Impacts of congestion pricing and reward strategies on automobile travelers’ morning commute mode shift decisions

Yaping Li, Yuntao Guo, Jian Lu and Srinivas Peeta

Transportation Research Part A: Policy and Practice, 2019, vol. 125, issue C, 72-88

Abstract: This paper studies the impacts of congestion pricing and reward strategies on automobile travelers’ morning commute mode shift decisions using stated preference travel mode choice data of over 1000 automobile travelers collected in Beijing inner districts. To address the complex impacts of these strategies on automobile travelers, a stage-based model framework is developed to analyze their mode shift decision-making process (whether they will shift from using automobile to public transit, biking or walking or continue using automobile) under these strategies. Four multilevel structural equation models are created for participants using automobile (personal vehicle and/or taxi) as the most common mode of transportation (hereafter referred to as “more habitual automobile travelers”) and those using automobile as second most common mode (hereafter referred to as “less habitual automobile travelers”) under each strategy. Model estimation results show that the impacts of latent psychological factors on mode shift decisions under congestion pricing and reward strategies are significantly different between more and less habitual automobile travelers. The results also show that congestion pricing strategies are more effective than reward strategies in promoting mode shifts among more habitual automobile travelers, while the opposite is observed for less habitual automobile travelers. This study provides insights for designing congestion pricing and reward strategies and illustrates the importance of developing complementary modules that target numerous factors in different stages of the mode shift decision-making process to effectively promote mode shifts from automobile to sustainable travel modes in China.

Date: 2019
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DOI: 10.1016/j.tra.2019.05.008

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