Abstract

Driver distraction is a leading cause of crashes. The introduction of in-vehicle technology in the last decades has added support to the driving task. However, in-vehicle technologies and handheld electronic devices may also be a threat to driver safety due to information overload and distraction. Adaptive in-vehicle information systems may be a solution to this problem. Adaptive systems could aid the driver in obtaining information from the device (by reducing information density) or prevent distraction by not presenting or delaying information when the driver’s workload is high. In this paper, we describe an on-the-road evaluation of a real-time driver workload estimator that makes use of geo-specific information. The results demonstrate the relative validity of our experimental methods and show the potential for using location-based adaptive in-vehicle systems.

Abstract

International audience; The scale of proposed carbon dioxide (CO2) capture and storage (CCS) chains also draws attention to the hazards posed by the transport mode, enabling the link from source to sink. As such, the adverse consequences of an accidental release from transport pipelines or other equipment containing CO2 at high pressure in a dense-phase (supercritical or liquid) state are (re)considered in theory and in practice. Several experimental observations of large-scale CO2 releases have been made, and yet the physics and thermodynamics involved are not fully understood. The work presented here provides a database focused on the specificities of the release and the dispersion of the carbon dioxide cloud in case of substantial variations of storage (temperature and pressure) and discharge conditions (nozzle size). Document type: Conference object

Abstract

—With increasing numbers of vehicles using low level automation and higher level automation expected in the future, significant effects are expected on traffic flow. Despite much simulation and driving simulator research on SAE level 1 vehicles, there remain many questions in regard to the effects of the systems on traffic flow. The effects of higher levels of automation are even more difficult to estimate, as these vehicles are not even present on roads at this time, let alone in sufficient numbers to analyze. In this research, we propose a methodology for a-priori analysis of potential conflict situations: Method for Explorative TRaffic scenario Observation and Analysis (METRO-A). It is applied to the case of automated driving in conventional traffic to analyze potential difficulties that SAE level 3 and 4 and higher vehicles may encounter in mixed traffic conditions, for a weaving section case-study. Furthermore, a set of important research questions are constructed that are relevant for the automotive industry, and road agencies and authoritie

Abstract

This paper describes a study which gives insight into the size of improvement that is possible with individual in-car routing advice based on the actual traffic situation derived from floating car data (FCD). It also gives an idea about the required penetration rate of floating car data needed to achieve a certain degree of improvement. The study uses real loop detector data from the region of Amsterdam collected for over a year, a route generating algorithm for in-car routing advice, and emulated floating car data to generate the routing advice. The case with in-car routing advice has been compared to the base case, where drivers base their routing decisions on average knowledge of travel times in the network. The improvement in total delay using the in-vehicle system is dependent on penetration rate and accuracy of the floating car data and varies from 2.0% to 3.4% for 10% penetration rate. This leads to yearly savings of about 15 million euros if delay is monetarised using standard prices for value of time (VOT). © 2017 Gerdien A. Klunder et al. Document type: Article

Abstract

Intelligent transport systems are accepted as an integral part of the transport system. They have high potential in reducing the carbon footprint of traffic while improving efficient and safe transport. The calculation of CO"inf"2"/inf" emissions arising from the transport sector incorporating the impact of ITS is a challenging task. A systematic assessment methodology will support developers, public authorities and investors in ITS solutions to make sound decisions based on comparable and transparent impact estimates. As the basis for such an assessment, the fragmentation of traffic in underlying processes is suggested. These processes can be divided into transport demand related processes and driver behaviour and vehicle related processes. Together these processes lead to traffic flow. Transport processes are influenced by various factors. Both the processes itself and the factors influencing them can be affected by ITS. A systematic analysis of the potential effects of ITS on all these levels is the prerequisite for choosing a suitable modelling approach to quantify the effects. It also ensures the transparency of the modelling process by elucidating the required model sensitivities. The details of such an approach and its context from user need to a standardised assessment methodology for ITS is described. © The Institution of Engineering and Technology 2015.

Abstract

Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO"inf"2"/inf" emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour. © 2015.

