A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.     

Noisy demand and mode choice

Mode choice under stochastically varying demand is studied via a dynamic mathematical model which describes the behavioural interactions between population groups. The model is developed by assuming competing attractivity functions for automobile and public transit which motivate their use subject to an overall demand for transportation. When this demand is allowed to vary stochastically, a set of stochastic differential equations describing the model are obtained. These are solved for their steady-state values. It is found that noisy demand can structure the system qualitatively differently than when the demand is fixed. The noise is found to generally reduce the level of public transit ridership, but it also changes the values of the threshold at which new regimes occur and, most interestingly, it induces new steady-state solutions for ridership at critical values of the variance of demand. In the latter case, noise becomes a source of new possibilities in the system by triggering a steady-state solution not present in the noise-free environment.     

Specification,transferability and the effect of data outliers in modeling the choice of mode in urban travel

Three problems of great importance to urban travel demand modeling using multinominal logit models are examined in this paper. They are (1) the effect of data outliers on model coefficients; (2) the effect of model specification on coefficients and model explanatory power; and (3) the transferability of model coefficients within the region, between regions, and in time.Four data sets are used in the study. They are: Washington, D.C., Minneapolis-St. Paul, and two data sets from the San Francisco Bay Area, Pre-BART and Post-BART. The data are standard home-interview survey data appended with network supplied modal travel cost and time information.The findings of the research are occasionally contradictory; the majority of the evidence supports the following conclusions. The outliers do not have a statistically significant effect (at 0.05 level) on the coefficients; however, the outliers can have a substantial effect on the point estimates of some of the coefficients. Model specification has an impact on model coefficients and model explanatory power. In particular, the definition of out-of-vehicle travel time appears to be important and, if available, the use of separate walk and wait times is preferred over their sum, the out-of-vehicle time. Finally, the model coefficients do not appear transferable within region, between regions, or in time.Research was supported in part by the Alfred P. Sloan Foundation, through grant 74-12-8 to the Department of Economics, University of California, Berkeley and by the National Science Foundation, through grant APR 74-20392, Research Applied to National Needs Program, to the University of California, Berkeley.     

Modelling mode choice for freight transport using advanced choice experiments

In this paper we use advanced choice modelling techniques to analyse demand for freight transport in a context of modal choice. To this end, a stated preference (SP) survey was conducted in order to estimate freight shipper preferences for the main attributes that define the service offered by the different transport modes. From a methodological point of view, we focus on two critical issues in the construction of efficient choice experiments. Firstly, in obtaining good quality prior information about the parameters; and secondly, in the improved quality of the experimental data by tailoring a specific efficient design for every respondent in the sample.With these data, different mixed logit models incorporating panel correlation effects and accounting for systematic and random taste heterogeneity are estimated. For the best model specification we obtain the willingness to pay for improving the level of service and the elasticity of the choice probabilities for the different attributes. Our model provide interesting results that can be used to analyse the potential diversion of traffic from road (the current option) to alternative modes, rail or maritime, as well as to help in the obtaining of the modal distribution of commercial traffic between Spain and the European Union, currently passing through the Pyrenees.     

Regulatory reform and freight mode choice

Reform of trucking Vehicle Weights and Dimensions (VWD) regulations in Canada is now underway. The effect these reforms will have on the freight transportation industry are only recently the subject of research. This paper is part of this new research effort, aimed at understanding how regulatory reform in the trucking sector will affect the existing competitive relationship between trucks and the railways. The paper presents the results of study and research into modelling the relationship between mode choice and the service characteristics that are affected by VWD reform.Using several periods of data, a series of econometric models are developed which help to elucidate the relative relationships between the mode service characteristics for both of the principal interprovincial freight modes. A technique is developed and presented to model railway user costs in order to overcome the complex and often unrepresentable pricing activities of Canadian railways.The strength of the developed econometric models is presented, including their significance and statistical reliability. This is further reinforced by the similarities exhibited by all the models in the series. The selected model is applied to predict market service responses required of the railway industry in order to compete with the trucking sector now adapting to the new regulations.The impact of the newly implemented vehicle weight and dimension reform on the rail transportation industry is analyzed and railway industry improvements aimed at maintaining its market share are presented. The results predicted by the model show that railway user cost reductions should be moderate, and likely less than the level of inflation over the period of time when trucking converts its fleet to take advantage of the new regulations.Abbreviations AASHTO American Association of State Highway and Transportation Officials - CCMTA Canadian Conference of Motor Transport Administrators - EPI End products, inedible commodity classification - GVW Gross Vehicle Weight - NA 1988 VWD National Agreement - RTAC Roads and Transportation Association of Canada - VWD Vehicle Weight(s) and Dimension(s) - WA 1988 VWD Western Agreement     

