-Poisson model widely. However, it was observed that for large variations in electric vehicles’ charging load, quasi-Poisson fits better than random forest. For the classification models, evaluated through Accuracy and the Area under the Curve, the Lasso and elastic-net regularized generalized linear (GLMNET) model provided the best global performance with accuracy up to 100% when evaluated on the test dataset. The results of this work offer great insights for enhancing demand response strategies that involve PEV charging regarding charging habits across seasons.

Document type: Article

A Correlative Analysis of Modern Logistics Industry to Developing Economy Using the VAR Model: A Case of Pakistan

Scipedia content — Mon, 15 Feb 2021 11:07:22 +0100

The modern logistics industry has opened new strategic perspectives in establishing its interrelation with economic growth. In recent years, understanding such an overlap has become a policy issue considering ever-increasing factors and their influence on this relation. Most existing studies have explored this interaction from a general perspective, or for developed countries. This paper explores time-series analysis of the dynamic variables and their inter-related influence in both the short and long run on the relationship between modern logistic industry and economic growth—a more specific perspective, particularly for developing countries. Accordingly, we exemplify our analysis by employing the vector autoregression (VAR) model to the most updated time series data of investment in the logistics industry and the economic growth of Pakistan from 1990 to 2018. The empirical findings endorse the previous studies’ outcomes and recognize the importance of sustainable economic development concerning continuously improving the logistics industry. However, a unidirectional relation is observed that economic growth leads to developing the logistics industry—economic growth exerts a significant demand-pull effect on Pakistan’s logistics. It implies that logistic industrial development is comparatively quicker in the geographical areas where economic growth is higher than those areas where economic growth is low. To conclude this study’s findings, logistics industry reforms should prioritize the selected geographical areas in improving the economy that would lead to the modern logistics industry’s development. As the model adopts Pakistan’s context, the overall statistical analysis can be generalized to other developing economies. These results would be of particular interest to strategy makers working in developing countries and help them design and develop modern transportation and logistics, coupled with interlinked technological factors, which would attract investment in the logistics industry for sustainable economic development.

Document type: Article

Research on the Electromagnetic-Heat-Flow Coupled Modeling and Analysis for In-Wheel Motor

Scipedia content — Mon, 15 Feb 2021 11:05:40 +0100

In this paper, a 15 KW in-wheel motor (IWM) is taken as the research object, and the coupling factors among the electromagnetic field, temperature field and flow field are analyzed, and the strong and weak coupling factors between the three fields are clarified, and by identifying the strong and weak coupling factors between the three fields, a three-field coupling analysis model for IWM with appropriate complexity is established, and the validity of the model is verified. In a certain driving condition, the electromagnetic field, temperature field and flow field characteristics of IWM are analyzed with the multi-field coupling model. The result shows that, after the IWM runs 8440 s under driving conditions, in this paper, the IWM electromagnetic torque of the rated working condition is 134.2 Nm, and IWM the electromagnetic torque of the peak working condition is 451.36 Nm, and the power requirement of the motor can be guaranteed. The highest temperature of the IWM is 150 °

C, which does not exceed the insulation grade requirements of the motor (155 °

C), the highest temperature of the permanent magnet (PM) is 65.6 °

C, and it does not exceed the highest operating temperature of the PM, and ensures the accurate calculation of components loss and the temperature of the motor. It can be found, through research, that the electromagnetic torque difference between unidirectional coupling and bidirectional coupling is 3.2%, the maximum temperature difference is 7.98% in the three-field coupling analysis of IWM under rated working conditions. Therefore, it is necessary to consider the influence of coupling factors on the properties of motor materials when analyzing the electromagnetic field, temperature field and flow field of IWM

it also provides some reference value for the simulation analysis of IWM in the future.

Document type: Article

Understanding how city networks are leveraging climate action: experimentation through C40

Scipedia content — Mon, 15 Feb 2021 11:05:02 +0100

"jats:p"Climate change is one of the most challenging environmental and social problems for contemporary urban planning. In response to this phenomenon, city networks have emerged as new configurations of urban climate governance that encourage the implementation of experiments such as testing new solutions regarding sustainable transport. While city networks are gaining momentum and influence as effective platforms to transform and scale up pilot experiments into city-wide schemes, little is known regarding their role in conditioning and leveraging such urban experiments Our paper investigates the underexplored nature of urban experiments within city networks and provides a better understanding of how these networks condition urban experiments. To this end an analytical model has been developed and applied to the case of the C40 Climate Leadership Group (C40) and its "jats:italic"Climate Positive Development Good Practice Guide."/jats:italic" Our findings suggest that the C40 encourages variation in local climate experiments and the generation of new and innovative climate solutions in member cities. In particular they reveal that the implementation of climate positive experiments has passed the ‘variation’ stage, is currently in the ‘selection’ stage, and likely to move towards the ‘retention’ stage in the near future. Potential experimentation outputs of the case are identified as built environment change, new citizen practices, policy change, infrastructural change and new technology. Noticeably, we consider that the C40 plays an important role in providing fundamental institutional support to implement and leverage climate projects within its member cities.

Document type: Article

Defining Highway Node Acceptance Capacity (HNAC): Theoretical Analysis and Data Simulation

Scipedia content — Mon, 15 Feb 2021 11:04:39 +0100

A new concept of Highway Node Acceptance Capacity (HNAC) is proposed in this paper inspired by a field data observation. To understand HNAC in microscopic view, boundary condition of successful merging is found using car-following behaviours and lane-changing rules, which could also explain traffic oscillations. In macroscopic view, linear positive relationship between HNAC and background traffic volume is obtained based on moving bottleneck. To determine the explicit form of the relationship, data simulation considering car-following behaviours and traffic flow theory is used. In the results, the synchronization phenomenon of oscillation in on-ramp (with respect to main road) and intersected road is found. The explicit equation of HNAC is determined based on standard deviation and correlation coefficient analysis, and also proved to be accurate with model validation, which is helpful in studies related to propagation mechanism of traffic emergencies on highway network.

Document type: Article

Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity

Scipedia content — Mon, 15 Feb 2021 11:02:29 +0100

Stopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping time confirms the existence of group specific effects on drivers. However, the AFT model is developed based on the homogeneity assumption. To overcome this drawback, shared frailty survival models are developed for stopping time analysis, which consider the group heterogeneity of drivers. The results show that log-logistic based frailty model with age as a grouping variable has the best goodness of fit and prediction accuracy. Analysis of the models’ parameters indicates that phone status, maximum deceleration, vehicles’ speed, and the distance to stopping line at the onset of the yellow signal have significant impacts on stopping time. Additionally, heterogeneity analysis illustrates that young, middle-aged, and female drivers are more likely to brake harshly and stop past the stop line, which may block the intersection. Furthermore, drivers, who are more familiar with traffic environments, are more possible to make reasonable stopping decisions approaching intersections. The results can be utilized by traffic authorities to implement road safety strategies, which will help reduce traffic incidents caused by improper stopping behavior at intersections.

Document type: Article

Research on Consumers’ Preferences for the Self-Service Mode of Express Cabinets in Stations Based on the Subway Distribution to Promote Sustainability

Scipedia content — Mon, 15 Feb 2021 11:00:56 +0100

With the explosive growth in the express delivery business, last-mile delivery issues have come to the forefront in China. Subway-based distribution has been demonstrated and practiced. The self-service mode of express cabinets in stations based on the subway distribution can effectively reduce the last-mile delivery costs, increase the utilization rate of public transportation resources, and reduce traffic congestion and carbon emissions. This paper designed self–service mode of express cabinets in stations and discussed the feasibility by investigating consumers’ preferences. The consumers’ preferences and influencing factors were examined by using the multicategorical logit model. The results show that consumers’ gender, education level and number of online purchases per month have an impact on consumers’ preferences. The majority of consumers are willing to actively engage in green consumer behavior. Meanwhile, consumers are more concerned about whether the express mode is convenient to conduct and the queuing of an express cabinet. Some suggestions and recommendations on promoting this self-service mode were put forward, such as pushing different advertisements for different groups of consumers, designing efficient and multi-function express cabinets, and adopting a reward system. This research provides guidance for decision making regarding the promotion of a new self–service mode based on the subway distribution, which can promote sustainable consumption and improve the efficient operation of urban last-mile delivery and the low-carbon development of urban transportation.

