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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 vanes 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 modechoice 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.

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Telephone-interview surveys are a very efficient way of conducting large-scale travel surveys. Recent advancements in computer technology have made it possible to improve upon the quality of data collected by telephone surveys through computerization of the entire sample-control process, and through the direct recording of the collected data into a computer. Notwithstanding these technological advancements, potential sources of bias still exist, including the reliance on an adult member of the household to report the travel information of other household members.Travel data collected in a recent telephone interview survey in the Toronto region is used to examine this issue. The statistical tool used in the research was the Analysis of Variance (ANOVA) technique as implemented within the general linear model framework in SAS. The study-results indicate that reliance on informants to provide travel information for non-informant members of their respective households led to the underreporting of some categories of trips. These underreported trip categories were primarily segments of home-based discretionary trips, and non home-based trips. Since these latter two categories of trips are made primarily outside the morning peak period, estimated factors to adjust for their underreporting were time-period sensitive. Further, the number of vehicles available to the household, gender, and driver license status respectively were also found to be strongly associated with the underreporting of trips and thus were important considerations in the determination of adjustment factors.Work and school trips were found not to be underreported, a not surprising result giving the almost daily repetitiveness of trips made for these purposes and hence the ability of the informant to provide relatively more precise information on them.Daniel A. Badoe is in the