Surveys with stated choice experiments (SCE) are widely used to derive values of time and reliability for transport project appraisal purposes. However, such methods ask respondents to make hypothetical choices, which in turn could create a bias between choices made in the experiment compared to those in an environment where the choices have consequence. In this paper, borrowing principles of experimental economics, we introduce an incentive compatible driving simulator experiment, where participants are required to experience the travel time of their chosen route and actually pay any toll costs associated with the choice of a tolled road. In a first for the literature, we use a within respondent design to compare both the value of travel time savings (VTT) and value of travel time reliability (VOR) across a typical SCE and an environment with simulated consequence. Given the importance of VTT and VOR to transport decision making and the difficulty in estimating VOR using revealed preference data, our results are noteworthy and emphasise that more research on this topic is imperative. We provide suggestions on how the results herein may be used in future studies, to potentially reduce hypothetical bias that may be exhibited in SCE.@en

Introduction: Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature. Method: Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles. Results: The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity ” and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature. Conclusions: By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.@en

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.@en

Stated choice surveys have been used for several decades to estimate preferences of agents using choice models, and are widely applied in the transportation domain. Different types of experimental designs that underlie such surveys have been used in practice. In unlabelled experiments, where all alternatives are described by the same generic utility function, such designs may suffer from choice tasks containing a dominant alternative. Also in labelled experiments with alternative specific attributes and constants such dominance may occur, but to a lesser extent. We show that dominant alternatives are problematic because they affect scale and may bias parameter estimates. We propose a new measure based on minimum regret to calculate dominance and automatically detect such choice tasks in an experimental design or existing dataset. This measure is then used to define a new experimental design type that removes dominance and ensures the making of trade-offs between attributes. Finally, we propose a new regret-scaled multinomial logit model that takes the level of dominance within a choice task into account. Results using simulated and empirical data show that the presence of dominant alternatives can bias model estimates, but by making scale a function of a smooth approximation of normalised minimum regret we can properly account for scale differences without the need to remove choice tasks with dominant alternatives from the dataset.@en

Due to the surge in the amount of data that are being collected, analysts are increasingly faced with very large data sets. Estimation of sophisticated discrete choice models (such as Mixed Logit models) based on these typically large data sets can be computationally burdensome, or even infeasible. Hitherto, analysts tried to overcome these computational burdens by reverting to less computationally demanding choice models or by taking advantage of the increase in computational resources. In this paper we take a different approach: we develop a new method called Sampling of Observations (SoO) which scales down the size of the choice data set, prior to the estimation. More specifically, based on information-theoretic principles this method extracts a subset of observations from the data which is much smaller in volume than the original data set, yet produces statistically nearly identical results. We show that this method can be used to estimate sophisticated discrete choice models based on data sets that were originally too large to conduct sophisticated choice analysis.@en