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ZHONG Shao-peng, DENG Wei. Optimum congestion pricing model of stochastic system with multiple user classes[J]. Journal of Traffic and Transportation Engineering, 2010, 10(6): 94-101. doi: 10.19818/j.cnki.1671-1637.2010.06.016
Citation: ZHONG Shao-peng, DENG Wei. Optimum congestion pricing model of stochastic system with multiple user classes[J]. Journal of Traffic and Transportation Engineering, 2010, 10(6): 94-101. doi: 10.19818/j.cnki.1671-1637.2010.06.016
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Optimum congestion pricing model of stochastic system with multiple user classes

doi: 10.19818/j.cnki.1671-1637.2010.06.016
  • 1.

    School of Transportation, Southeast University, Nanjing 210096, Jiangsu, China

  • 2.

    School of Transportation Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China

More Information
  • Author Bio:

    ZHONG Shao-peng(1982-), male, lecturer, doctoral student, +86-452-8097807, szsp001@163.com

    DENG Wei(1966-), male, professor, +86-25-83795857, dengwei@seu.edu.cn

  • Received Date: 2010-07-04
  • Publish Date: 2010-12-25
    Abstract
  • Traffic network equilibrium model and marginal cost pricing (MCP) theory were combined, an optimum congestion pricing model of stochastic system with multiple user classes under the endogenous market share of advanced traveller information systems (ATIS) and demand elasticity was established, and the influences of the joint implementation of ATIS and MCP on different users' travel behaviours were analyzed. Analysis result indicates that MCP models with symmetric impedance function neglect external costs that travellers impose on users on nearby roads, and the obtained MCP is smaller than the real value. When the parameters of travel cost perception variations for equipped and unequipped ATIS users are 0.50 and 0.01 respectively, compared to none of traffic management measures being taken, the joint implementation of ATIS and MCP can save 14.3% of total network travel cost, and the separate implementation of ATIS or MCP can save 13.6% or 6.3% of total network travel cost respectively. So network operation performance can be optimized efficiently by the joint implementation of the two technologies.

     

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