Value of time – A citizen perspective

The dominant empirical approach to infer Value of Time is based on experiments in which respondents are typically asked to make hypothetical travel choices as if they were paying travel costs from their own budget, in exchange for personal travel time gains. However, many scholars have argued that such travel choice decisions of individuals in their role of consumer of mobility are likely to be a poor proxy of how they in their role of citizen believe government should spend tax money to generate travel time gains for large numbers of travelers. So far, this possible deviation between what we call ‘consumer VoT’ and ‘citizen VoT’ has not been studied empirically. In this paper, we fill this gap, by designing a Stated Choice experiment with eight different frames; some representing a typical consumer choice situation, others gradually approaching a citizen perspective. We find that individuals’ willingness to pay from previously collected tax money for travel time gains created by a government policy, is significantly higher than their willingness to pay, from their after tax income, for time gains obtained by choosing a different route. This result implies that citizen VoT is higher than consumer VoT. This difference does not stem from a stronger willingness to spend previously collected tax money compared to spending one’s own income, but from a difference in the value attached to travel gains: a travel time gain resulting from government action is valued more than the same travel time gain obtained by one’s own route choices. This and a range of other empirical results are discussed in depth, in light of the conceptual differences between preferences of individuals in a role of consumer or citizen.     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

A long panel survey to elicit variation in preferences and attitudes in the choice of electric vehicles

This paper describes the methodology we set up to gather appropriate data to study the impact of real life experience with electric vehicles (EVs) over a relatively long period of time on individual preferences and attitudes. We used stated choices (SC) to elicit individual preferences because EVs and their associated charging infrastructure are not yet fully integrated onto the market. Furthermore, to measure the extent to which the experience of using an EV may affect individual preferences and attitudes, we set up a “long panel” survey, where data was gathered before and after individuals experienced an EV in real life during a three-month period. We also measured attitudinal effects (AE) that might affect the choice of an EV by individuals. To our knowledge, this represents the first example of a “long panel” SC/AE and the first attempt to measure the formation of preferences and attitudes for this emerging product. Our results show that preferences and attitudes are indeed affected by real life experience. In the SC experiment, the respondents only chose the EV half as often as compared to the situation where they had not yet tried it. Furthermore, we measured a change in attitude for statements regarding the use of EVs. On the whole, respondents got a more positive view of the EV driving performance and this change is significantly greater for women than for men. However, respondents expressed more concern about being able to maintain current mobility with an EV. The data gathered in this survey should also serve to analyse the changes generated by direct experience with EVs, and eventually to formulate and estimate advanced discrete choice models that allow insights into factors relevant for improved understanding of market behaviour.     

The accuracy of proxy responses in a stated choice setting: A re-examination and some controversial conclusions

Data is typically gathered from an individual respondent who represents the group or the household. This individual is often identified as the “primary decision maker” and is asked to provide responses as a proxy for the group given that the cost of interviewing each member individually is impractical and/or expensive. The collection of joint preferences is rarely undertaken, with the use of proxy responses not uncommon in travel behaviour research. Under such a framework, there exists an assumption that the primary decision maker has perfect knowledge of other group member preferences, and bargaining behaviour, and is able to synthesise this information when providing a response on their behalf. The validity of such an assumption however remains an open question, with recent research calling the reliability of proxy responses into account (Bateman and Munro, 2009). In this paper, using three models estimated in willingness to pay space, we examine the accuracy of proxy responses in a stated choice experiment. We find that there is overlap between a proxy response and the own preferences of the individual providing the proxy choice, but while the proxy responses fail to represent the full preference heterogeneity that exists in the actual choices made by individuals, the proxy responses in aggregate provide a suitable replacement for actual data, subject to a number of caveats.     

Understanding variability,habit and the effect of long period activity plan in modal choices: a day to day,week to week analysis on panel data

Understanding variability in individual behaviour is crucial for the comprehension of travel patterns and for the development and evaluation of planning policies. But, with only one notable exception, there are no studies on the intrinsic variability in the individual preferences for mode choices in absence of external changes in the transport infrastructures. This requires using continuous panel data. Few papers have studied mode choice with continuous panel data but mainly focused on the panel correlation. In this work we use a six-week travel diary survey to study the intrinsic variability in the individual preferences for mode choices, the effect of long period plans and habitual behaviour in the daily mode choices. Mixed logit models are estimated that account for the above effects as well as for systematic and random heterogeneity over individual preferences and responses. We also account for correlation over several time periods. Our results suggest that individual tastes for time and cost are fairly stable but there is a significant systematic and random heterogeneity around these mean values and in the preferences for the different alternatives. We found that there is a strong inertia effect in mode choice that increases with (or is reinforced by) the number of time the same tour is repeated. The sequence of mode choice made is influenced by the duration of the activity and the weekly structure of the activities     