Document type: Article

The influence of economic barriers and drivers on energy efficiency investments in maritime shipping, from the perspective of the principal-agent problem

Scipedia content — Mon, 15 Feb 2021 10:56:54 +0100

[Abstract:] Maritime transport stands out as a strategic sector; the increasing trend in maritime traffic makes it essential to reduce energy consumption and emissions through investment in energy efficiency. However, investments can be hindered by barriers, and drivers are necessary to reduce or overcome them and promote investment. Consequently, the purpose of this study is to analyze what factors influence investment decisions—and how they do so—when there are principal-agent problems in the shipowner–charterer relationship. The methodology is based on the following process: model and hypotheses formulation, variable definition, the creation of a study sample and statistical treatment through a descriptive analysis of variables and a binomial logistic regression model, all based on a state-of-the-art application. The results corroborate the hypotheses and indicate that principal-agent problems and split incentives, especially in time charter contracts, and a lack of verified information make the shipowners less likely to invest. Moreover, energy efficiency measures are less likely to be implemented in older vessels, possibly due to the difficulty associated with recovering the investment; they are more likely in larger and newer vessels, and regulation encourage their adoption. Furthermore, investment is more likely in vessels with verified information and high levels of both activity and harmful emissions. Improved knowledge in this field could help businesses and governments to act in a more sustainable manner, without detriment to an innovative and competitive sector. Consellería de Cultura, Educación e Ordenación Universitaria; ED481A-2015/224 Ministerio de Ciencia, Innovación y Universidades; RTI2018-100702-B-I00

Document type: Article

Quantifying the Impact of Rainfall on Taxi Hailing and Operation

Scipedia content — Mon, 15 Feb 2021 10:55:50 +0100

Adverse weathers are well-known to impact the operation of transportation systems, including taxis. This paper utilizes taxi GPS waypoint data to investigate the quantitative impact of rainfall on taxi hailing and taxi operations to help improve service quality on rainy days. Through statistical analysis, the study proves that it is more difficult to hail taxis on rainy days, especially during morning peak hours. By modelling the difference value of factors for rainfall and nonrainfall conditions in a multivariate regression model and attaining the significance and elasticity of each factor, passenger demand, taxi supply, search time and velocity are proved to be the significant factors that lower the taxis’ level of service on rainy days. Among them, the number of passengers and taxis are two factors that have the greatest impact. It is also shown that there is no significant difference in the total taxi supply and passenger demand between rainfall and nonrainfall conditions, but a dramatic change in the spatial distribution is discovered. The results suggest that instead of simply providing more taxis on rainy days, optimally dispatching taxicabs to high demand regions can be a more effective solution.

Document type: Article

Energy-Efficient Direct Yaw Moment Control for In-Wheel Motor Electric Vehicles Utilising Motor Efficiency Maps

Scipedia content — Mon, 15 Feb 2021 10:54:43 +0100

An active energy-efficient direct yaw moment control (DYC) for in-wheel motor electric vehicles taking motor efficiency maps into consideration is proposed in this paper. The potential contribution of DYC to energy saving during quasi-steady-state cornering is analysed. The study in this paper has produced promising results which show that DYC can be used to reduce the power consumption while satisfying the same cornering demand. A controller structure that includes a driver model and an offline torque distribution law during continuous driving and cornering is developed. For comparison, the power consumption of stability DYC is also analysed. Simulations for double lane change manoeuvres are performed and driving conditions either with a constant velocity or with longitudinal acceleration are designed to verify the effectiveness of the proposed controller in different driving situations. Under constant velocity cornering, since the total torque demand is not high, two rear wheels are engaged and during cornering it is beneficial to distribute more torque to one wheel to improve energy efficiency. In the simulated driving manoeuvres, up to 10% energy can be saved compared to other control methods. During acceleration in cornering, since the total torque demand is high, it is energy-efficient to use all the four in-wheel motors during cornering.

Document type: Article

Passenger Volume Prediction by a Combined Input-Output and Distributed Lag Model and Data Analytics of Industrial Investment

Scipedia content — Mon, 15 Feb 2021 10:51:56 +0100

In order to sketch the transport infrastructure construction in an economy or a region, the government has to predict the passenger volume, under the local policy of industrial investment. In this paper, we propose a combined input-output and distributed lag prediction model of passenger volume in a province in P. R. China, under a certain policy of industrial investment called Silk Road Economic Belt. Specifically, the relationships between the passenger volume, GDP (gross domestic product), gross output, and transportation consumption are analyzed, and then the industrial development speed analysis and classification are used to calculate the average development speeds and the GDP contributions of 42 industries. Combining the input-output table, the provincial transportation consumption under the Silk Road Economic Belt policy is predicted, and the passenger volumes of the cities and the province in the future are predicted by the distributed lag models. Considering the uncertainty of the investment, the elastic ranges of the cities and the province’s passenger volumes are determined. The results show that the correlation between the passenger volume and transportation consumption is the highest, and it is equal to 0.975. In 2020, the passenger volume in Shaanxi is 1,641,305 thousands, and the error between the predicted value and the value obtained by summing the cities’ passenger volumes is smaller than 0.002%.

Document type: Article

Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data

Scipedia content — Mon, 15 Feb 2021 10:50:34 +0100

Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.

Document type: Article

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Scipedia content — Mon, 15 Feb 2021 10:49:19 +0100

In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.

Document type: Article

A computational framework for revealing competitive travel times with low carbon modes based on smartphone data collection

Scipedia content — Mon, 15 Feb 2021 10:48:41 +0100

Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time.. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment’s preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions.

Document type: Article

The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving

Scipedia content — Mon, 15 Feb 2021 10:47:24 +0100

In order to improve the adaptation of driver to the advanced driver assistance system (ADAS) and optimize the active safety control technology of vehicle under man-computer cooperative driving, this paper investigated the correlation between driver’s improper driving behaviors and abnormal vehicle states under the ADAS. Based on the warning data collected from the driver’s assistance warning system equipped on buses, the interaction between improper behaviors, between abnormal vehicle states, and between improper behaviors and abnormal vehicle states were quantitatively analyzed through the hierarchical clustering method and improved Apriori algorithm. The results showed that eye closure and yawn were high in concurrency (probability: 0.888) and interaction (average probability: 0.946); the interaction among lane departure, rapid acceleration, and rapid deceleration are frequent (average probability: 0.7224); eye closure (average probability: 0.452) and yawn (average probability: 0.444) are likely to induce abnormal vehicle states such as rapid acceleration and rapid deceleration. Some suggestions proposed based on the results are as follows. First, it is suggested that the ADAS should combine the warning modes of eye closure and yawn; second, when the driver closes eyes or yawns, the control of the ADAS over the lateral and longitudinal performance of vehicle should be enhanced; third, the extent of control by the ADAS should be determined according to the relationship probability; finally, the lateral control over the vehicle by the ADAS should be strengthened when there is a forward collision warning.

