GPS-Based Tracking of Daily Activities

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. Respondents in a survey may lie or falsely recall information about destinations, times of travel, trip purpose, trip destination and other critical characteristics, such as under-reporting of short trips and the number of stops in a trip chain (Brog, et al., 1982; Purvis, 1990). 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 stop was recorded in real 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. Although the new GPS-involved data collection methodology is not expected to replace the traditional data collection method in behavioral science within a short period of time, it does provide a more robust alternative for defining personal travel than the current methods. After being provided with a CD data record of all the transactions by DOT, a variety of analytical techniques and methods were used on the GPS-collected survey data.

[1]  Jay A. Farrell,et al.  GPS/INS Based Lateral and Longitudinal Control Demonstration: Final Report , 1998 .

[2]  Chris Caplice,et al.  Aspects of commuting behavior: Preferred arrival time, use of information and switching propensity , 1992 .

[3]  James R. Marston,et al.  Improving Transit Access for the Blind and Vision Impaired , 1998 .

[4]  Janusz Supernak,et al.  TEMPORAL UTILITY PROFILES OF ACTIVITIES AND TRAVEL: UNCERTAINTY AND DECISION MAKING , 1992 .

[5]  James R. Marston,et al.  Attitudes of Visually Impaired Persons toward the Use of Public Transportation , 1997 .

[6]  Reginald G. Golledge.  DEFINING THE CRITERIA USED IN PATH SELECTION. , 1995 .

[7]  Tommy Gärling,et al.  Computational-Process Modelling of Household Activity Scheduling , 1993 .

[8]  Reginald G. Golledge,et al.  Place recognition and wayfinding: Making sense of space , 1992 .

[9]  R. Golledge,et al.  Comparing Rectangular Data Matrices , 1982 .

[10]  Kenneth A. Small,et al.  THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS , 1982 .

[11]  James R. Marston,et al.  Investigating travel behavior of nondriving blind and vision impaired people: The role of public transit , 1997 .

[12]  James R. Marston,et al.  Assistive Devices and Services for the Disabled: Auditory Signage and The Accessible City for Blind or Vision Impaired Travelers , 1998 .

[13]  Wilfred W. Recker,et al.  MICROCOMPUTERS IN TRANSPORTATION. USING INTERACTIVE SIMULATION TO MODEL DRIVER BEHAVIOR UNDER ATIS , 1992 .

[14]  Reginald G. Golledge,et al.  Path Selection and Route Preference in Human Navigation: A Progress Report , 1995, COSIT.

[15]  Bin Ran,et al.  A link-based variational inequality model for dynamic departure time/route choice , 1996 .