A non-compensatory model of transportation behavior based on sequential consideration of attributes

The proposed model of travel choice behavior is based upon an assumption that individuals compare their choice alternatives on a series of attributes ordered in terms of importance; they eliminate from consideration those alternatives which do not meet their expectation on one or more of the characteristics. The process is repeated with adjusted levels of expectation until only one alternative remains. The model thus incorporates a number of psychological decision axioms which have seldom been applied in models aimed at providing transportation planners with useful information from consumer survey data.Estimates of parameters defining distributions of expectation levels in a population of travelers are generated using a nonlinear optimization technique. The technique is demonstrated to provide estimates which replicate well the choices of travelers in two different contexts: choice of hypothetical concepts of small urban vehicles and choice of destination for shopping trips within an urban area.     

Latent temporal preferences: An application to airline travel

An essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.     

Heterogeneity assumptions in the specification of bargaining models: a study of household level trade-offs between commuting time and salary

With many real world decisions being made in conjunction with other decision makers, or single agent decisions having an influence on other members of the decision maker’s immediate entourage, there is strong interest in studying the relative weight assigned to different agents in such contexts. In the present paper, we focus on the case of one member of a two person household being asked to make choices affecting the travel time and salary of both members. We highlight the presence of significant heterogeneity across individuals not just in their underlying sensitivities, but also in the relative weight they assign to their partner, and show how this weight varies across attributes. This is in contrast to existing work which uses weights assigned to individual agents at the level of the overall utility rather than for individual attributes. We also show clear evidence of a risk of confounding between heterogeneity in marginal sensitivities and heterogeneity in the weights assigned to each member. We show how this can lead to misleading model results, and argue that this may also explain past results showing bargaining or weight parameters outside the usual [0,1] range in more traditional joint decision making contexts. In terms of substantive results, we find that male respondents place more weight on their partner’s travel time, while female respondents place more weight on their partner’s salary.     

Differences in probability weighting for individual travelers: a managed lane choice application

Using a 2012 stated preference survey based on a traveler’s most recent actual trip, this study predicts traveler choices between general purpose lanes and managed lanes for a freeway in Houston, Texas. The choice model incorporates probability weighting for risky travel times. The results indicate significant improvement in predicative power over a model that excludes weighting, confirming non-linearity in the probability weighting function. The maximum value of time (VOT) measures calculated in this study are lower than estimated in many previous route choice studies. This highlights the importance of incorporating individual weights for travel risks. Travelers’ underweighting of travel time risks would help explain the lower VOTs found in our study because respondents consider route choice decision-making as a gamble, but assign their own probabilities of occurrence to arriving at their destination on time, late, or early. We find that traveler groups are heterogeneous and the different weights developed for different groups of travelers can be used to better understand their probabilities. Segmentation analysis indicates that Age may serve to proxy the effects of more experience over time, or changing driving abilities, or changes in one’s sense of optimism or pessimism at different ages. Gender and Income also play a role in how the objective probabilities presented to respondents were translated into subjective probabilities.     

An activity-based approach of investigating travel behaviour of older people

The paper presents the results of an investigation on daily activity-travel scheduling behaviour of older people by using an advanced econometric model and a household travel survey, collected in the National Capital Region (NCR) of Canada in 2011. The activity-travel scheduling model considers a dynamic time–space constrained scheduling process. The key contribution of the paper is to reveal daily activity-travel scheduling behaviour through a comprehensive econometric framework. The resulting empirical model reveals many behavioural details. These include the role that income plays in moderating out-of-home time expenditure choices of older people. Older people in the highest and lowest income categories tend to have lower variations in time expenditure choices than those in middle-income categories. Overall, the time expenditure choices become more stable with increasing age, indicating that longer activity durations and lower activity frequency become more prevalent with increasing age. Daily activity type and location choices reveal a clear random utility-maximizing rational behaviour of older people. It is clear that increasing spatial accessibility to various activity locations is a crucial factor in defining daily out-of-home activity participation of older people. It is also clear that the diversity of out-of-home activity type choices reduces with increasing age and older people are more sensitive to auto travel time than to transit or non-motorized travel time.     

Does the income elasticity of road traffic depend on the source of income?

