Skip to main content
eScholarship
Open Access Publications from the University of California

A GPS-based Analysis of Household Travel Behavior

  • Author(s): Golledge, Reginald G
  • Zhou, Jack
  • et al.
Abstract

While characteristics of daily travel behavior have been determined from analyses of the reconstructed household travel behavior recorded in travel diaries, such reconstructions are subject to criticisms that people lie or falsely recall information about destinations, times of travel, trip purposes, trip destination, and other critical characteristics, such as under-reporting of short trips and the number of stops in a trip chain. In 1997 the Department of Transportation carried out a one week study in Lexington, Kentucky in which the cars of 100 households were equipped with GPS and in-car computers. Every stop was logged by the GPS receiver and the purpose of the trip was recorded at that time on an in-car computer. The final report of the study gave descriptions of travel behavior but performed little analysis on the data so collected. Using a CD-ROM data record of all transactions provided by DOT, we propose to examine questions such as: To what extent were the travel behaviors recorded on each day highly correlated? Are there recurring cyclic patterns that show similar patterns of repetitive behavior on different days of the week? How well can a model calibrated for activity behavior on one day predict behavior on other dates? How well does this data support panel and/or diary studies? To what extent to discrete choice models of travel behavior fit the data for different days with approximately the same parameters? To what extent did travelers use optimal travel routes (e.g. shortest path?)? What proportion of different trip purposes were undertaken entirely on suburban streets and what proportion relied on different quantities of freeway travel?

Knowing the answers to these questions provides the final pieces to the puzzle of repetitiveness in household travel behavior. It is of considerable importance to the calibration of predictive models of behavior and to successfully implementing Advanced Transportation Management and Information Systems (ATMIS).

Main Content
Current View