Document type: Article

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

Scipedia content — Mon, 15 Feb 2021 10:45:52 +0100

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

Document type: Article

Internal versus external European air market realities: the competitive divide

Scipedia content — Mon, 15 Feb 2021 10:44:55 +0100

Background This paper looks at how ongoing attempts to improve air market competitiveness in Europe are challenged by the differing internal and external realities that exist. Europe’s internal multilateral single air market has encouraged the proliferation of pan-European airlines unhindered by national borders, which have stimulated increased competition and driven down airfare prices. Meanwhile, externally the bilateral system continues to dominate the wider global airline industry and a number of countries still prefer to negotiate air access with individual European countries. Methods Data from a five stage mixed-method Delphi study underpin the paper. Qualitative data, collected at a first stage brainstorming workshop and during final stage in-depth interviews, were thematically analysed to locate key and sub-themes. Quantitative survey data were collected across the remaining stages and were statistically analysed with mostly t-tests and chi-square tests of association to a 95% confidence level. Results The key theme transferability of the European regional single air market emerged from the study data; supported by the three sub-themes EU regional model, extraterritoriality and North Atlantic single air market. Conclusions Europe remains the multilateral exception to the general rule in international aviation that bilateralism is the norm. Despite efforts to address this competitive divide, aeropowers like China and Russia are reluctant to embrace extensive change, while major European flag carriers resist unfettered competition from outside the bloc.

Document type: Article

Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment

Scipedia content — Mon, 15 Feb 2021 10:44:03 +0100

The approaches monitoring fatigue driving are studied because of the fact that traffic accidents caused by fatigue driving often have fatal consequences. This paper proposes a new approach to predict driving fatigue using location data of commercial dangerous goods truck (CDT) and driver’s yawn data. The proposed location data are from an existing dataset of a transportation company that was collected from 166 vehicles and drivers in an actual driving environment. Six different categories of the predictor set are considered as fatigue-related indexes including travel time, day of week, road type, continuous driving time, average velocity, and overall mileage. The driver’s yawn data are used as a proxy for ground truth for the classification algorithm. From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). The results show that the random forest is more suitable for predicting fatigue driving using location data according to its best accuracy (74.18%), F1-measure (62.02%), and AUROC (0.8059). Finally, we analyze the relationship between fatigue driving and driving environment according to variable importance described by random forest. In summary, our results obviously exhibit the potential of location data for reducing the accident rate caused by fatigue driving in practice.

Document type: Article

Driving Simulator Validity of Driving Behavior in Work Zones

Scipedia content — Mon, 15 Feb 2021 10:40:57 +0100

Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

Document type: Article

Moving a Taxi Sector to Become Electric: Characterizing Taxi Drivers Interested in Purchasing a Full Electric Vehicle

Scipedia content — Mon, 15 Feb 2021 10:39:59 +0100

Electrification of mobility exceeds personal transport to increasingly focus on particular segments such as city logistics and taxis. These commercial mobility segments have different motives to purchase a full electric vehicle and require a particular approach to incentivize and facilitate the transition towards electric mobility. A case where a municipality was successful in stimulating the transition to electric mobility is the taxi sector in the city of Amsterdam. Using results from a survey study (n = 300), this paper analyses the differences in characteristics between taxi drivers that either have or do not have interest in purchasing a full electric taxi vehicle. Results show a low intention across the sample to adopt a full electric vehicle and no statistically significant differences in demographics between the two groups. Differences were found between the level of acceptability of the covenant, the rated attractiveness of the incentives, the ratings of full electric vehicle attributes and the consultation of objective and social information sources. These results can be used by policy makers to develop new incentives that target specific topics currently influencing the interest in a full electric taxi vehicle.

Document type: Article

Optimization and Simulation of Carsharing under the Internet of Things

Scipedia content — Mon, 15 Feb 2021 10:39:30 +0100

Internet of Things devices are popular in civilian and military applications, including smart device cities, smart grids, smart pipelines, and medical Internet of Things. Among them, carsharing supported by the Internet of Things is developing rapidly due to their advantages in environmental protection and reducing traffic congestion. The optimization of the carsharing system needs to consider the uncertainty of demand and the coupling relationship of multiple decision variables, which brings difficulties to the establishment of mathematical models and the design of efficient algorithms. Existing studies about carsharing optimization are mainly divided into four subproblems: the operation mode selection, vehicle type selection, demand analysis, or decision-making, rather than comprehensive consideration. This paper summarizes the four subproblems from the perspective of mathematical models, solving algorithms, and statistical methods and provides references for more comprehensive research in the future.

Document type: Article

Simulation-Based Research on Driver Visibility of Black-and-White Striped Vehicles

Scipedia content — Mon, 15 Feb 2021 10:37:08 +0100

The vehicle color is considered to be a significant factor affecting driver visibility. The primary objective of this study is therefore to determine the impact of black-and-white striped vehicles (BWVs) on driver visibility through simulation-based experiments. In these experiments, subjects were asked to perform front and rear target identification tasks under daylight and twilight conditions. Then, a 2 (lighting conditions) × 2 (vehicle size) × 5 (vehicle color) analysis of variance was conducted for each task. Under the front identification scenario, the main factors affecting visibility were found to be lighting conditions, vehicle size, vehicle color, and the interactions between these factors. Under the rear identification scenario, lighting conditions and vehicle color were found to be the main factors. The results of this study demonstrate that driver visibility of BWVs is poorer than that of other colors of vehicles and that BWV visibility is susceptible to lighting conditions.

Document type: Article

Understanding Electric Bikers’ Red-Light Running Behavior: Predictive Utility of Theory of Planned Behavior vs Prototype Willingness Model

Scipedia content — Mon, 15 Feb 2021 10:36:49 +0100

To date, electric bikers’ (e-bikers’) red-light running (RLR) behavior is often viewed as one of the main contributors to e-bike-related accidents, especially for traffic scenarios with high e-bike ridership. In this paper, we aim to understand e-bikers’ RLR behavior based on structural equation modeling. Specifically, the predictive utility of the theory of planned behavior (TPB), prototype willingness model (PWM), and their combined form, TPB-PWM model, is used to analyze e-bikers’ RLR behavior, and a comparison analysis is made. The analyses of the three modeling approaches are based on the survey data collected from two online questionnaires, where more than 1,035 participant-completed questionnaires are received. The main findings in this paper are as follows: (i) Both PWM and TPB-PWM models could work better in characterizing e-bikers’ RLR behavior than the TPB model. The former two models explain more than 80% (81.3% and 81.4%, respectively) of the variance in e-bikers’ RLR behavior, which is remarkably higher than that of the TPB model (only 74.3%). (ii) It is also revealed that RLR willingness contributes more on influencing the RLR behavior than RLR intention, which implies that such behavior is dominated by social reactive decision-making rather than the reasoned one. (iii) Among the examined psychological factors, attitude, perceived behavioral control, past behavior, prototype perceptions (favorability and similarity), RLR intention, and RLR willingness were the crucial predictors of e-bikers’ RLR behavior. Our findings also support designing of more effective behavior-change interventions to better target e-bikers’ RLR behavior by considering the influence of the identified psychological factors.

Document type: Article

Assessing the Contribution of Data Mining Methods to Avoid Aircraft Run-Off from the Runway to Increase the Safety and Reduce the Negative Environmental Impacts

Scipedia content — Mon, 15 Feb 2021 10:34:54 +0100

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution &ldquo

Safety Support Tools for Avoiding Runway Excursions&rdquo

. This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection.

Document type: Article

Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data

Scipedia content — Mon, 15 Feb 2021 10:32:50 +0100

Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and "jats:italic"k"/jats:italic"-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on "jats:italic"k"/jats:italic"-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.

Document type: Article

Audio-Based Event Detection at Different SNR Settings Using Two-Dimensional Spectrogram Magnitude Representations

Scipedia content — Mon, 15 Feb 2021 10:32:27 +0100

Audio-based event detection poses a number of different challenges that are not encountered in other fields, such as image detection. Challenges such as ambient noise, low Signal-to-Noise Ratio (SNR) and microphone distance are not yet fully understood. If the multimodal approaches are to become better in a range of fields of interest, audio analysis will have to play an integral part. Event recognition in autonomous vehicles (AVs) is such a field at a nascent stage that can especially leverage solely on audio or can be part of the multimodal approach. In this manuscript, an extensive analysis focused on the comparison of different magnitude representations of the raw audio is presented. The data on which the analysis is carried out is part of the publicly available MIVIA Audio Events dataset. Single channel Short-Time Fourier Transform (STFT), mel-scale and Mel-Frequency Cepstral Coefficients (MFCCs) spectrogram representations are used. Furthermore, aggregation methods of the aforementioned spectrogram representations are examined; the feature concatenation compared to the stacking of features as separate channels. The effect of the SNR on recognition accuracy and the generalization of the proposed methods on datasets that were both seen and not seen during training are studied and reported.

