E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands

Transportation - In recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to...     

Effects of task complexity and time pressure on activity-travel choices: heteroscedastic logit model and activity-travel simulator experiment

This paper derives, estimates and applies a discrete choice model of activity-travel behaviour that accommodates potential effects of task complexity and time pressure on decision-making. To the best of our knowledge, this is the first time that both factors (task complexity and time pressure) are jointly captured in a discrete choice model. More specifically, our heteroscedastic logit model captures potential impacts of task complexity and time pressure through the scale of the utility of activity-travel options. We collect data using a novel activity-travel simulator experiment that has been specifically designed with the aim of testing our model. Results are in line with expectations, in that higher levels of task complexity and time pressure are found to result in a smaller scale of utility. In other words, higher levels of task complexity and time pressure lead to more random choice behaviour and as a consequence to less pronounced differences in choice probabilities between alternatives. An empirical illustration suggests that choice probability-differences between models that do and those that do not capture these effects, can be very substantial; this in turn suggests that failing to capture the effects of task complexity and time pressure in discrete choice models of activity travel decision-making might lead to serious bias in forecasts of the effects of transport policies.     

Measuring user benefits of changes in the transport system when traveler awareness is limited

This paper derives and illustrates measures for the ex ante evaluation of user benefits associated with improvements in the transport system. In contrast with conventional analyses, we assume that awareness among travelers of changes in the transport system is limited and grows over time. Specifically, we postulate that each day (trip) provides travelers with an opportunity to learn about changes that occurred recently in the transport system. This learning process may involve learning through direct experience and indirect learning through for example social networks or information provision. Our measure of user benefits incorporates the conventional logsum-measure as a special case (when full awareness is assumed) and has a closed form solution under reasonable error term assumptions. A numerical illustration provides a first sign of face validity. We derive and discuss a number of practical implications, and discuss possible model extensions.     

An empirical comparison of travel choice models that capture preferences for compromise alternatives

Compromise alternatives have an intermediate performance on each or most attributes rather than having a poor performance on some attributes and a strong performance on others. The relative popularity of compromise alternatives among decision-makers has been convincingly established in a wide range of decision contexts, while being largely ignored in travel behavior research. We discuss three (travel) choice models that capture a potential preference for compromise alternatives. One approach, which is introduced in this paper, involves the construction of a so-called compromise variable which indicates to what extent (i.e., on how many attributes) a given alternative is a compromise alternative in its choice set. Another approach consists of the recently introduced random regret-model form, where the popularity of compromise alternatives emerges endogenously from the regret minimization-based decision rule. A third approach consists of the contextual concavity model, which is known for favoring compromise alternatives by means of a locally concave utility function. Estimation results on a stated route choice dataset show that, in terms of model fit and predictive ability, the contextual concavity and random regret models appear to perform better than the model that contains an added compromise variable.     

Risk aversion,regret aversion and travel choice inertia: an experimental study

This paper presents the results of an experimental study into the role of risk aversion and regret aversion as codeterminants of travel choice inertia. Theoretical results published by Chorus and Dellaert are tested empirically. More specifically, the expectation is tested that when (1) travelers are risk averse, (2) the quality of travel choices is uncertain, and (3) the quality is partially revealed upon usage, travel choice inertia emerges as a learning-based lock-in effect. In addition, this paper studies the role of regret aversion as a possible trigger of travel choice inertia. Analyses are based on data collected in an experiment, where the reward that participants obtain is a function of the outcome of choices they make. Empirical results suggest that the learning-based lock-in effect indeed plays a role in the context of our data. The evidence for the hypothesis that regret aversion triggers inertia is mixed at best.     

