Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries

ABSTRACT This paper investigates empirical relationships between trip chain type and mode class choice for developing countries. To formulate these two sets of decisions, four empirical models are developed using structural equation modeling (SEM). Those models are calibrated using one-month travel diary data collected in Dhaka city. SEM correlates the observed variables and identifies their relationship with trip-chaining type utility and mode class choice utility. The fitted models are selected based on statistical results and similarity with the real-life situation. Direct relationships between trip-chaining and mode choice utilities are found insignificant. However, several socio-demographic factors influence both simultaneously. Consequently, it is essential to consider mode class choice concurrently for modeling trip chains. This study also investigates the influencing factors for work-based and non-work-based trip chains separately and effects of road users’ heterogeneity. The research results can be utilized to perceive trip chain-mode choice patterns for developing countries.

[1]  Eric J. Miller,et al.  Temporal transferability of work trip mode choice models in an expanding suburban area: the case of York Region, Ontario , 2014 .

[2]  Chandra R. Bhat,et al.  A New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variables , 2015 .

[3]  Xin Ye Robust Modeling Analysis of Relationships Between Mode Choice and Trip Chaining Pattern Using Two-Stage Semi-Nonparametric Method , 2010 .

[4]  D. Levinson,et al.  Activity, Travel, and the Allocation of Time , 1995 .

[5]  C. Bhat An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey , 1999 .

[6]  D. Hensher,et al.  Trip chaining as a barrier to the propensity to use public transport , 2000 .

[7]  S Handy NON-WORK TRAVEL OF WOMEN: PATTERNS, PERCEPTIONS AND PREFERENCES , 2000 .

[8]  Matthew J. Roorda,et al.  Long- and short-term dynamics in activity scheduling: A structural equations approach , 2008 .

[9]  Michael R. Mullen,et al.  Structural equation modelling: guidelines for determining model fit , 2008 .

[10]  Takayuki Morikawa,et al.  Transport Policy Analysis for Developing Countries Using a Nested Logit Model of Vehicle Ownership, Mode Choice and Trip-Chain , 2001 .

[11]  Kay W. Axhausen,et al.  Within household allocation of travel - the case of upper Austria: paper submitted for presentaion at the 80th Annual Meeting of the Transportation Research Board, Washington, D.C., January 2001 , 2002 .

[12]  N. McGuckin,et al.  Examining Trip-Chaining Behavior: Comparison of Travel by Men and Women , 1999 .

[13]  O. D. Duncan,et al.  Introduction to Structural Equation Models. , 1977 .

[14]  B. Byrne Book Review: Structural Equation Modeling with EQS and EQS/Windows: Basic Concepts, Applications, and Programming , 1994 .

[15]  Fengming Su,et al.  An analysis of trip chaining among older London residents , 2010 .

[16]  Chandra R. Bhat,et al.  Tour-Based National Model System to Forecast Long-Distance Passenger Travel in the United States , 2015 .

[17]  D. Hensher,et al.  The Trip Chaining Activity of Sydney Residents: A Cross- Section Assessment by Age Group with a focus on Seniors , 2007 .

[18]  Frederick J Wegmann,et al.  Trip Linkage Patterns for Workers , 1998 .

[19]  Pui‐wa Lei,et al.  Introduction to Structural Equation Modeling: Issues and Practical Considerations , 2007 .

[20]  Barbara M. Byrne,et al.  Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[21]  M. McNally,et al.  A MODEL OF ACTIVITY PARTICIPATION AND TRAVEL INTERACTIONS BETWEEN HOUSEHOLD HEADS , 1996 .

[22]  Khandker Nurul Habib,et al.  Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets , 2014 .

[23]  Xin Ye,et al.  An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns , 2007 .

[24]  M. Hickman,et al.  Household type and structure, time-use pattern, and trip-chaining behavior , 2007 .

[25]  Chandra R. Bhat,et al.  Analysis of the Impact of Technology Use on Multimodality and Activity Travel Characteristics , 2017 .

[26]  Johanna Zmud,et al.  Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making , 2005 .

[27]  Khandker Nurul Habib,et al.  Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary , 2012 .

[28]  J. Strathman,et al.  Effects of household structure and selected travel characteristics on trip chaining , 1994 .

[29]  R. Pendyala,et al.  Modeling Time and Task Allocation among Household Members for Simulating Household Activity Travel Patterns , 2002 .

[30]  Joffre Swait,et al.  Distinguishing taste variation from error structure in discrete choice data , 2000 .

[31]  M. Ben-Akiva,et al.  A THEORETICAL AND EMPIRICAL MODEL OF TRIP CHAINING BEHAVIOR , 1979 .

[32]  J. H. Steiger Structural Model Evaluation and Modification: An Interval Estimation Approach. , 1990, Multivariate behavioral research.

[33]  Ram M. Pendyala,et al.  Activity patterns, time use, and travel of millennials: a generation in transition? , 2016 .

[34]  You-Lian Chu Empirical Analysis of Commute Stop-Making Behavior , 2003 .

[35]  E. I. Pas,et al.  Socio-demographics, activity participation and travel behavior , 1999 .

[36]  You-Lian Chu,et al.  Daily Stop-Making Model for Workers , 2004 .

[37]  Chandra R. Bhat,et al.  A comprehensive daily activity-travel generation model system for workers , 2000 .

[38]  Chandra R. Bhat,et al.  WORK TRAVEL MODE CHOICE AND NUMBER OF NON-WORK COMMUTE STOPS , 1997 .

[39]  David M Levinson,et al.  Chained Trips in Montgomery County, Maryland , 1995 .

[40]  Kay W. Axhausen,et al.  Within-Household Allocation of Travel: Case of Upper Austria , 2001 .

[41]  Sarah Salem,et al.  Use of repeated cross-sectional travel surveys to develop a Meta model of activity-travel generation process models: accounting for changing preference in time expenditure choices , 2015 .

[42]  Andreas Ritter,et al.  Structural Equations With Latent Variables , 2016 .

[43]  Jennifer Barnes,et al.  Evaluating the Effects of Traveler and Trip Characteristics on Trip Chaining, with Implications for Transportation Demand Management Strategies , 2000 .

[44]  Satoshi Fujii,et al.  EVALUATION OF TRIP-INDUCING EFFECTS OF NEW FREEWAYS USING A STRUCTURAL EQUATIONS MODEL SYSTEM OF COMMUTERS' TIME USE AND TRAVEL , 2000 .

[45]  Takayuki Morikawa,et al.  Household Travel Behavior in Developing Countries: Nested Logit Model of Vehicle Ownership, Mode Choice, and Trip Chaining , 2002 .

[46]  H. M. Zhang A mathematical theory of traffic hysteresis , 1999 .

[47]  S. Subbarao,et al.  Trip Chaining Behavior in Developing Countries: A Study of Mumbai Metropolitan Region, India , 2013 .

[48]  R. Pendyala,et al.  A structural equations analysis of commuters' activity and travel patterns , 2001 .

[49]  Xuewu Chen,et al.  Empirical Analysis of Commute Trip Chaining , 2007 .

[50]  T. Golob A NON-LINEAR CANONICAL CORRELATION ANALYSIS OF WEEKLY TRIP CHAINING BEHAVIOUR , 1985 .