Graduation Year

2008

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Civil Engineering

Major Professor

John J. Lu, Ph.D.

Co-Major Professor

Steven E. Polzin, Ph.D.

Committee Member

Xuehao Chu, Ph.D.

Committee Member

Joseph S. DeSalvo, Ph.D.

Committee Member

Philip L. Winters, B.S.

Keywords

travel behavior, transportation demand management, compressed work weeks, telecommuting, mode choices, nested logit model, generalized ordered logit model

Abstract

Travel demand Management (TDM) focuses on improving the efficiency of the transportation system through changing traveler's travel behavior rather than expanding the infrastructure. An employer based integrated TDM program generally includes strategies designed to change the commuter's travel behavior in terms of mode choice, time choice and travel frequency. Research on TDM has focused on the evaluation of the effectiveness of TDM program to report progress and find effective strategies. Another research area, identified as high-priority research need by TRB TDM innovation and research symposium 1994 [Transportation Research Circular, 1994], is to develop tools to predict the impact of TDM strategies in the future. These tools are necessary for integrating TDM into the transportation planning process and developing realistic expectations. Most previous research on TDM impact evaluation was worksite-based, retrospective, and focused on only one or more aspects of TDM strategies. That research is generally based on survey data with small sample size due to lack of detailed information on TDM programs and promotions and commuter travel behavior patterns, which cast doubts on its findings because of potential small sample bias and self-selection bias. Additionally, the worksite-based approach has several limitations that affect the accuracy and application of analysis results.

Based on the Washington State Commute Trip Reduction (CTR) dataset, this dissertation focuses on analyzing the participation rates of compressed work week schedules and telecommuting for the CTR affected employees, modeling the determinants of commuter's compressed work week schedules and telecommuting choices, and analyzing the quantitative impacts of an integrated TDM program on individual commuter's mode choice. The major findings of this dissertation may have important policy implications and help TDM practitioners better understand the effectiveness of the TDM strategies in terms of person trip and vehicle trip reduction. The models developed in this dissertation may be used to evaluate the impacts of an existing TDM program. More importantly, they may be incorporated into the regional transportation model to reflect the TDM impacts in the transportation planning process.

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