Travel mode choice among same-sex couples

Same-sex partnered individuals are far more likely to use transit, walk, and cycle, and to a lesser extent, use carpools than are people in straight couples. As society becomes more tolerant, gay and lesbian populations are an increasingly visible social group, yet they have received scant attention by transportation scholars. This paper builds on this nascent literature by documenting and attempting to explain these dramatic differences by controlling for factors known to influence mode choice.We perform two separate analyses employing two distinct datasets. The first analysis examines journey-to-work data from the American Community Survey. The second analysis focuses in specifically on non-motorized (walking, biking) travel using use self-reported walk and bike frequency from the 2009 National Household Travel Survey. In both, we find that characteristics of the neighborhoods in which gays and lesbians live, as well characteristics of the individuals themselves, only explain part of the increased propensity to use “alternative” modes of transportation; a strong residual effect remains.     

Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach

Understanding travel behavior and its relationship to urban form is vital for the sustainable planning strategies aimed at automobile dependency reduction. The objective of this study is twofold. First, this research provides additional insights to examine the effects of built environment factors measured at both home location and workplace on tour-based mode choice behavior. Second, a cross-classified multilevel probit model using Bayesian approach is employed to accommodate the spatial context in which individuals make travel decisions. Using Washington, D.C. as our study area, the home-based work (Home-work) tour in the AM peak hours is used as the analysis unit. The empirical data was gathered from the Washington-Baltimore Regional Household Travel Survey 2007–2008. For parameter estimation, Bayesian estimation method integrating Markov Chain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important role that the built environment at both home location and work ends plays in affecting commuter mode choice behavior. Meanwhile, a comparison of different model results shows that the cross-classified multilevel probit model offers significant improvements over the traditional probit model. The results are expected to give a better understanding on the relationship between the built environment and commuter mode choice behavior.     

Addressing endogeneity in strategic urban mode choice models

Transportation - Endogeneity is a potential anomaly in econometric models, which may cause inconsistent parameter estimates. Transport models are prone to this problem and applications that...     

Gender inequality in mobility and mode choice in Pakistan

Using the nationally representative dataset of the 2007 Pakistan Time-Use Survey, this paper examines gender differences in daily trip rate, mode choice, travel duration, and purpose of travel, which are previously unreported because of limited data availability. Wide gender mobility gaps are observed in the country, where women are less likely to travel, are half as mobile as men and may rely heavily on walking. The particular social and cultural context of the country, that renders women as private, secluded and family honor, seems influential in shaping their mobility and choice of activities. Demographic factors such as age, household income, and marital status significantly decrease female mobility levels. Hence, these findings call for a gender-based culturally responsive transportation policy in the country.     

Modelling commuting mode choice with explicit consideration of carpool in the choice set formation

This article investigates the carpool mode choice option in the context of overall commuting mode choice preferences. The article uses a hybrid discrete choice modelling technique to jointly model the consideration of carpooling in the choice set formation as well as commuting mode choice together with the response bias corrections through the accommodation of measurement equations. A cross-nested error structure for the econometric formulation is used to capture correlations among various commuting modes and carpool consideration in the choice set. Empirical models are estimated using a data set collected through a week-long commuter survey in Edmonton, Alberta. The empirical model reveals many behavioural details of commuting mode choice and carpooling. Interestingly, it reveals that interactions between various Travel Demand Management (TDM) tools with the carpooling option can be different at different level of decision making (choice set formation level and final choice making level).     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

A Stated preference freight mode choice model

Transport policy aims to assist the transport system to work more efficiently and effectively. An understanding of the reasons why people choose to move freight in a certain manner is critical to the development of appropriate policies. This article outlines a data collection approach and the development of a disaggregate mode choice model applicable to the analysis of freight shipper decision making. It focuses on the choice between rail and road in Java, Indonesia. The model indicates that safety, reliability and responsiveness are major attributes influencing rail/road freight mode choice. Transport policies aimed at improving these dimensions should increase the attractiveness of rail transport.     