Document type: Article

Valve Location Method for Evaluating Drain Efficiency in Water Transmission Pipelines

Scipedia content — Mon, 15 Feb 2021 10:28:49 +0100

Water transmission pipelines, which transport bulk water into storage facilities, usually have a tree-type configuration with large dimensions; thus, the breakage of a pipeline may cause a catastrophic service interruption to customers. Although drain efficiency is closely related to the number of washout and control valves and their locations, there is no useful guideline. This paper proposes a valve locating method by introducing numerical analyses to enumerate drainage time and zone. A time integration method, combined with the Newton–Raphson algorithm, is suggested to resolve drainage time, while considering the friction loss in gravitational flow. A drain direction matrix, which shows drain direction and coverage, is derived using a network searching algorithm. Furthermore, a feasible practical approach is presented by introducing a critical horizontal slope, a major washout valve, drainage indices, and control valve embedment. The developed method is first applied to simple pipes to validate the drainage time module. Subsequently, the model is expanded to the CY transmission line, which is one of the BR water supply systems in South Korea currently in operation. The results reveal that three drain valve locations have been neglected, and the addition of control valves guarantees consistent drain time below the operational criteria.

Document type: Article

Health and Structural Integrity of Monitoring Systems: The Case Study of Pressurized Pipelines

Scipedia content — Mon, 15 Feb 2021 10:26:46 +0100

In the operation of some structures, particularly in energy or chemical industry where pressurized pipeline systems are employed, certain unexpected critical situations may occur, which must be definitely avoided. Otherwise, such situations would result in undesirable damage to the environment or even the endangerment of human life. For example, the occurrence of such nonstandard states can significantly affect the safety of high-pressure pipeline systems. The following paper discusses basic physical prerequisites for assembling the systems that can sense loading states and monitor the operational safety conditions of pressure piping systems in the long-run. The appropriate monitoring system hardware with cost-effective data management was designed in order to enable the real-time monitoring of operational safety parameters. Furthermore, the paper presents the results obtained from the measurements of existing real-time safety monitoring systems for selected pipeline systems.

Document type: Article

Social efficiency deficit deciphers social dilemmas

Scipedia content — Mon, 15 Feb 2021 10:24:55 +0100

"jats:p"What do corruption, resource overexploitation, climate inaction, vaccine hesitancy, traffic congestion, and even cancer metastasis have in common? All these socioeconomic and sociobiological phenomena are known as social dilemmas because they embody in one form or another a fundamental conflict between immediate self-interest and long-term collective interest. A shortcut to the resolution of social dilemmas has thus far been reserved solely for highly stylised cases reducible to dyadic games (e.g., the Prisoner’s Dilemma), whose nature and outcome coalesce in the concept of dilemma strength. We show that a social efficiency deficit, measuring an actor’s potential gain in utility or fitness by switching from an evolutionary equilibrium to a social optimum, generalises dilemma strength irrespective of the underlying social dilemma’s complexity. We progressively build from the simplicity of dyadic games for which the social efficiency deficit and dilemma strength are mathematical duals, to the complexity of carcinogenesis and a vaccination dilemma for which only the social efficiency deficit is numerically calculable. The results send a clear message to policymakers to enact measures that increase the social efficiency deficit until the strain between what is and what could be incentivises society to switch to a more desirable state.

Document type: Article

Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models

Scipedia content — Mon, 15 Feb 2021 10:23:56 +0100

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Document type: Article

Between fairness, welfare and feasibility: an approach for applying different distributive principles in transport evaluation

Scipedia content — Mon, 15 Feb 2021 10:23:14 +0100

Background For assessing the desirability and feasibility of major transport projects decision makers often recur to ex-ante evaluation methods such as cost-benefit analysis or multi-criteria analysis. In these methods projects are evaluated for their impacts on the welfare of society as one indivisible entity. The use of these methods is limited for assessing socio-spatial equity, as costs and benefits of transport are unequally spread over space and society. Moreover, in projects that cross political borders these methods poorly represent the spatially differentiated interests of the decision makers. Methods This article proposes a novel evaluation approach, applied in a study on the possible demolition of a motorway linking the three Belgian regions of Brussels, Flanders and Wallonia. Results The application demonstrates how the social and spatial differentiation of effects can be evaluated, allowing to differentiate impacts for crucial stakeholders or criteria, but also to aggregate evaluation results in cases where pursuing supra-local or common interests is appropriate. Conclusions Whether and where decision making in transport should follow utilitarian or egalitarian distributive principles depends on context and political considerations. The presented approach allows decision makers to apply both principles where they are deemed appropriate, transparently, in a single project.

Document type: Article

Lane-Change Model and Tracking Control for Autonomous Vehicles on Curved Highway Sections in Rainy Weather

Scipedia content — Mon, 15 Feb 2021 10:18:12 +0100

In this study, we propose an adaptive path planning model and tracking control method for collision avoidance and lane-changing manoeuvres on highways in rainy weather. Considering the human-vehicle-road interaction, we developed an adaptive lane change system that consists of an intelligent trajectory planning and tracking controller. Gaussian distribution was introduced to evaluate the impact of rain on the pavement characteristics and deduce adaptive lane-change trajectories. Subsequently, a score-based decision mechanism and multilevel autonomous driving mode that considers safety, comfort, and efficiency were proposed. A tracking controller was designed using a linearised model predictive control method. Finally, using simulated scenarios, the feasibility and effectiveness of the proposed method were demonstrated. The results obtained herein are a valuable resource that can be used to develop an intelligent lane change system for autonomous vehicles and can help improve highway traffic safety and efficiency in adverse weather conditions.

Document type: Article

Shunting Strategy for Placing-In and Taking-Out Wagons on Branch-Shaped Freight Operation Network with Simulation Method

Scipedia content — Mon, 15 Feb 2021 10:16:54 +0100

Because of China’s vast territory, large population, and huge demand for bulk materials, the railway transportation mode has always received considerable attention. For long-haul transportation, railway transportation can provide a scheduled-based transport plan, all-weather transport service, and cheaper carry instead of other transport modes. The study described in this paper attempts to develop a simulation platform for optimizing the placing-in and taking-out wagons system on the branch-shaped freight operation network (PTWS-BSFON). The operation process of PTWS is thoroughly analyzed from three aspects of decoupling-coupling wagons subsystem, placing-in local wagons subsystem, and taking-out local wagons subsystem. And then the simulation platform encompassing two typically shunting modes of PTWS are developed by Arena software. Under scenarios of PTWS-SO and PTWS-SSMS, the hierarchical structure of the shunting strategies is, respectively, outlined in the simulation platform. Finally, the shunting strategies based on the simulation platform are carried out by concrete examples, which prove the rationality of the methodology in applying different strategies and enhancing the performances of the PTWS-BSFON.

Document type: Article

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Scipedia content — Mon, 15 Feb 2021 10:14:47 +0100

Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor for ensuring the reliability of energy transportation systems is the assessment of real technical conditions of pipelines. Methods for assessing the state of pipeline systems by their vibro-acoustic parameters are widely used today. Traditionally, the Fourier transform is used to process vibration signals. However, as a rule, the oscillations of the pipe-liquid system are non-linear and non-stationary. This reduces the reliability of devices based on the implementation of classical methods of analysis. The authors used neural network methods for the analysis of vibro-signals, which made it possible to increase the reliability of diagnosing pipeline systems. The present work considers a method of neural network analysis of amplitude-frequency measurements in pipelines to identify the presence of a defect and further clarify its variety.

