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Understanding factors influencing user engagement in incentive-based travel demand management program

  • Songhua Hu
  • , Chenfeng Xiong
  • , Ya (Eric) Ji
  • , Xin Wu
  • , Kailun Liu
  • , Paul Schonfeld*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Incentive-based travel demand management (IBTDM) has proven effective in mitigating traffic congestion. However, a comprehensive understanding of factors influencing user engagement in IBTDM is lacking due to limited empirical evidence from real-world applications. This study bridges this research gap by examining data from over 4,000 users in an actual IBTDM program, incenTrip, in the Washington, D.C.-Baltimore region. Employing Poisson-Tweedie generalized additive models to account for excess zeros and nonlinear relations, the study examines how home and work-related factors influence users’ enrollment and engagement, measured by the number of registrations, generated trips, and earned incentives at a census block group level. Results reveal that: 1) Urban areas with high population densities and low incomes attract more users and encourage more green travel. 2) Initial enrollment is higher among young, female, Asian, and highly-educated residents, although their subsequent engagement may not be sustained. 3) Workers in educational institutions and retail trades exhibit higher enrollment and maintain stronger engagement than other workers. 4) Well-developed transportation facilities and a higher density of points of interest near the users’ homes or workplaces substantially enhance program attractiveness. 5) Nonlinearities, particularly threshold effects, are observed across various relations analyzed. These findings have valuable policy implications for optimizing ongoing IBTDM programs and informing future initiatives. Policy recommendations include implementing targeted and progressive incentives, adopting combined TDM strategies, prioritizing user-friendly designs, fostering collaboration with employers, and employing nuanced policymaking. © 2024 Elsevier Ltd
Original languageEnglish
Article number104145
JournalTransportation Research Part A: Policy and Practice
Volume186
Online published14 Jun 2024
DOIs
Publication statusPublished - Aug 2024
Externally publishedYes

Funding

This research is financially supported by the Federal Highway Administration (FHWA) through the Metropolitan Washington Council of Governments (MWCOG). The work is part of the Project entitled "ATCMTD: Deployment of Personalized and Dynamic Travel Demand Management Technology in the Washington D.C.-Baltimore, MD-Richmond, VA Megaregion". The opinions in this paper do not necessarily reflect the official views of FHWA or MWCOG. They assume no liability for the content or use of this paper. The authors are solely responsible for all statements in this paper.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Research Keywords

  • Economics
  • Incentive
  • Poisson-Tweedie generalized additive model
  • Travel demand management
  • User profiles

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