A tour-based model of travel mode choice

This paper presents a new tour-based mode choice model. The model is agent-based: both households and individuals are modelled within an object-oriented, microsimulation framework. The model is household-based in that inter-personal household constraints on vehicle usage are modelled, and the auto passenger mode is modelled as a joint decision between the driver and the passenger(s) to ride-share. Decisions are modelled using a random utility framework. Utility signals are used to communicate preferences among the agents and to make trade-offs among competing demands. Each person is assumed to choose the best combination of modes available to execute each tour, subject to auto availability constraints that are determined at the household level. The households allocations of resources (i.e., cars to drivers and drivers to ride-sharing passengers) are based on maximizing overall household utility, subject to current household resource levels. The model is activity-based: it is designed for integration within a household-based activity scheduling microsimulator. The model is both chain-based and trip-based. It is trip-based in that the ultimate output of the model is a chosen, feasible travel mode for each trip in the simulation. These trip modes are, however, determined through a chain-based analysis. A key organizing principle in the model is that if a car is to be used on a tour, it must be used for the entire chain, since the car must be returned home at the end of the tour. No such constraint, however, exists with respect to other modes such as walk and transit. The paper presents the full conceptual model and estimation results for an initial empirical prototype. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression.     

The meaning of car availability in mode choice decisions

The paper examines various definitions of car availability that have been used in the literature, and compares the results when applied to a common data set. It argues that car availability means different things to different people depending on their licence holding/car owning status. Using in‐depth interview data, the factors determining car allocation and transferability within a household, and subsequently mode choice, are discussed. An attempt is made to draw some general conclusions for research and modelling. This suggests that different approaches are required depending on whether or not there is competition for use of the car within a household. Also that in cases of competition, mode choice is not a two‐stage process depending on car availability and trip characteristics, but a concurrent decision based on both of these factors.     

Factors affecting temporal changes in mode choice model parameters

In travel demand forecasting models, parameters are often assumed to be stable over time. The stability of these parameters, however, has been questioned. This study investigates the factors affecting temporal changes in mode choice model parameters using a method proposed by the author that jointly utilises repeated cross-sectional data. In this method, the parameters are assumed to follow functional forms and the parameter changes are modelled endogenously. While the author’s previous studies assumed that all parameters are the same function of the same variable, this study assumes that different parameters are different functions of different variables, including time (year) and macro-economic variables. The paper describes a case study of a journey-to-work mode choice analysis for Nagoya, Japan, that examines 288 combinations of the functional forms and variables. The analysis found that the functions of time had serious over-fitting problems and that parameter changes are more closely related to economic factors.     

Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

Bike route choice modeling using GPS data without choice sets of paths

Concerned by the nuisances of motorized travel on urban life, policy makers are faced with the challenge of making cycling a more attractive alternative for everyday transportation. Route choice models can help achieve this objective by gaining insights into the trade-offs cyclists make when choosing their routes and by allowing the effect of infrastructure improvements to be analyzed. We estimate a link-based bike route choice model from a sample of GPS observations in the city of Eugene on a network comprising over 40,000 links. The so-called recursive logit (RL) model (Fosgerau et al., 2013) does not require to sample any choice set of paths. We show the advantages of this approach in the context of prediction by focusing on two applications of the model: link flows and accessibility measures. Compared to the path-based approach which requires to generate choice sets, the RL model proves to make significant gains in computational time and to avoid paradoxical accessibility measure results discussed in previous works, e.g. Nassir et al. (2014).     

Model development for metro station access mode choice

This paper analyses the behaviour of metro users in choosing their access mode to a metro station. Multinominal logit models with satisfactory predictive power were developed for access mode choice on the basis of data collected by interviewing metro users at existing metro stations. A population segmentation approach was adopted and models referring to individuals having the same set of alternative access modes were developed. Trip purpose was found to have significant effects on the access mode choice. Thus, for each population segment different models are proposed for work and education and other trip purpose. Various conclusions concerning the importance of the variables included in the proposed models were drawn through comparisons carried out across the models.     

Campus parking supply impacts on transportation mode choice

Parking demand is a significant land-use problem in campus planning. The parking policies of universities and large corporations with facilities located in small urban areas shape the character of their campuses. These facilities will benefit from a simplified methodology to study the effects of parking availability on transportation mode mix and impacts on recruitment and staffing policies. This paper, based on a case study of North Dakota State University in the United States, introduces an analytical framework to provide planners with insights about how parking supply and demand affects campus transportation mode choice. The methodology relies only on aggregate mode choice data for the special generator zone and the average aggregate volume/capacity ratio projections for all external routes that access the zone. This reduced data requirement significantly lowers analysis cost and obviates the need for specialized modelling software and spatial network analysis tools. Results illustrate that the framework is effective for analysing mode choice changes under different scenarios of parking supply and population growth.