The acquisition of pre-trip information: A stated preference approach

This paper describes a study into the effects of pre-trip information on travel behaviour, carried out as part of the DRIVE project EURONETT. The aim of the study was to investigate travellers' requirements for different types of travel information and methods of enquiry and to relate the process of information acquisition to changes in travel behaviour. The study was carried out using a stated preference approach, built on the use of a microcomputer based simulation of an in-home pre-trip information system offering information on travel times from home to City Centre, by bus and car, at different times of the day. A novel feature of the stated preference exercise was that respondents effectively generated their own choice set of alternatives through the process of information acquisition. Surveys were undertaken in parallel in Birmingham and Athens, thus allowing a comparison to be made between behaviour in typical Southern and Northern European settings.The first part of the paper discusses some of the fundamental behavioural and modelling issues raised by the introduction of advanced traveller information systems. It then describes the study methodology and the stated preference experiment. Results are presented from an analysis of the information acquisition process itself and from choice models relating the acquired information to effects on different dimensions of travel behaviour.     

Modeling Travel Time Under ATIS Using Mixed Linear Models

The objective of this paper is to model travel time when drivers are equipped with pre-trip and/or en-route real-time traffic information/advice. A travel simulator with a realistic network and real historical congestion levels was used as a data collection tool. The network included 40 links and 25 nodes. This paper presents models of the origin-to-destination travel time and en-route short-term route (link) travel time under five different types and levels of advanced traveler information systems (ATIS). Mixed linear models with the repeated observation's technique were used in both models. Different covariance structures (including the independent case) were developed and compared. The effect of correlation was found significant in both models. The trip travel time analysis showed that as the level of information increases (adding en-route to the pre-trip and advice to the advice-free information), the average travel time decreases. The model estimates show that providing pre-trip and en-route traffic information with advice could result in significant savings in the overall travel time. The en-route short-term (link) travel time analysis showed that the en-route short-term (link) information has a good chance of being used and followed. The short-term qualitative information is more likely to be used than quantitative information. Learning and being familiar with the system that provides the information decreases en-route short-term delay.     

Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function

Advanced Traveler Information Systems (ATIS) provide travelers with real time traffic information to optimize their travel choices. The objective of this paper is to model drivers' diversion from their normal routes in the provision of ATIS. Five different scenarios of traffic information are used. Generalized Estimating Equations (GEE) framework with repeated observations and binomial probit link function is introduced and implemented. GEE with four different correlation structures including the independent case are developed and compared with each other and with regular Maximum Likelihood Estimation (MLE). A travel simulator was used. Sixty-five subjects have traveled 10 simulated trial days each on a 40-link realistic network with real historical congestion levels. The results showed that providing traffic information increases the probability of drivers' diversion from their normal routes. Adding advice to the pre-trip and/or en-route information encourages drivers to divert. Providing en-route in addition to the pre-trip information with or without advice increases the diversion probability. High travel time on the normal route and less travel time on the diverted route increase the probability of diversion. High-educated drivers are less likely to divert. Expressway users are more likely to divert from their normal routes under ATIS. Drivers' familiarity with the device that provides the information and high number of traffic signals on the normal route increase the diversion probability.     

Transition choice probabilities and welfare analysis in random utility models with imperfect before–after correlation

Welfare in random utility models is used to be analysed on the basis of only the expectation of the compensating variation. De Palma and Kilani (De Palma, A., Kilani, K., 2011. Transition choice probabilities and welfare analysis in additive random utility models. Economic Theory 46(3), 427–454) have developed a framework for conditional welfare analysis which provides analytic expressions of transition choice probabilities and associated welfare measures. The contribution is of practical relevance in transportation because it allows to compute shares of shifters and non-shifters and attribute benefits to them in a rigorous way. In De Palma and Kilani (2011) the usual assumption of unchanged random terms before and after is made.The paper generalises the framework for conditional welfare analysis to cases of imperfect before–after association of the random terms. The joint before–after distribution of the random terms is introduced with postulated properties in terms of marginal distributions and covariance matrix. Analytic expressions, based on the probability density function and the cumulative distribution function of the joint before–after distribution, and simulation procedures for computation of the transition choice probabilities and the conditional expectations of the compensating variation are provided. Results are specialised for multinomial logit and probit. In the case without income effects, it is proved that the unconditional expectation of the compensating variation depends only on the marginal distributions.The theory is illustrated by a numerical example which refers to a multinomial logit applied to the choice of the transport mode with two specifications, one without and one with income effects. Results show that transition probabilities and conditional welfare measures are affected significantly by the assumption on the before–after correlation. The variability in the transition probabilities across transitions tends to decrease as the before–after correlation decreases. In the extreme case of independent random terms, the conditional expectations of the compensating variation tend to be close to the unconditional expectation.     

