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Rationing and pricing strategies for congestion mitigation: Behavioral theory, econometric model, and application in Beijing

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  • Zhu, Shanjiang
  • Du, Longyuan
  • Zhang, Lei

Abstract

Some travel demand management policies such as road pricing have been widely studied in literature. Rationing policies, including vehicle ownership quota and vehicle usage restrictions, have been implemented in several megaregions to address congestion and other negative transportation externalities, but not well explored in literature. Other strategies such as Vehicle Mileage Fee have not been well accepted by policy makers, but attract growing research interest. As policy makers face an increasing number of policy tools, a theoretical framework is needed to analyze these policies and provide a direct comparison of their welfare implications such as efficiency and equity. However, such a comprehensive framework does not exist in literature. To bridge this gap, this study develops an analytical framework for analyzing and comparing travel demand management policies, which consists of a mathematical model of joint household vehicle ownership and usage decisions and welfare analysis methods based on compensating variation and consumer surplus. Under the assumptions of homogenous users and single time period, this study finds that vehicle usage rationing performs better when relatively small percentages of users (i.e. low rationing ratio) are rationed off the roads and when induced demand elasticity resulting from congestion mitigation is low. When the amount of induced demand exceeds a certain level, it is shown analytically that vehicle usage restrictions will always cause welfare losses. When the policy goal is to reduce vehicle travel by a fixed portion, road pricing provides a larger welfare gain. The performance of different policies is influenced by network congestion and congestibility. This paper further generalizes the model to consider heterogenous users and demonstrates how it can be applied for policy analysis on a real network after careful calibration.

Suggested Citation

  • Zhu, Shanjiang & Du, Longyuan & Zhang, Lei, 2013. "Rationing and pricing strategies for congestion mitigation: Behavioral theory, econometric model, and application in Beijing," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 210-224.
  • Handle: RePEc:eee:transb:v:57:y:2013:i:c:p:210-224
    DOI: 10.1016/j.trb.2013.07.013
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    3. Bénédicte Meurisse & Maxime Le Roy, 2014. "Towards a clean vehicle fleet: from households’ valuation of fuel efficiency to policy implications," Working Papers 1406, Chaire Economie du climat.
    4. Chen, Dongxu & Sun, Yu & Yang, Zhongzhen, 2020. "Optimization of the travel ban scheme of cars based on the spatial distribution of the last digit of license plates," Transport Policy, Elsevier, vol. 94(C), pages 43-53.
    5. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    6. Yu Ding & Huapu Lu & Lei Zhang, 2016. "An analysis of activity time use on vehicle usage rationed days," Transportation, Springer, vol. 43(1), pages 145-158, January.
    7. Yu Ding & Huapu Lu & Lei Zhang, 2016. "An analysis of activity time use on vehicle usage rationed days," Transportation, Springer, vol. 43(1), pages 145-158, January.
    8. Xu, Meng & Grant-Muller, Susan, 2016. "Trip mode and travel pattern impacts of a Tradable Credits Scheme: A case study of Beijing," Transport Policy, Elsevier, vol. 47(C), pages 72-83.
    9. Nie, Yu (Marco), 2017. "On the potential remedies for license plate rationing," Economics of Transportation, Elsevier, vol. 9(C), pages 37-50.

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