Document type: Article

Variations in Naturalistic Driving Behavior and Visual Perception at the Entrances of Short, Medium, and Long Tunnels

Scipedia content — Mon, 15 Feb 2021 10:11:24 +0100

Driver behavior and visual perception are very important factors in the management of traffic accident risk at tunnel entrances. This study was undertaken to analyze the differences in driving behavior and visual perception at the entrances of three types of tunnels, namely, short, medium-length, and long tunnels, under naturalistic driving conditions. Using three driving behavior indicators (speed, deceleration, and position) and two visual perception indicators (fixation and saccade), the driving performance of twenty drivers at six tunnels (two tunnels per condition) was comparatively analyzed. The results revealed that the speed maintained by the drivers prior to deceleration with braking under the short-tunnel condition was significantly larger than that under the medium- and long-tunnel conditions and that the drivers had a greater average and maximum deceleration rates under the short-tunnel condition. A similar general variation of driver visual perception appeared under the respective tunnel conditions, with the number of fixations gradually increasing and the maximum saccade amplitude gradually decreasing as the drivers approached the tunnel portal. However, the variation occurred approximately 60 m earlier under the short-tunnel condition than under the medium- and long-tunnel conditions. Interactive correlations between driving behavior and visual perception under the three conditions were established. The commencement of active deceleration was significantly associated (with correlation factors of 0.80, 0.77, and 0.79 under short-, medium-, and long-tunnel conditions, respectively) with the point at which the driver saccade amplitude fell below 10 degrees for more than 3 s. The results of this study add to the sum of knowledge of differential driver performance at the entrances of tunnels of different lengths.

Document type: Article

Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method

Scipedia content — Mon, 15 Feb 2021 10:11:06 +0100

In"?tex id="Q1" staff-cmt="As per style “The Nertherlands” in country name should not contain, hence we ignored the authors corrections. Please check and confirm."?" highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.

Document type: Article

Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks

Scipedia content — Mon, 15 Feb 2021 10:08:07 +0100

Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency.

Document type: Article

Assessing Resources Management for Sharing Economy in Urban Logistics

Scipedia content — Mon, 15 Feb 2021 10:07:49 +0100

Sharing economy requires cities to redefine their management strategies. As a consequence of the development of new ideas, the main aim of modern cities should focus on achieving the sustainable use of resources. In the existing literature, only a partial analysis of resources management in cities can be found. For this reason, the authors decided to prepare the framework for empirical research about resources management in sharing economy in cities, especially in the urban logistics system. The main aim of the study is systematizing criteria related to the assessment of the management of urban resources in the era of sharing economy. To achieve the goal of the research, a systematic literature review was made according to chosen approaches and procedures. This resulted in creating a set of criteria for the analysis and evaluation of resources management in urban areas. It contains five areas with 26 criteria and a map of assumed correlations between them. Those outcomes can be used by local authorities or even other urban logistics stakeholders to define or improve their actions aimed at developing a sharing economy services portfolio on the local market. Additionally, they constitute a set of initial information for further scientific research.

Document type: Article

Passenger Demand-Oriented High-Speed Train Stop Planning with Service-Node Features Analysis

Scipedia content — Mon, 15 Feb 2021 10:07:29 +0100

As a critical foundation for train traffic management, a train stop plan is associated with several other plans in high-speed railway train operation strategies. The current approach to train stop planning in China is based primarily on passenger demand volume information and the preset high-speed railway station level. With the goal of efficiently optimising the stop plan, this study proposes a novel method that uses machine learning techniques without a predetermined hypothesis and a complex solution algorithm. Clustering techniques are applied to assess the features of the service nodes (e.g., the station level). A modified Markov decision process (MDP) is conducted to express the entire stop plan optimisation process considering several constraints (service frequency at stations and number of train stops). A restrained MDP-based stop plan model is formulated, and a numerical experiment is conducted to demonstrate the performance of the proposed approach with real-world train operation data collected from the Beijing-Shanghai high-speed railway.

Document type: Article

Supply Chain Financial Risk Evaluation of Small- and Medium-Sized Enterprises under Smart City

Scipedia content — Mon, 15 Feb 2021 10:06:55 +0100

Prevention and control of risks are an eternal theme of financial institutions. Although, to some extent, the emergence of supply chain finance can enhance the financing capacity of small- and medium-sized enterprises (SMEs) and reduce financial risks of financial institutions, with the development of smart city and smart finance, the financial risks of SMEs are more complex, infectious, dormant, and difficult to accurately identify and measure. Facing this status, financial institutions have been required to understand and evaluate the financial risks of SMEs from a new perspective. Therefore, this paper, based on the study of financial risks assessment of SMEs under the smart city and smart finance, innovatively constructs a new index evaluation system for supply chain finance, based on improved hesitant fuzzy linguistic PROMETHEE method, and the effectiveness and advantages of the model have been verified through an example. To a certain degree, the SMEs financing the evaluation model and improved PROMETHEE method can not only help financial institutions reduce the risks in the specific financial transactions but also reduce the survival threat of financial institutions. Moreover, it is of positive significance to the stable operation of the financial system.

Document type: Article

Benefits of Electrified Powertrains in Medium- and Heavy-Duty Vehicles

Scipedia content — Mon, 15 Feb 2021 10:06:05 +0100

The benefits of electrified powertrains for light-duty vehicles are well understood, however sufficient published information is not available on the benefits of advanced powertrains on the various types of medium and heavy duty vehicles. Quantifying the benefits of powertrain electrification will help fleet operators understand the advantages or limitations in adopting electrified powertrains in their truck fleets. Trucks vary in size and shape, as they are designed for specific applications. It is necessary to model each kind of truck separately to understand what kind of powertrain architecture will be feasible for their daily operations. This paper examines 11 types of vehicles and 5 powertrain technology choices to quantify the fuel saving potential of each design choice. This study uses the regulatory cycles proposed by the US Environmental Protection Agency (EPA) for measuring fuel consumption.

Document type: Article

Containerships Sailing Speed and Fleet Deployment Optimization under a Time-Based Differentiated Freight Rate Strategy

Scipedia content — Mon, 15 Feb 2021 10:04:30 +0100

This paper investigates the problem of containership sailing speed and fleet deployment optimization in an intercontinental liner shipping network. Under the consideration of the time value of container cargo, three kinds of impact of sailing speed changes on long legs of each liner route are analysed, and a time-based freight rate strategy is proposed. Then, the optimization problem is formulated as a mixed-integer nonlinear programming. Its goal is to maximize the total profits of a container liner shipping. To find the optimal solution to the model and improve the efficiency of model solution, a discretization algorithm is proposed. Numerical results verify the applicability of the proposed model and the efficiency of the algorithm. In addition, the time-based freight rate strategy is able to achieve more profit compared to a fixed freight rate strategy.