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall.     

A composite attitude-behavior model of traveler decision making

A general model for the behavioral analysis of traveler decision making is described. The model is one which blends research in human information processing with probabilistic choice theory. The model is used as a basis for discussing two studies of grocery store choice behavior. In these studies, algebraic models of individuals' grocery store preferences which are derived using controlled hypothetical choice situations are related to real-world choice behavior. Results show a high monotonic correspondence between laboratory and real-world behavior. Implications of the research for existing approaches to behavioral modeling are described.     

A GA-based household scheduler

One way of making activity-based travel analysis operational for transport planning is multi-agent micro-simulation. Modelling activity and trip generation based on individual and social characteristics are central steps in this method. The model presented here generates complete daily activity schedules based on the structure of a household and its members’ activity calendars. The model assumes that the household is another basic decision-making unit for travel demand aside from individual mobility needs. Results of the model are schedules containing complete information about activity type and sequence, locations, and means of transportation, as well as activity start times and durations. The generated schedules are the outcome of a probabilistic optimisation using genetic algorithms. This iterative method improves solutions found in a random search according to the specification of a fitness criterion, which equals utility here. It contains behavioural assumptions about individuals as well as the household level. Individual utility is derived from the number of activities and their respective durations. It is reduced by costs of travelling and penalties for late, respectively early arrival. The household level is represented directly by the utility of joint activities, and indirectly by allocation of activities and means of transportation to household members. The paper presents initial tests with a three-person household, detailing resulting schedules, and discussing run-time experiences. A sensitivity analysis of the joint utility parameter impact is also included.     

Multi-state supernetwork framework for the two-person joint travel problem

Most travel behavior studies on route and mode choice focus only on an individual level. This paper adopts the concept of multi-state supernetworks to model the two-person joint travel problem (JTP). Travel is differentiated in terms of activity-vehicle-joint states, i.e. travel separately or jointly with which transport mode and with which activities conducted. In each state, route choice can be addressed given the state information and travel preference parameters. The joint travel pattern space is represented as a multi-state supernetwork, which is constructed by assigning the individual and joint networks to all possible states and connecting them via transfer links at joints where individuals can meet or depart. Besides route choice, the choices of where and when to meet, and which transport mode(s) to use can all be explicitly represented in a consistent fashion. A joint path through the supernetwork corresponds to a specific joint travel pattern. Then, JTP is reduced to an optimization problem to find the joint path with the minimum disutility. Three standard shortest path algorithm variants are proposed to find the optimal under different scenarios. The proposed framework further indicates the feasibility of multi-state supernetworks for addressing high dimensional problems and contributes to the design of a next generation of joint routing systems.     

Investigating the effect of travel time variability on drivers' route choice decisions in Shanghai,China

Abstract

This paper investigates the effect of travel time variability on drivers' route choice behavior in the context of Shanghai, China. A stated preference survey is conducted to collect drivers' hypothetical choice between two alternative routes with designated unequal travel time and travel time variability. A binary choice model is developed to quantify trade-offs between travel time and travel time variability across various types of drivers. In the model, travel time and travel time variability are, respectively, measured by expectation and standard deviation of random travel time. The model shows that travel time and travel time variability on a route exert similarly negative effects on drivers' route choice behavior. In particular, it is found that middle-age drivers are more sensitive to travel time variability and less likely to choose a route with travel time uncertainty than younger and elder drivers. In addition, it is shown that taxi drivers are more sensitive to travel time and more inclined to choose a route with less travel time. Drivers with rich driving experience are less likely to choose a route with travel time uncertainty.     

Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: A stated preference approach

The paper presents valuing of qualitative and quantitative travel attributes influencing the attractiveness of suburban train service in Mumbai city, India. A stated preference experiment is designed to capture the data of sub-urban train mode choice behavior. The behavioral data are analyzed using different modeling techniques such as multinomial logit (MNL) and mixed logit (ML). In ML model, the random parameters are assumed to follow constrained triangular distribution, where mean equals its spread. The decomposition of preference heterogeneity around the mean estimate of random parameter is also investigated using ML model. The study shows the influence of headway time and train ride time associated with a particular crowding level (expressed in density of standing passengers/m²) in choosing the sub-urban train mode by calculating their willingness-to-pay (WTP) values and highlights the importance of WTP for addressing policy issues in the reduction of in-vehicle crowding level. The present study documents new findings of the effect of crowding level on train ride time in the context of a developing country and suggests some important directions for future suburban train transport crowding valuation research.     

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.     

On the flexibility of using marginal distribution choice models in traffic equilibrium

Traffic equilibrium models are fundamental to the analysis of transportation systems. The stochastic user equilibrium (SUE) model which relaxes the perfect information assumption of the deterministic user equilibrium is one such model. The aim of this paper is to develop a new user equilibrium model, namely the MDM-SUE model, that uses the marginal distribution model (MDM) as the underlying route choice model. In this choice model, the marginal distributions of the path utilities are specified but the joint distribution is not. By focusing on the joint distribution that maximizes expected utility, we show that MDM-SUE exists and is unique under mild assumptions on the marginal distributions. We develop a convex optimization formulation for the MDM-SUE. For specific choices of marginal distributions, the MDM-SUE model recreates the optimization formulation of logit SUE and weibit SUE. Moreover, the model is flexible since it can capture perception variance scaling at the route level and allows for modeling different user preferences by allowing for skewed distributions and heavy tailed distributions. The model can also be generalized to incorporate bounded support distributions and discrete distributions which allows to distinguish between used and unused routes within the SUE framework. We adapt the method of successive averages to develop an efficient approach to compute MDM-SUE traffic flows. In our numerical experiments, we test the ability of MDM-SUE to relax the assumption that the error terms are independently and identically distributed random variables as in the logit models and study the additional modeling flexibility that MDM-SUE provides on small-sized networks as well as on the large network of the city of Winnipeg. The results indicate that the model provides both modeling flexibility and computational tractability in traffic equilibrium.     

Testing probabilistic discrete choice models of travel demand by comparing predicted and observed aggregate choice shares

Probabilistic discrete choice models of travel demand often are tested for the presence of specification errors by comparing the models' predictions of aggregate choice shares in population strata with observed shares. A model is rejected as misspecified if the differences between its predictions and the observations are judged too large. This judgement usually is made on intuitive grounds without use of formal statistical methods and, therefore includes no systematic method for distinguishing the effects of specification errors on differences between predictions and observations from those of random sampling errors. This paper represents formal statistical tests for comparing predicted and observed aggregate chioce shares in population strata and reports the results of an investigation of the power of the tests. The test statistics are asymptotically χ² disturbed when the model being tested is correctly specified. The results of the power investigation suggests that greater power is obtained (i.e. there is ability to detect misspecified models) when all of the available data are used for both parameter estimation and specification testing than when the available data are divided into separate estimation and test data sets. Specification tests based on comparisons of predicted and observed aggregate choice shares appear to have less power than do likelihood ratio and likelihood ratio index specification tests when the alternative models required by the latter tests are correctly or approximately correctly specified. However, tests based on comparisons of predicted and observed shares ca have greater power than the other tests when the alternative models are seriouslymisspecified.     

Network impacts of a road capacity reduction: Empirical analysis and model predictions

In spite of their widespread use in policy design and evaluation, relatively little evidence has been reported on how well traffic equilibrium models predict real network impacts. Here we present what we believe to be the first paper that together analyses the explicit impacts on observed route choice of an actual network intervention and compares this with the before-and-after predictions of a network equilibrium model. The analysis is based on the findings of an empirical study of the travel time and route choice impacts of a road capacity reduction. Time-stamped, partial licence plates were recorded across a series of locations, over a period of days both with and without the capacity reduction, and the data were ‘matched’ between locations using special-purpose statistical methods. Hypothesis tests were used to identify statistically significant changes in travel times and route choice, between the periods of days with and without the capacity reduction. A traffic network equilibrium model was then independently applied to the same scenarios, and its predictions compared with the empirical findings. From a comparison of route choice patterns, a particularly influential spatial effect was revealed of the parameter specifying the relative values of distance and travel time assumed in the generalised cost equations. When this parameter was ‘fitted’ to the data without the capacity reduction, the network model broadly predicted the route choice impacts of the capacity reduction, but with other values it was seen to perform poorly. The paper concludes by discussing the wider practical and research implications of the study’s findings.     

Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand

Random utility models are undoubtedly the most used models for the simulation of transport demand. These models simulate the choice of a decision-maker among a set of feasible alternatives and their operational use requires that the analyst is able to correctly specify this choice-set for each individual.Some early applications basically ignored this problem by assuming that all decision-makers chose from the same pre-specified choice-set. This assumption may be unrealistic in many practical cases and cause significant misspecification problems (P. Stopher, Transportation Journal of ASCE 106 (1980) 427; H. Williams, J. Ortuzar, Transportation Research B 16 (1982) 167).The problem of choice-set simulation has been dealt within the literature following two basically different approaches:

• •simulating the perception/availability of an alternative implicitly in the choice model,
• •simulating the choice-set generation explicitly in a separate model.

The implicit approach is more convenient from an operational point of view, while the explicit one is more appealing from a theoretical point of view.In this paper, a different approach to the modeling of availability/perception of alternatives in the context of random utility model is proposed. This approach is based on the concept of intermediate degrees of availability/perception of each alternative simulated through a model (or “inclusion function”) which in turn is introduced in the systematic utility of standard random utility models.This model, named implicit availability/perception (IAP), may be differently specified depending on assumptions made on the joint distribution of random residuals and the way in which the average degree of availability/perception is modeled.In this paper, a possible specification of the IAP model, based on the assumption of random residual distributed as i.i. Gumbel and with the average degree of availability/perception modeled as a binomial logit, is proposed.The paper also proposes ML estimation models in two cases: in the first, only information on alternatives choices is available, while in the second, this information is complemented with others on variables related to a latent (i.e., non-observable) alternatives availability/perception degree (e.g., information on car availability of decision-maker i used as an indirect measurement of the unknown and non-observable availability/perception degree of alternative car for decision-maker i in a modal split).The proposed specification is tested on mode choice data; the calibration results are compared with those of a similar logit specification with encouraging results in terms of goodness of fit.     

A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices

Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However, such models need to take into account self-selection effects in residential location choice, wherein households choose to reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon, well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copula-based framework that facilitates the estimation of joint equations systems with error dependence structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency among the choice dimensions and that self-selection effects cannot be ignored when modeling land use-travel behavior interactions.     

The transaction cost approach to modeling the demand for automobiles

The purchase of an automobile involves significant transaction costs in addition to the purchase price. Therefore, the assumptions implied by static car holding models are invalid. This paper describes a dynamic approach to the modeling of level-of-ownership and auto-type choice, based on a transaction choice model which utilizes information on past car ownership. The cost or disutility of a transaction depends on the attributes of the household and the purchased car, as well as on past car-ownership characteristics. A set of assumptions underlying the incorporation of transaction costs in the model is presented. The paper discusses the econometric implications of omitting the dynamic attributes (i.e., past ownership characteristics). A disaggregate model was estimated, using a choice-based sample consisting of a random sample of households enriched with a sample of households which transacted in the car market during the study period. This sampling method combined with a random choice of a subset of the car alternatives provides for a cost-effective method to estimate a transaction model.     

Investigating the joint choice behavior of intercity transport mode and high‐speed rail cabin with a strategy map

This paper investigates the joint choice behavior of intercity transport modes and high‐speed rail cabin class within a two‐dimensional choice structure. Although numerous studies have been conducted on the mode choice behavior, little is known about the influence of cabin class on their intercity traveling choice. Hence, this study is conducted with a revealed preference survey to investigate the intercity traveling behavior for the western corridor of Taiwan. The results of nested logit model reveal that a cabin strategy has a more significant influence on cabin choice than on mode choice. Furthermore, this study proposes a new strategy map concept to assist transport operators in defining and implementing their pricing strategies. The results suggest that to capture a higher market share, high‐speed rail operators should choose an active price reduction strategy, while bus and rail operators are advised to implement a passive price increase strategy to raise unit revenue. Copyright © 2014 John Wiley & Sons, Ltd.