An extensive body of literature addresses the income elasticity of road traffic, in which income is typically treated as a homogenous quantity. Here we report evidence of heterogeneity in cross-sectional estimates of the elasticity of vehicle-kilometres of travel (VKT) with respect to income, when household income is disaggregated on the basis of income source.The results are generally intuitive, and show that the cross-sectional income elasticity of road traffic is not homogeneous as is typically specified in transport planning models. We show that in a number of circumstances the cross-sectional elasticity with respect to aggregate household income is of the opposite sign in comparison to more refined estimates of elasticity disaggregated by income source. If further research confirms that the elasticities we report here are causal in nature, neglecting the elemental effects could result in misleading results affecting practical infrastructure-investment and policy decisions, particularly as the mix of income sources shifts (e.g. if, as society ages, pension income increases as a share of all income).These results are of interest to both researchers and forecasters of travel demand, as well as designers of future travel survey instruments; the latter group must decide how to generate data about respondents’ income. Current expert guidance is to collect a single estimate of aggregate income at the household level. Future travel survey design choices will bound the analyses that can be supported by the resulting survey data, and therefore methodological research to re-visit the trade-offs associated with such choices is warranted.     

Travel,transport and energy implications of university-related student travel: A case study approach

This study highlighted significant cultural differences and complexity in travel behaviour associated with travel to university across the UK and Ireland. This paper examines university travel behaviours and the implications for emissions, across the 2012–2013 academic year, based on responses from 1049 students across 17 universities in Ireland and the UK. Surveys were analysed to examine the trips of students both during term time and when accessing the universities each year. The data analysis in this paper examines three aspects of the transport implications of travel to and from university. Firstly the journey between university and term time address (or permanent address if the respondent does not have a separate term time address), secondly the journey between the university area and a separate permanent address where relevant; and thirdly implications for emissions resulting from university-related travel.The study found that student car users were more likely to be female, older students, or studying part time; male students were more likely to use active modes. The study indicated interesting differences between students living in different parts of the UK and Ireland. For example, it was found that there was a higher level of car dependence amongst Northern Irish students compared to other areas; and a greater variability in travel distances in Scotland and Northern Ireland. In England, car use was more pronounced when students travelled from their permanent address to term time address, and, as in Ireland, there was evidence of more car sharing on such trips. Public transport usage was more pronounced amongst Scottish students. The effect of these transport choices on emissions is significant and demonstrates the importance of education related trips to the development of a transport policy response. The analysis shows that annual emissions are highest for regular travel to and from university when a student has a permanent address rather than a separate term time and permanent address.     

Preferences for travel time under risk and ambiguity: Implications in path selection and network equilibrium

In this paper, we study the preferences for uncertain travel times in which probability distributions may not be fully characterized. In evaluating an uncertain travel time, we explicitly distinguish between risk, where the probability distribution is precisely known, and ambiguity, where it is not. In particular, we propose a new criterion called ambiguity-aware CARA travel time (ACT) for evaluating uncertain travel times under various attitudes of risk and ambiguity, which is a preference based on blending the Hurwicz criterion and Constant Absolute Risk Aversion (CARA). More importantly, we show that when the uncertain link travel times are independently distributed, finding the path that minimizes travel time under the ACT criterion is essentially a shortest path problem. We also study the implications on Network Equilibrium (NE) model where travelers on the traffic network are characterized by their knowledge of the network uncertainty as well as their risk and ambiguity attitudes under the ACT. We derive and analyze the existence and uniqueness of solutions under NE. Finally, we obtain the Price of Anarchy that characterizes the inefficiency of this new equilibrium. The computational study suggests that as uncertainty increases, the influence of selfishness on inefficiency diminishes.     

Interactive stated choice surveys: a study of air travel behaviour

Stated preference (SP) experiments are becoming an increasingly popular survey methodology for investigating travel behaviour. Nevertheless, some evidence suggests that SP experiments do not mirror decisions in real markets. With an increasing number of real world decisions made using the internet, an opportunity exists to improve the realism of the SP counterparts of such choices by aligning the choice environment with such online portals. In this paper, we illustrate the benefits of such an approach in the context of air travel surveys. Our survey is modelled on the interface and functionality of an online travel agent (OTA). As with a real OTA, many ticket options are presented. Sort tools allow the options to be reordered, search tools allow options to be removed from consideration, and a further tool allows attributes to be hidden and shown. Extensive use of these tools is made by the 462 respondents. A traditional SP component was also completed by the respondents. Our exploratory analysis as well as random utility model estimation results confirm not only that respondents seem to engage more actively with the interactive survey, but also that the resulting data allows for better performance in model estimation compared to a more conventional SP experiment. These results have implications for the study of other complex travel choices where interactive surveys may similarly be preferable to standard approaches.     