Document type: Article

The traffic signal control problem for intersections: a review

Scipedia content — Mon, 15 Feb 2021 10:01:20 +0100

"jats:sec" "jats:title"Background"/jats:title" "jats:p"The intersection traffic signal control problem (ITSCP) has become even more important as traffic congestion has been more intractable. The ITSCP seeks an efficient schedule for traffic signal settings at intersections with the goal of maximizing traffic flow while considering various factors such as real-time strategies, signal timing constraints, rapid developments in traffic systems, and practical implementation. Since the factors constituting the ITSCP exhibit stochastically complicated interactions, it is essential to identify these factors to propose solution methods that can address this complexity and still be practically implemented."/jats:p" "/jats:sec" "jats:sec" "jats:title"Objective"/jats:title" "jats:p"The objective of this review is to provide a survey of problems, methods, and practices in the evaluation of the ITSCP. In this paper, a unified terminology for the ITSCP and a citation network of the current body of relevant research are accordingly presented, and various assumptions, constraints, and solution approaches are summarized. A review across the entire body of knowledge throughout the history of the ITSCP is therefore provided. This review also highlights open issues and challenges that remain to be addressed by future research."/jats:p" "/jats:sec

Document type: Article

A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions

Scipedia content — Mon, 15 Feb 2021 10:00:52 +0100

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

Document type: Article

Location Design of Electrification Road in Transportation Networks for On-Way Charging

Scipedia content — Mon, 15 Feb 2021 10:00:37 +0100

Electric vehicles tend to be a great mobility option for the potential benefits in energy consumption and emission reduction. On-way charging (OWC) has been recognized to be a promising solution to extend driving range for electric vehicles. Location of the electrification road (ER) is a critical issue for future urban traffic management to accommodate the new mobility option. This paper proposes a mathematical program with equilibrium constraint (MPEC) approach to solve this problem, which minimizes the total travel time with a limited construction budget. To describe the drivers’ routing choice, a path-constrained network equilibrium model is proposed to minimize their travel time and prevent running out of charge. We develop a modified active set algorithm to solve the MPEC model. Numerical experiments are presented to demonstrate the performance of the model and the solution algorithm and analyze the impact of charging efficiency, battery size, and comfortable range.

Document type: Article

Impact of Smart Charging for Consumers in a Real World Pilot

Scipedia content — Mon, 15 Feb 2021 09:59:46 +0100

smart charging profile was implemented on 39 public charging stations in Amsterdam on which the current level available for electric vehicle (EV) charging was limited during peak hours on the electricity grid (07:00&ndash

08:30 and 17:00&ndash

20:00) and was increased during the rest of the day. The impact of this profile was measured on three indicators: average charging power, amount of transferred energy and share of positively and negatively affected sessions. The results are distinguished for different categories of electric vehicles with different charging characteristics (number of phases and maximum current). The results depend heavily on this categorisation and are a realistic measurement of the impact of smart charging under real world conditions. The average charging power increased as a result of the new profile and a reduction in the amount of transferred energy was detected during the evening hours, causing outstanding demand which was solved at an accelerated rate after limitations were lifted. For the whole population, 4% of the sessions were positively affected (charged a larger volume of energy) and 5% were negatively affected. These numbers are dominated by the large share of plug-in hybrid electric vehicles (PHEVs) in Amsterdam which are technically not able to profit from the higher current levels. For new generation electric vehicles, 14% of the sessions were positively affected and the percentage of negatively affected sessions was 5%.

Document type: Article

Impacts of increasing maximum truck weight – case Finland

Scipedia content — Mon, 15 Feb 2021 09:59:30 +0100

Finnish government allowed in October 2013 operating high capacity vehicles (HCV) with a maximum weight limit of 76 t on Finnish roads. An analysis on how HCVs have affected the Finnish road freight transport sector is presented here based on a continuous time series data from 2013 to 2017. The analysis shows a significant increase in the average payload weight and a transition from 7-axle to 8- and 9-axle articulated vehicle combinations, which allow the higher weights. Truck mileage of 225 million km has been avoided from October 2013 until the end of 2017 and avoided mileage corresponded in 2017 to approximately 4% of total truck mileage in Finland. This equals around 126 million € cost savings in 2017 and 0.1 Mt of CO2 emissions reduction in road freight, even after taking into account that there has been some modal shift from rail to road.

Document type: Article

Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles

Scipedia content — Mon, 15 Feb 2021 09:58:00 +0100

The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared.

Document type: Article

The Reversible Lane Network Design Problem (RL-NDP) for Smart Cities with Automated Traffic

Scipedia content — Mon, 15 Feb 2021 09:55:04 +0100

With automated vehicles (AVs), reversible lanes could be a sustainable transportation solution once there is vehicle-to-infrastructure connectivity informing AVs about the lane configuration changes. This paper introduced the reversible lane network design problem (RL-NDP), formulated in mixed-integer non-linear mathematical programming&mdash

both the traffic assignment and the reversible lane decisions were embedded. The model was applied on an hourly basis in the case study of the city of Delft, the Netherlands. Reversible lanes are examined under no traffic equilibrium (former paths are maintained)

user-equilibrium (UE) assignment (AVs decide their own paths)

and system-optimum (SO) traffic assignment (AVs are forced to follow SO paths). We found out that reversible lanes reduce congested roads, total travel times, and delays up to 36%, 9%, and 22%, respectively. The SO scenario was revealed to be beneficial in reducing the total travel time and congested roads in peak hours, whereas UE is equally optimal in the remaining hours. A dual-scenario mixing SO and UE throughout the day reduced congested roads, total travel times, and delay up to 40%, 8%, and 19%, respectively, yet increased 1% in travel distance. The spatial analysis suggested a substantial lane variability in the suburbs, yet a strong presence of reversible lanes in the city center.

Document type: Article

Control of Dual-Output DC/DC Converters Using Duty Cycle and Frequency

Scipedia content — Mon, 15 Feb 2021 09:54:36 +0100

As part of the integration process of the auxiliary power systems of electric vehicles, plug-in hybrid vehicles and fuel cell vehicles, this study proposes a method to control two different voltage types using two control factors of the rectangular alternating waveforms contained in DC/DC converters, namely the duty cycle and frequency. A prototype circuit consisting of an H-bridge inverter, a transformer, two series resonant filters and two diode bridge circuits was constructed. The H-bridge inverter was connected to the primary side of the transformer and the diode bridge rectifier circuit was connected to the secondary side in parallel. Series resonant filters were inserted between one of the diode bridge circuits and the transformer. Thereafter, the proposed control method was applied to the transformer voltage of the prototype circuit. Although the circuit operation became complex owing to the circulating current flowing between the ground (GND) of the two output circuits, it exhibited ideal static and dynamic characteristics, thereby confirming the possibility of controlling two voltages with the duty cycle and frequency control factors. The results of the efficiency evaluation and loss analysis demonstrated a minimum efficiency of 68.3% and a maximum efficiency of 88.9%. As the output power of the circuit containing the resonant filters increased, the current peak value increased and the circuit became less efficient.

Document type: Article

Optimization and Comparative Analysis of Traffic Restriction Policy by Jointly Considering Carpool Exemptions

Scipedia content — Mon, 15 Feb 2021 09:51:47 +0100

As a countermeasure to urban exhaust pollution and traffic congestion, traffic restriction policy (TRP) and carpooling strategy have been widely introduced throughout the world. However, their effects are largely determined by the rationality of implementing policies, and unreasonable policies make them controversial on the long-term implementation benefits. To more effectively manage traffic demand and maintain the sustainability of transportation system, it is necessary to make optimization for management policy before implementation. In this paper, the elastic demand model and equilibrium assignment model are developed under TRP. Considering the negative impact of the mandatory TRP on the public acceptance, we propose a novel TRP strategy, namely TRP with carpool exemptions (TRP-CE), that is, a proportion of high occupancy vehicles (HOV) are allowed to travel in the restricted district even if their license plate numbers are restricted. Then, a bi-level programming model is proposed to solve the optimal schemes by combining multi purposes of ensuring travel convenience, alleviating traffic congestion, and reducing the exhaust pollution. Finally, a numerical experiment is conducted to evaluate the effectiveness of proposed models and make comparative analysis between separate TRP and TRP-CE. The results indicate that TRP-CE has benefits in the following aspects: (1) Carpool exemptions provide an incentive to carpool for travelers by private cars; (2) the public acceptance of TRP is improved by introducing carpool exemptions as a compensatory mitigation strategy for mandatory TRP; (3) the implementation effect of demand management can be well achieved by joint optimization; and (4) there is no need to design and reconstruct HOV lanes for the implementation of TRP-CE, which is convenient for practical application.

