Evaluation of Penalty and Enforcement Strategies to Combat Speeding Offences among Professional Drivers: A Hong Kong Stated Preference Experiment

You are viewing a past publication from the Good Systems Network Digest in July 2020.Office of the VP for Researc

A New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variables

This paper formulates a generalized heterogeneous data model (GHDM) that jointly handles mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables—by representing the covariance relationships among them through a reduced number of latent factors. Sufficiency conditions for identification of the GHDM parameters are presented. The maximum approximate composite marginal likelihood (MACML) method is proposed to estimate this jointly mixed model system. This estimation method provides computational time advantages since the dimensionality of integration in the likelihood function is independent of the number of latent factors. The study undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multidimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis.Civil, Architectural, and Environmental Engineerin

A Copula-Based Joint Model of Commute Mode Choice and Number of Non-Work Stops during the Commute

At the time of publication A. Portoghese, E. Spissu, and I. Meloni were at University of Cagliari, and C.R. Bhat and N. Eluru were at the University of Texas at Austin.In this paper, in the spirit of a tour-based frame of analysis, we examine the commute mode choice and the number of non-work stops during the commute. Understanding the mode and activity stop dimensions of weekday commute travel is important since the highest level of weekday traffic congestion in urban areas occurs during the commute periods. The paper employs a copula-based joint multinomial logit – ordered modeling framework in which commute mode choice is modeled using a multinomial logit formulation and the number of commute stops is modeled using an ordered response formulation. The data used in this study are drawn from the “Time use” multipurpose survey conducted between 2002 and 2003 by the Turin Town Council and the Italian National Institute of Statistics (ISTAT) in the Greater Turin metropolitan area of Italy. The results highlight the importance of accommodating the inter-relationship between commute mode choice and commute stops behavior. The results also point to the stronger effect of household responsibilities and demographic characteristics in the Italian context compared to the US context.Civil, Architectural, and Environmental Engineerin

Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model

This paper makes both a methodological contribution as well as an empirical contribution. From a methodological perspective, we propose a new econometric approach for the estimation of joint mixed models that include a multiple discrete choice outcome and a nominal discrete outcome, in addition to the count, binary/ordinal outcomes, and continuous outcomes considered in traditional structural equation models. These outcomes are modeled together by specifying latent underlying unobserved individual lifestyle, personality, and attitudinal factors that impact the many outcomes, and generate the jointness among the outcomes. From an empirical perspective, we analyze residential location choice, household vehicle ownership choice, as well as time-use choices, and investigate the extent of association versus causality in the effects of residential density on activity participation and mobility choices. The sample for the empirical application is drawn from a travel survey conducted in the Puget Sound Region in 2014. The results show that residential density effects on activity participation and motorized auto ownership are both associative as well as causal, emphasizing that accounting for residential self-selection effects are not simply esoteric econometric pursuits, but can have important implications for land-use policy measures that focus on neo-urbanist design

A Model of Deadheading Trips and Pick-Up Locations for Ride-Hailing Service Vehicles

You are viewing a past publication from the Good Systems Network Digest in July 2020.Office of the VP for Researc

Joint Model of App-Based Ridehailing Adoption, Intensity of Use and Intermediate Public Transport (IPT) Consideration among Workers in Chennai City

You are viewing a past publication from the Good Systems Network Digest in July 2020.Office of the VP for Researc

Integrating CEMDAP and MATSIM to Increase the Transferability of Transport Demand Models

At the time of publication C.R. Bhat was at the University of Texas at Austin, while D. Ziemke and K. Nagel were at the University of Berlin.An activity-based approach to transport demand modeling is considered the most behaviorally sound procedure to assess the impacts of transport policies. In this paper, it is investigated whether it is possible to transfer an estimated model for activity generation from elsewhere (the estimation context) and use local area (application context) traffic counts to develop a local area activity-based transport demand representation. Here, the estimation context is the Dallas-Fort Worth area, and the application context is Berlin, Germany. Results in this paper suggest that such a transfer approach is feasible, based on comparison with a Berlin travel survey. Additional studies in the future need to be undertaken to examine the stability of the results obtained in this paper.Civil, Architectural, and Environmental Engineerin

An annual time use model for vacation travel

Vacation travel constitutes about 25% of all long-distance travel, and about 80% of this vacation travel is undertaken using the automobile. This paper contributes to the vacation travel literature by examining how households decide what vacation travel activities to participate in on an annual basis, and to what extent, given the total annual vacation travel time that is available at their disposal. To our knowledge, this is the first comprehensive modelling exercise in the literature to undertake such a vacation travel time-use analysis to examine purpose-specific time investments. A mixed multiple discrete-continuous extreme value (MDCEV) model structure that is consistent with the notion of “optimal arousal” in vacation type time-use decisions is used in the analysis. The data used is drawn from the 1995 American Travel Survey (ATS). The empirical results show that most households participate in different types of vacation travel over the course of a year, and they spend significantly different amounts of time on each type of vacation travel. The model developed here can be used to predict the changes in vacation travel timeuse patterns due to the changes in demographic, economic, and residence characteristics over time. Such predictions, in turn, can be used to examine the changing vacation travel needs of households, so that appropriate service and transportation facilities may be planned