Open AccessAn automatic segmentation procedure for studying variations in mode choice behaviour
Author(s) -
Badoe Daniel A.,
Miller Eric J.
Publication year - 1998
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.5670320205
Subject(s) - modal , population , mode choice , a priori and a posteriori , travel behavior , econometrics , computer science , variable (mathematics) , segmentation , service (business) , mode (computer interface) , discrete choice , geography , operations research , data mining , artificial intelligence , machine learning , mathematics , transport engineering , engineering , public transport , economics , demography , human–computer interaction , mathematical analysis , philosophy , chemistry , economy , epistemology , sociology , polymer chemistry
Urban areas are very complex and heterogeneous in terms of their population composition and activity systems. The transport system, modal choices and service levels available to the population also varies considerably across space and time. These similarities and differences in choices and levels of explanatory variables facing individual tripmakers have to be explicitly considered in any study of transport behvior. The common practice has been to include user attributes, in addition to the system characteristics, in the modal utility functions to help capture differences in choice behavior across individuals. However, it could well be that the mode‐choice behavior of a segment of the population is fundamentally different from other segments of the population. In view of this, some studies have applied segmentation schemes to help identify the subgroups of presumably different travel responses. Typically, such schemes have been based on stratification of the population by a single variable, chosen either based on a priori notions or one‐way cross tabulations. These have their shortcomings. Thus, this paper develops an analytical procedure that simultaneously deals with level of service, socioeconomic and spatial factors to determine the relative role each plays in determining travel behavior. The procedure is applied to data from the Toronto region to illustrate its use.