Document type: Article

Parking Pricing vs. Congestion Pricing: A Macroscopic Analysis of their Impact on Traffic

Scipedia content — Mon, 15 Feb 2021 09:47:19 +0100

As traffic congestion gets worse year by year in metropolitan areas, cities search for solutions to improve their traffic performance and reduce their environmental impacts. This paper focuses on parking pricing and congestion pricing and their short-term effects not only on traffic congestion but also on the potential revenue for a city. We develop an easy to implement multimodal macroscopic traffic and parking search model for a central area based on aggregated data at the network level. Our methodology allows us to analyze how introducing parking pricing inside a network, or a congestion toll combined with a park and ride (P+R) scheme can affect the drivers’ decision between entering the network by car (private vehicle) or using P+R instead. This decision directly influences the number of drivers using P+R, and this impacts, in turn, the traffic performance. Based on such analysis, a city can get valuable insights to evaluate whether congestion pricing is a necessity or if the traffic improvements resulting from implementing parking pricing strategies are sufficient when combined with P+R facilities. A search algorithm is used to find the best trade-off between the parking fees and the congestion toll. Any additional revenue collected through these schemes can then be used to improve public transport or the P+R facilities themselves. With minor data collection efforts and little computational costs compared to most existing parking and congestion pricing models, we illustrate our proposed framework in a case study of an area with a high parking demand for public parking spaces within the city of Zurich, Switzerland. Results show that parking pricing combined with P+R is indeed a viable option compared to congestion pricing for improving traffic performance, even if parking pricing schemes do not target all the drivers.

Document type: Article

Simulating Crowd Evacuation in a Social Force Model with Iterative Extended State Observer

Scipedia content — Mon, 15 Feb 2021 09:46:13 +0100

Due to the interaction and external interference, the crowds will constantly and dynamically adjust their evacuation path in the evacuation process to achieve the purpose of rapid evacuation. The information from previous process can be used to modify the current evacuation control information to achieve a better evacuation effect, and iterative learning control can achieve an effective prediction of the expected path within a limited running time. In order to depict this process, the social force model is improved based on an iterative extended state observer so that the crowds can move along the optimal evacuation path. First, the objective function of the optimal evacuation path is established in the improved model, and an iterative extended state observer is designed to get the estimated value. Second, the above model is verified through simulation experiments. The results show that, as the number of iterations increases, the evacuation time shows a trend of first decreasing and then increasing.

Document type: Article

Integrated Optimization of Sustainable Transportation and Inventory with Multiplayer Dynamic Game under Carbon Tax Policy

Scipedia content — Mon, 15 Feb 2021 09:43:23 +0100

Growth in environmental sustainability has prompted the logistics industry to seek sustainable development, and carbon tax policies are considered an effective approach to reducing carbon emissions. This study investigates the optimization of sustainable transportation and inventory under a carbon tax policy and explores effective methods for coordinating the interests of governments and enterprises. The results can provide insights into sustainable logistics for decision-making by enterprises and policy-making by governments. We first examine a Stackelberg game model and design an iterative solution to optimize sustainable transportation and inventory under the carbon tax policy. We then establish a three-stage dynamic game model to optimize the wholesale price, carbon tax rate, and proportion of sustainable investment shared by the government. Furthermore, we perform a simulation to identify the optimal solution of the three-stage game, and we compare the simulation results with a numerical example. The results indicate that a carbon tax policy can improve social welfare and the sustainability of transportation and inventory but could hinder corporate profits. An appropriate sustainable investment-sharing strategy could compensate for the shortcomings of the carbon tax policy and result in positive outcomes for governments and enterprises.

Document type: Article

Driver Monitoring for a Driver-Centered Design and Assessment of a Merging Assistance System Based on V2V Communications

Scipedia content — Fri, 12 Feb 2021 16:24:03 +0100

Merging is one of the most critical scenarios that can be found in road transport. In this maneuver, the driver is subjected to a high mental load due to the large amount of information he handles, while making decisions becomes a crucial issue for their safety and those in adjacent vehicles. In previous works, it was studied how the merging maneuver affected the cognitive load required for driving by means of an eye tracking system, justifying the proposal of a driver assistance system for the merging maneuver on highways. This paper presents a merging assistance system based on communications between vehicles, which allows vehicles to share internal variables of position and speed and is implemented on a mobile device located inside the vehicle. The system algorithm decides where and when the vehicle can start the merging maneuver in safe conditions and provides the appropriate information to the driver. Parameters and driving simulator tests are used for the interface definition to develop the less intrusive and demanding one. Afterward, the system prototype was installed in a real passenger car and tests in real scenarios were conducted with several drivers to assess usability and mental load. Comparisons among alternative solutions are shown and effectiveness is assessed.

Document type: Article

Long-Term Forecast of Energy and Fuels Demand Towards a Sustainable Road Transport Sector in Ecuador (2016–2035): A LEAP Model Application

Scipedia content — Fri, 12 Feb 2021 16:21:58 +0100

The total energy demand in the transport sector represented 48.80% of the total consumption in Ecuador throughout 2016, where 89.87% corresponded to the road transport sector. Therefore, it is crucial to analyze the future behavior of this sector and assess the economic and environmental measures towards sustainable development. Consequently, this study analyzed: (1) the total energy demand for each vehicle class and fuel type

(2) the GHG (greenhouse gas) emissions and air pollutants NOx and PM10

and (3) the cost attributed to the fuel demand, between 2016 and 2035. For this, four alternative demand scenarios were designed: BAU: Bussiness As Usual

EOM: Energy Optimization and Mitigation

AF: Alternative Fuels

and SM: Sustainable Mobility using Long-range Energy Alternatives Planning system. After analysis, the EOM, AF, and SM scenarios have advantages relative to BAU, where SM particularly stands out. The results show that SM compared to BAU, contributes with a 12.14% (141,226 kBOE) decrease of the total energy demand, and the economic savings for this fuel demand is of 14.22% (26,720 MUSD). Moreover, global NOx and PM10 emissions decreased by 14.91% and 13.78%, respectively. Additionally, accumulated GHG emissions decreased by 13.49% due to the improvement of the fuel quality for the vehicles that mainly consume liquefied petroleum gas, natural gas, and electricity.

Document type: Article

What Motivates Drivers to Comply with Speed Guidance Information at Signalized Intersections?

Scipedia content — Fri, 12 Feb 2021 16:21:05 +0100

This study explored the intrinsic motivation of drivers most likely to accept guidance information at signalized intersections by using a mixed model approach. The proposed approach contains a multiple-indicator multiple-cause model (MIMIC) with a latent class analysis (LCA). The MIMIC model was used to quantify intrinsic motivations according to individual heterogeneity. From a group similarity perspective, the LCA was employed for the latent classification of drivers. The features and possibility of accepting guidance information of each class were also analyzed according to the intrinsic motivation of drivers. Data were collected from the stated preference online surveys, in which the questionnaire was designed according to the diffusion of innovation, in 2015 and 2019 in China. Four subjective perceptions of drivers were identified: the perception of innovating guidance information, the perception of convenience regarding guidance information transmission, the perception of surrounding complexity, and the individual innovation. The estimation results show that age, driving experience, education levels, and familiarity with road network are significant factors of compliance behavior. The proportion of conservatives gradually decreased from 2015 to 2019, while the proportion of early followers and late followers increased through market penetration, familiarity with the Internet of vehicles, and social networks in the same period. This prevalence demonstrates that guidance information at signalized intersections is gradually becoming acceptable in China.

