Abstract

As a countermeasure to urban exhaust pollution and traffic congestion, traffic restriction policy (TRP) and carpooling strategy have been widely introduced throughout the world. However, their effects are largely determined by the rationality of implementing policies, and unreasonable policies make them controversial on the long-term implementation benefits. To more effectively manage traffic demand and maintain the sustainability of transportation system, it is necessary to make optimization for management policy before implementation. In this paper, the elastic demand model and equilibrium assignment model are developed under TRP. Considering the negative impact of the mandatory TRP on the public acceptance, we propose a novel TRP strategy, namely TRP with carpool exemptions (TRP-CE), that is, a proportion of high occupancy vehicles (HOV) are allowed to travel in the restricted district even if their license plate numbers are restricted. Then, a bi-level programming model is proposed to solve the optimal schemes by combining multi purposes of ensuring travel convenience, alleviating traffic congestion, and reducing the exhaust pollution. Finally, a numerical experiment is conducted to evaluate the effectiveness of proposed models and make comparative analysis between separate TRP and TRP-CE. The results indicate that TRP-CE has benefits in the following aspects: (1) Carpool exemptions provide an incentive to carpool for travelers by private cars; (2) the public acceptance of TRP is improved by introducing carpool exemptions as a compensatory mitigation strategy for mandatory TRP; (3) the implementation effect of demand management can be well achieved by joint optimization; and (4) there is no need to design and reconstruct HOV lanes for the implementation of TRP-CE, which is convenient for practical application.

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The different versions of the original document can be found in:

https://doaj.org/toc/2071-1050 under the license cc-by
http://dx.doi.org/10.3390/su12187734
https://www.mdpi.com/2071-1050/12/18/7734,
https://academic.microsoft.com/#/detail/3087111108 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.3390/su12187734
Licence: Other

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