A model for integrated analysis of household location and travel choices

We develop a model for integrated analysis of household location and travel choices and investigate it from a theoretical point of view.Each household makes a joint choice of location (zone and house type) and a travel pattern that maximizes utility subject to budget and time constraints. Prices for housing are calculated so that demand equals supply in each submarket. The travel pattern consists of a set of expected trip frequencies to different destinations with different modes. The joint time and budget constraints ensure that time and cost sensitivities are consistent throughout the model. Choosing the entire travel pattern at once, as opposed to treating travel decisions as a series of isolated choices, allows the marginal utilities of trips to depend on which other trips are made.When choosing trip frequencies to destinations, households are assumed to prefer variation to an extent varying with the purpose of the trip. The travel pattern will tend to be more evenly distributed across trip ends the less similar destinations and individual preferences are. These heterogeneities of destinations and individual preferences, respectively, are expressed in terms of a set of parameters to be estimated.     

A Time-use Model for the Automated Vehicle-era

Automated Vehicles (AVs) offer their users a possibility to perform new non-driving activities while being on the way. The effects of this opportunity on travel choices and travel demand have mostly been conceptualised and modelled via a reduced penalty associated with (in-vehicle) travel time. This approach invariably leads to a prediction of more car-travel. However, we argue that reductions in the size of the travel time penalty are only a crude proxy for the variety of changes in time-use and travel patterns that are likely to occur at the advent of AVs. For example, performing activities in an AV can save time and in this way enable the execution of other activities within a day. Activities in an AV may also eliminate or generate a need for some other activities and travel. This may lead to an increase, or decrease in travel time, depending on the traveller’s preferences, schedule, and local accessibility. Neglecting these dynamics is likely to bias forecasts of travel demand and travel behaviour in the AV-era. In this paper, we present an optimisation model which rigorously captures the time-use effects of travellers’ ability to perform on-board activities. Using a series of worked out examples, we test the face validity of the model and demonstrate how it can be used to predict travel choices in the AV-era.     

A review of separate and joint strategies for the use of data on revealed and stated choices

This paper examines recent developments in household travel surveys that collect data for use in transportation planning and modeling efforts. The paper then introduces the notion of a total survey design and elaborates on what is meant by this concept. This is done first in the context of standard surveys of revealed choices. The paper discusses aspects of content and respondent burden and illustrates the potential to reduce respondent burden through careful consideration of content, question design, and question ordering. The paper also explores some issues of survey "friendliness" particularly with respect to activity surveys versus time-use surveys, with some observations about the potential of time-use surveys to eliminate some of the burden and content problems of previous diary designs. The remainder of the paper concentrates on the issue of collecting stated-response data and examines two alternative methods for collection: simultaneous collection of the contextual information and "on-the-fly" development of the alternatives for the stated-response questions, or sequential collection of contextual data and development of the stated-response questions. The paper also addresses issues of respondent burden that arise in the administration of stated-response surveys. The paper concludes with exploration of some of the reasons for collecting stated-response data, with particular emphasis on the US situation. In conclusion, the paper stresses again the need for a total design concept for collection of stated-response data, as well as for the simpler collection of more standard revealed choice data.     

Effect of traffic information on commuters' propensity to change route and departure time

Considerable public and private resources are devoted to the collection and dissemination of real-time traffic information in the Chicago area. Such information is intended to help individuals make more informed travel decisions, yet its effect on behavior remains largely unexplored. This study evaluates the effect of traffic information on travelers' route and departure time changes and provides a stronger basis for developing advanced information systems. Downtown Chicago automobile commuters were surveyed during the AM peak period. The results indicate that a majority of the respondents access, use and respond to information. For example, individuals use travel information to reduce their anxiety—even if they do not change travel decisions; this indicates that information may have “intrinsic” value. That is, simply knowing traffic conditions is valued by travelers. More than 60% of the respondents had used traffic information to modify their travel decisions. Multivariate analysis using the ordered probit model showed that individuals were more likely to use traffic reports for their route changes if they perceived traffic reports to be accurate and timely, and frequently listened to traffic reports. Respondents were more likely to change their departure times if they perceived traffic reports to be accurate and relevant, and frequently listened to traffic reports. The implication for Advanced Traveler Information Systems are that they may be designed to support both enroute and pre-trip decisions. ATIS performance, measured in terms of accuracy, relevance and timeliness would be critical in the success of such systems. Further, near-term prediction of traffic conditions on congested and unreliable routes (where conditions change rapidly) and incident durations is desirable.     

A structural and empirical model of subsistence activity behavior and income

This paper develops a structural and empirical model of subsistence activity behavior and income. Subsistence activity decisions (work participation and hours of work decisions) and income have an important bearing on activity and travel behavior of individuals. The proposed structural model represents an effort to analyze subsistence activity behavior and income earnings to support a better understanding, and reliable forecasting, of individual travel behavior. The empirical model formulates and estimates an integrated model of employment, hours of work and income which takes account of interdependencies among these choices and their structural relationships with other relevant variables. Social factors that inhibit an individual's employment and work hours decision and affect an individual's income are incorporated in the model. A sample of households from the Dutch National Mobility Panel is used in the empirical analysis.