Abstract

utomated driving technology is emerging. Yet, little is known in the literature about when automated vehicles will reach the market, how penetration rates will evolve and to what extent this new transport technology will affect transport demand and planning. This study uses scenario analysis to identify plausible future development paths of automated vehicles in the Netherlands and to estimate potential implications for traffic, travel behaviour and transport planning on a time horizon up to 2030 and 2050. The scenario analysis was performed through a series of three workshops engaging a group of diverse experts. Sixteen key factors and five driving forces behind them were identified as critical in determining future development of automated vehicles in the Netherlands. Four scenarios were constructed assuming combinations of high or low technological development and restrictive or supportive policies for automated vehicles (AV ...in standby, AV ...in bloom, AV ...in demand, AV ...in doubt). According to the scenarios, fully automated vehicles are expected to be commercially available between 2025 and 2045, and to penetrate the market rapidly after their introduction. Penetration rates are expected to vary among different scenarios between 1% and 11% (mainly conditionally automated vehicles) in 2030 and between 7% and 61% (mainly fully automated vehicles) in 2050. Complexity of the urban environment and unexpected incidents may influence development path of automated vehicles. Certain implications on mobility are expected in all scenarios, although there is great variation in the impacts among the scenarios. Measures to curb growth of travel and subsequent externalities are expected in three out of the four scenario

Abstract

The IMPACTS project investigated how impurities affect the design and operation of CO2 transport and storage processes. Experimental work and modelling have advanced the state of the art in the areas of how impurities in CO2 affect thermo-physical properties, fluid flow, corrosion, and reservoir chemical reactivity. High-quality experimental thermodynamic data were obtained to develop new reference equations of state for exhaust gases (EOS-CG). Parts of the results have been distributed as the publicly available TREND 2.0 software. The software enables modelling the true behaviour of impure CO2 mixtures, thereby increasing the fidelity of the simulations. IMPACTS also provided recommendations that relate results from parts of the CCS chain into results that relate to the entire CCS chain. They cover such areas as trade-offs between impurity levels and CCS system costs, optimized CO2 quality on a case-by-case basis for CCS chains, and design/operation of pipelines. New methodologies have been suggested for selecting the design criteria for operation of CO2 pipelines to avoid two-phase flow and running ductile fractures. This can help limit over-specifying design and reduce costs. For example, IMPACTS has shown how 4% of impurities in the CO2 can increase the power consumption for compression and processing by 50%. IMPACTS has generated a large amount of new knowledge and is an example of how a wide-reaching research program with several groups investigating different aspects of the same topic has brought the knowledge base forward while ensuring that the results are aligned. © 2016 Elsevier Ltd

Abstract

Joint fact-finding has been advanced as a method for helping stakeholders grappling with technically intensive policy and planning challenges to collaboratively engage in research and arrive at shared sets of facts to inform their decision-making. This paper introduces joint fact-finding and considers its application in the context of infrastructure stakeholders aiming to assess and increase the resilience of their infrastructure systems to climate change. A set of evaluative criteria is introduced, which are proposed for assessing joint fact finding processes both procedurally and substantively in terms of the outcomes, considering them to be both arenas for collaborative governance and joint knowledge production efforts. These criteria are applied to a case in Rotterdam, the Netherlands. This case suggests that joint fact-finding can provide value, but also reveals some lessons. For the efforts themselves, these lessons relate to: The practical applicability of the outcomes; the inherently contingent nature of the outcomes when addressing wicked problems; questions of representation from stakeholder groups; and the importance of leadership and good process design. The following observations are made regarding the criteria: While they are typically interdependent, both process and outcomes should be evaluated; and more attention should be paid to the method and metrics of evaluation, while recognizing that there is no single formula or approach that can be applied, given the heterogeneity of the criteria. © 2016, Editorial Board EJTIR. All rights reserved.

Abstract

Weather conditions are widely acknowledged to contribute to the occurrence of congestion on motorway traffic by influencing both traffic supply and traffic demand. To the best of our knowledge, this is the first paper that explicitly integrates supply and demand effects in predicting the influence of adverse weather conditions on the probability of occurrence of congestion. Traffic demand is examined by conducting a stated adaptation experiment, in which changes in travel choices are observed under adverse weather scenarios. Based on these choices, a Panel Mixed Logit model is estimated. Supply effects are taken into account by examining the influence of precipitation on motorway capacity. Based on the Product Limit Method, capacity distribution functions are estimated for dry weather, light rain and heavy rain. With the developed model to integrate the supply and demand effects breakdown probabilities can be calculated for any given traffic demand and capacity. The results show that rainfall leads to a significant increase in the probability of traffic breakdown at bottleneck locations. Interestingly the probability of a breakdown at these bottleneck locations is predicted to be slightly higher in light rain (98.7%) than in heavy rain (95.7%) conditions, which is the result of the higher traffic demand in light rain conditions. Based on the results presented in this paper, it can be recommended to always incorporate both supply and demand effects in the predictions of motorway breakdown probabilities due to adverse weather conditions to improve the validity of the predictions. © 2015 Editorial Board EJTIR. All rights reserved.