The impact of traffic demand management policy mix on commuter travel choices

Yacan Wang, Kexin Geng, Anthony D. May and Huiyu Zhou

Transport Policy, 2022, vol. 117, issue C, 74-87

Abstract: The experience of traffic demand management policy in many cities shows that a single policy instrument has limited effect and may have side effects on other contemporaneous policies; therefore, formulating a policy mix is a more effective way to solve urban traffic problems. However, the bulk of previous literature has focused on the impact of single policy instruments, neglecting the growing interest in understanding the role played by the different combinations of policy instruments. Therefore, using a 6*3 matrix typology, this paper provides an empirical impact analysis of selected policy mixes in inducing sustainable travel behavior and reducing private car use. This study also designs orthogonal experiments and adopts stated preference questionnaires to analyze the main effects and full combined effects of packages of policy instruments through multinomial logit models. The results show that the effect of a policy mix is often not better than that of a single policy and demonstrate the need for careful systemic design. A balanced-designed policy mix can facilitate public transportation and help reduce traffic gridlock using a balanced combination of push, pull and systemic TDM policy instruments.

Keywords: Traffic demand management policy; Commuter behavior; SP Experiment; MNL Model; Policy mix (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.tranpol.2022.01.002

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