Document type: Article

Electrical bearing failures in electric vehicles

Scipedia content — Fri, 12 Feb 2021 16:20:49 +0100

In modern electric equipment, especially electric vehicles, inverter control systems can lead to complex shaft voltages and bearing currents. Within an electric motor, many parts have electrical failure problems, and among which bearings are the most sensitive and vulnerable components. In recent years, electrical failures in bearing have been frequently reported in electric vehicles, and the electrical failure of bearings has become a key issue that restricts the lifetime of all-electric motor-based power systems in a broader sense. The purpose of this review is to provide a comprehensive overview of the bearing premature failure in the mechanical systems exposed in an electrical environment represented by electric vehicles. The electrical environments in which bearing works including the different components and the origins of the shaft voltages and bearing currents, as well as the typical modes of electrical bearing failure including various topographical damages and lubrication failures, have been discussed. The fundamental influence mechanisms of voltage/current on the friction/lubrication properties have been summarized and analyzed, and corresponding countermeasures have been proposed. Finally, a brief introduction to the key technical flaws in the current researches will be made and the future outlook of frontier directions will be discussed.

Document type: Article

Auction Based Algorithm for a Smart Junction with Social Priorities

Scipedia content — Fri, 12 Feb 2021 16:20:31 +0100

Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. In the context of IoT in transportation, one feature is the smart junction. This research deals with this junction, where several cars approach the intersection from different directions, and a smart traffic light must decide regarding the time intervals of red and green light in each direction. Out novel approach is based not only on the number of vehicles in each lane, but also on the social characteristics of the passengers (e.g. a handicapped person, a driver with no previous traffic violations). These factors will be gleaned from IoT network sources on cars, traffic lights, individuals, municipality data, and more. In this paper, we suggest using a VCG (Vickrey-Clarke-Groves) auction mechanism for the intersection scheduling, combining the social characteristic with a benefit parameter that expresses the passenger’s subjective perception of the importance of crossing the intersection as soon as possible. Our simulation results show the efficiency of the suggested protocol and demonstrate how the intersection scheduling depends on the passengers’ preferences, as well as on their social priorities.

Document type: Article

Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model

Scipedia content — Fri, 12 Feb 2021 16:18:49 +0100

Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.

Document type: Article

Behavioural Effects of Spatially Structured Scoring Systems in Location-Based Serious Games—A Case Study in the Context of OpenStreetMap

Scipedia content — Fri, 12 Feb 2021 16:18:19 +0100

Location-based games have become popular in recent years, with Poké

mon Go and Ingress being two very prominent examples. Some location-based games, known as Serious Games, go beyond entertainment and serve additional purposes such as data collection. Such games are also found in the OpenStreetMap context and playfully enrich the project&rsquo

s geodatabase. Examples include Kort and StreetComplete. This article examines the role of spatially structured scoring systems as a motivational element. It is analysed how spatial structure in scoring systems is correlated with changes observed in the game behaviour. For this purpose, our study included two groups of subjects who played a modified game based on StreetComplete in a real urban environment. One group played the game with a spatially structured scoring system and the other with a spatially random scoring system. We evaluated different indicators and analysed the players&rsquo

GPS trajectories. In addition, the players filled out questionnaires to investigate whether they had become aware of the scoring system they were playing. The results obtained show that players who are confronted with a spatially structured scoring system are more likely to be in areas with high scores, have a longer playing time, walk longer distances and are more willing to take detours. Furthermore, discrepancies between the perception of a possible system in the scoring system and corresponding actions were revealed. The results are informative for game design, but also for a better understanding of how players interact with their geographical context during location-based games.

Document type: Article

Moving Object Classification Using 3D Point Cloud in Urban Traffic Environment

Scipedia content — Fri, 12 Feb 2021 16:17:01 +0100

Moving object classification is essential for autonomous vehicle to complete high-level tasks like scene understanding and motion planning. In this paper, we propose a novel approach for classifying moving objects into four classes of interest using 3D point cloud in urban traffic environment. Unlike most existing work on object recognition which involves dense point cloud, our approach combines extensive feature extraction with the multiframe classification optimization to solve the classification task when partial occlusion occurs. First, the point cloud of moving object is segmented by a data preprocessing procedure. Then, the efficient features are selected via Gini index criterion applied to the extended feature set. Next, Bayes Decision Theory (BDT) is employed to incorporate the preliminary results from posterior probability Support Vector Machine (SVM) classifier at consecutive frames. The point cloud data acquired from our own LIDAR as well as public KITTI dataset is used to validate the proposed moving object classification method in the experiments. The results show that the proposed SVM-BDT classifier based on 18 selected features can effectively recognize the moving objects.

Document type: Article

Economic impact of autonomous vehicles in Spain

Scipedia content — Fri, 12 Feb 2021 16:16:12 +0100

"jats:sec" "jats:title"Background"/jats:title" "jats:p"Pollution, traffic accidents, and congestion are huge problems in most urban areas. Autonomous and electric vehicles are leading our society to a new mobility model, also known as the New Era of Transportation (NERTRA). Mobility is a strategic issue for any country, and this change of model will mean, in addition to a great technological revolution, an economic revolution."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methodology"/jats:title" "jats:p"The purpose of this work is to carry out a study of the main economic sectors affected in Spain and evaluate the economic impact that autonomous vehicles will have on each one. Data from this sector have been collected, analyzing the economic and technological factors that will affect to a greater or lesser extent and made evolution forecasts."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The study presents three different scenarios depending on how the transition progresses. The results are presented by each sector in each of these scenarios. These results have great value for the industry itself and the professionals affected, as well as for the authorities of the country to take the appropriate measures from the beginning of the transition. The sectors directly affected by this change represent more than 38% of the gross domestic product of Spain. Key factors, such as technological innovation in vehicles, and the emergence of new business and mobility models determine drastic changes in some important sectors. It is important to make quick decisions both from administrations and from the industry itself to adapt to new mobility."/jats:p" "/jats:sec

Document type: Article

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Scipedia content — Fri, 12 Feb 2021 16:13:18 +0100

This study analyzes the performance of a queue length-dependent overload control policy using a leaky bucket (LB) scheme. This queueing model is applied to the operation of a battery swapping and charging station for electric vehicles (EVs). In addition to the LB scheme, we propose two congestion control policies based on EV queue length thresholds. With these policies, the model determines both EV-arrival and battery-supply intervals, and these depend on the number of EVs waiting in the queue. The queue length distributions, including those at arbitrary epochs, are derived using embedded Markov chain and supplementary variable methods. Performance measures such as blocking probability and mean waiting time are investigated using numerical examples. We study the characteristics of the system using numerical examples and use a cost analysis to investigate situations in which the application of each congestion control policy is advantageous.

Document type: Article

A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks

Scipedia content — Fri, 12 Feb 2021 16:12:33 +0100

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system.

Document type: Article

Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Scipedia content — Fri, 12 Feb 2021 16:08:20 +0100

Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

Document type: Article

BEHT: Blockchain-Based Efficient Highway Toll Paradigm for Opportunistic Autonomous Vehicle Platoon

Scipedia content — Fri, 12 Feb 2021 16:07:57 +0100

Autonomous vehicle platoon is a promising paradigm towards traffic congestion problems in the intelligent transportation system. However, under certain circumstances, the advantage of the platoon cannot be fully developed. In this paper, we focus on the highway Electronic Toll Collection (ETC) charging problem. We try to let the opportunistic platoon pass the ETC as a whole. There are three main issues in this scenario. Firstly, the opportunistic platoon is temporarily composed; vehicles do not trust each other. Secondly, single vehicle may try to escape from the ETC charging by following the platoon. Finally, platoon members may collude with each other and try to underreport the number of vehicles in the platoon so as to evade payment. To solve these challenges, we propose a blockchain-based efficient highway toll paradigm for the opportunistic platoon. The driving history, credential information of every registered vehicle, is recorded and verified from the blockchain. A roadside unit (RSU) is adopted to distinguish the single vehicle from the platoon and in charge of lane allocation. Additionally, an aggregate signature is introduced to accelerate the authentication procedure in the RSU. We analyse the potential security threats in this scenario. The experimental result indicates that our scheme is efficient and practical.

Document type: Article