Participatory Value Evaluation (PVE) is a new method to assess the desirability of government projects. In a PVE, individuals select their preferred portfolio of government projects given a constrained public budget. Individuals’ preferences for (the impacts of) government projects can be determined based on these choices. The obtained preferences can be used to rank government projects in terms of their desirability. Cost-Benefit Analysis (CBA) is an alternative appraisal method used to assess the desirability of government projects. CBA establishes the desirability of public projects through analyzing people's trade-offs between their private income and impacts of public projects. The primary objective of this paper is to investigate whether CBA and PVE lead to different policy recommendations in the context of urban mobility investments. We conducted CBAs and a PVE for 16 urban mobility investment projects and find indicative evidence that projects which focus on improving traffic safety and improvements for cyclists/pedestrians rank higher in the PVE, whereas car projects rank higher in the CBA analysis.

@en

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the driving environment characteristics that affect the performance of automated vehicles. In this context, a field test with Lane Departure Warning (LDW) and Lane Keeping Systems (LKS)-enabled vehicles was conducted in the Netherlands. Empirical data from the experiment was used to estimate the impact of driving environment components such as weather condition and lane width on the performance of the automated vehicles. Driving at night in the presence of streetlights with rain resulted in least detection performance for both the vehicles as compared to other visibility conditions. As for lane-keeping performance, the LKS positioned the vehicle significantly more to the left of the lane on left-curves than on straight sections. The LKS also positioned the vehicle more left on lanes with a width less than 250 cm than on wider lanes. These findings were translated into levels of service of the Operational Design Domain (ODD). Each level of service corresponded to a performance level of the lane assistance systems, classified as “High”, “Medium”, and “Low”, and defined using indicators.@en

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the road infrastructure requirements that would lead to safe operation of automated vehicles. In this context, a field test with Lane Departure Warning and Lane Keeping Systems-enabled vehicles was conducted in the province of North Holland, The Netherlands. The performance of these automated systems was evaluated using performance indicators such as Mean Lateral Position and Standard Deviation of Lane Position. In this study, the Systems Theoretic Accident Modelling and Processes (STAMP) model was adopted to understand the relationships between the various components of the “Road System”, which in this study include the road authority, the automated vehicle system, elements of the road infrastructure, and weather conditions. Empirical data from the experiment is used to estimate the relationships between the different components, followed by the assessment of their impact on the performance of the automated vehicles. It was found that visibility conditions have a significant effect on detection performance, which worsens in rainy conditions especially under streetlights. It has been also observed that there is a significant difference in Lane Position between Left Curves and Straight sections, and between lane widths less than 250 cms and those that have larger widths. These findings are combined with the results from the STAMP analysis to formulate a set of road infrastructure requirements that would lead to safe performance of Lane Assistance Systems.@en

This special issue presents seven selected papers from the 5th Symposium of the European Association of Research in Transportation (hEART) which was held in September 2016 in Delft, The Netherlands, and was organised by Delft University of Technology. The contributions cover a wide range of topics in transportation, reflecting the broad scope of the hEART symposium series, including but not limited to travel behaviour, travel survey methods, railway freight as well as (terminal) logistics. As editors of this special issue we are very grateful for the efforts made by the authors, in particular their enduring efforts to push the quality of their contributions to a higher level during the review process. Also, we like to take the opportunity to thank all the reviewers for their constructive feedback and suggestions for improvement. This editorial provides a brief overview of the hEART2016 symposium. After that, an overview is given of the seven contributions that form this special issue.@en

This paper is the first to develop a measure of consumer surplus for the Random Regret Minimisation (RRM) model. Following a not so well-known approach proposed two decades ago, we measure (changes in) consumer surplus by studying (changes in) observed behaviour, i.e. the choice probability, in response to price (changes). We interpret the choice probability as a well-behaved approximation of the probabilistic demand curve and accordingly measure the consumer surplus as the area underneath this demand curve. The developed welfare measure enables researchers to assign a measure of consumer surplus to specific alternatives in the context of a given choice set. Moreover, we are able to value changes in the non-price attributes of a specific alternative. We illustrate how differences in consumer surplus between random regret and random utility models follow directly from the differences in their behavioural premises.@en