New approaches to understanding travel behavior

Abstract

The objectives of this study were to improve the understanding of travel behavior and to enhance practical transportation forecasting models by investigating and applying fundamental concepts from the social sciences. After extensive reviews of the literature in Phase I and the early part of Phase II, three concepts were selected for in-depth investigation. Lifestyle, which can be defined as how individuals and households allocate time to alternative activities such as work, in-home time, and recreation, has become increasingly prominent in travel behavior research. The emphasis on activities is also consistent with a stratification of travel by trip purpose. Life cycle, typically defined in terms of the ages of the adult members of a household and the number and ages of children, was also selected for investigation. Household structure is a closely related concept, especially relevant in light of the ongoing changing size and composition of households. The life cycle and household structure concepts suggest that the number of household members, a variable typically used in standard trip generation models, might not adequately capture the characteristics of households that affect travel demand. The standard urban transportation demand forecasting system consists of four components: trip generation, trip distribution, modal choice, and route assignment. In order to avoid duplication of other major research projects, and to use this project's resources in a cost-effective manner, the research team and the NCHRP advisory panel determined that enhancement of the trip generation models would be the major practical focus. Analyses were performed in three major areas. First, the effects of life cycle, household structure, and other sociodemographic variables on the allocation of time to specific activities by individuals were examined. Second, the usefulness of life cycle, household structure, and residential location characteristics in trip generation models was explored. Third, structural equation methods were used to analyze simultaneously the interactions among measures of mobility such as travel time, travel distance, and trip frequency.

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