Trip chaining impact on within-day mode choice dynamics: Evidences from a multi-day travel survey

Abstract Mode choice is influenced by a large variety of factors, as for example users’ socio-economic attributes or level of service for the different alternatives. In order to understand better what leads to temporal and spatial variations of modal split, we propose in this paper an analysis of a multi-day travel survey, with a series of descriptive statistics as well as inferential analysis on the correlation between mode choice and tour-specific attributes at both spatial and temporal levels. This paper discusses the importance of considering tour-based mode choice not only because it brings consistency between successive mode choices but also allows the inclusion of relevant tours’ characteristics such as activity types, distances, time of the day, and previous mode choices. A total of 5848 home-based tours done in 2008 are studied in the area of Ghent, Belgium. Identified patterns show the importance of modelling dynamic mode choice with trip chaining and time of the day. The modal share of car drivers differs of more than 40% between hours of the day and about 30% between different activities. Furthermore, the definition of activity spaces by principal mode choice and home-work locations introduces the calibration of probabilistic aggregate Gaussian fit to visited points.

[1]  Harry Timmermans,et al.  Capturing tour mode and activity choice interdependencies: A co-evolutionary logit modelling approach , 2007 .

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

[3]  Basile Chaix,et al.  Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study). , 2014, Social science & medicine.

[4]  Moshe Ben-Akiva,et al.  Activity Based Travel Demand Model Systems , 1998 .

[5]  Cinzia Cirillo,et al.  Dynamic model of activity-type choice and scheduling , 2010 .

[6]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[7]  Graham Currie,et al.  Exploring the trip chaining behaviour of public transport users in Melbourne , 2011 .

[8]  K. Axhausen,et al.  Observing the rhythms of daily life: A six-week travel diary , 2002 .

[9]  K. Axhausen,et al.  Activity spaces: Measures of social exclusion? , 2003 .

[10]  John L. Renne,et al.  Socioeconomics of Urban Travel: Evidence from the 2001 NHTS , 2003 .

[11]  Stefan Schönfelder,et al.  Capturing Human Activity Spaces , 2007 .

[12]  S. Farber,et al.  Potential Path Areas and Activity Spaces in Application: A Review , 2015 .

[13]  B. Lenntorp.  Paths in space-time environments : a time-geographic study of movement possibilities of individuals , 1976 .

[14]  Ennio Cascetta,et al.  Modeling Transportation Systems: Preliminary Concepts and Application Areas , 2009 .

[15]  Ran Li,et al.  Constructing human activity spaces: A new approach incorporating complex urban activity-travel , 2016 .

[16]  Tai-Yu Ma,et al.  Variability in daily activity-travel patterns: the case of a one-week travel diary , 2016 .

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

[18]  Ruben H. Zamar,et al.  Robust Estimates of Location and Dispersion for High-Dimensional Datasets , 2002, Technometrics.

[19]  Cathy Macharis,et al.  Linking modal choice to motility: a comprehensive review , 2013 .

[20]  T. Hägerstrand.  What about people in Regional Science? , 1970 .

[21]  Constantinos Antoniou,et al.  Factors affecting modal choice in urban mobility , 2012, European Transport Research Review.

[22]  Patrice Marcotte,et al.  Equilibrium and Advanced Transportation Modelling , 2013 .