Use and Effects of Advanced Traveller Information Services (ATIS): A Review of the Literature

Abstract

Rapid technological developments in the field of personal communication services probe visions of a next generation in Advanced Traveller Information Services (ATIS). These technological developments provoke a renewed interest in the use and effect of such next‐generation ATIS among academia as well as practitioners. To understand better the potential use and effects of such next‐generation ATIS, a thorough review is warranted of contemporary conceptual ideas and empirical findings on the use of travel information (services) and their effects on travellers’ choices. This paper presents such a review and integrates behavioural determinants such as the role of decision strategies with manifest determinants such as trip contexts and socio‐economic variables into a coherent framework of information acquisition and its effect on travellers’ perceptions.     

Travelers’ compliance with social routing advice: evidence from SP and RP experiments

Transportation - This study examines to what extent travel information can be used to direct travelers to system-optimal routes that may be sub-optimal for them personally, but contribute to...     

Substantial Changes and Their Impact on Mobility: A Typology and an Overview of the Literature

During the past 40 years, mobility patterns have enduringly changed several times as a result of the occurrence of a number “substantial changes”. Examples of such substantial changes are the rapid emergence of affordable air travel, the oil crises, and profound ICT developments. To most researchers and policy-makers in transportation, it seems more than likely that the next 40 years will also witness a number of substantial changes, some of which might even have larger impacts on mobility than the ones described above. However, literature on substantial changes and their impact on mobility are difficult to access as it is spread across different research fields and suffers from ambiguous use of terminology. As a result, overview of the literature on substantial changes and their findings is missing and discussions on the impacts on future mobility of potential substantial changes are hampered. To overcome these problems, this paper (1) proposes a typology of substantial changes and (2) ties together and reviews the scholarly literature that has focused on identifying the impacts of past substantial changes on mobility patterns. In this paper, we show how the proposed typology on substantial changes can be applied to contemplate on substantial changes and on their impacts.     

Random regret minimization or random utility maximization: an exploratory analysis in the context of automobile fuel choice

Interest in alternative behavioural paradigms to random utility maximization (RUM) has existed ever since the dominance of the RUM formulation. One alternative is known as random regret minimization (RRM), which suggests that when choosing between alternatives, decision makers aim to minimize anticipated regret. Although the idea of regret is not new, its incorporation into the same discrete choice framework of RUM is very recent. This paper is the first to apply the RRM‐model framework to model choice amongst durable goods. Specifically, we estimate and compare the RRM and RUM models in a stated choice context of choosing amongst vehicles fuelled with petrol, diesel and hybrid (associated with specific levels of fuel efficiency and engine capacity). The RRM model is found to achieve a marginally better fit (using a non‐nested test of differences) than its equally parsimonious RUM counterpart. As a second contribution, we derive a formulation for regret‐based elasticities and compare utility‐based and regret‐based elasticities in the context of stated vehicle type choices. We find that in the context of our choice data, mean estimates of elasticities are different for many of the attributes and alternatives. Copyright © 2011 John Wiley & Sons, Ltd.     

Logsums for utility-maximizers and regret-minimizers,and their relation with desirability and satisfaction

This paper studies to what extent the Logsum-measure of user benefits relates to travelers’ perceptions of choice set-desirability and choice-satisfaction. Knowing these relations is important since researchers have recently started to analyze and interpret user benefits in general – and Logsums in specific – in terms of these more behaviorally oriented notions, rather than in terms of expected utility. Participants to a stated route choice experiment were asked to indicate, after each choice made, to what extent they considered the choice set to be desirable, or to what extent they were satisfied with the chosen alternative. These measurements were correlated with Logsums that were computed for each choice situation. In addition, the paper derives a regret-based Logsum (which gives the expected regret of a choice situation) and presents a comparison with its utilitarian counterpart. Also for this regret-based Logsum, correlations with desirability- and satisfaction-ratings are computed. As a general finding, it appears that all computed correlations are rather weak. This suggests that, at least in the context of our data, the utility-based Logsum and its regret-based counterpart appear to have only a fairly weak connection with the behavioral notions of choice set-desirability and choice-satisfaction.