Traffic Cordon Pricing Model Considering Link Capacity Constraint

Document Type : Research Paper

Authors

1 MSc. Grad., Institute of Higher Education for Management and Planning, Tehran, Iran

2 Assistant Professor, Department of Civil Engineering, University of Tehran, Tehran, Iran

3 Associate Professor, Department of Civil and Environmental Engineering, Tehran, Iran

Abstract

Links have capacity which is their physical attribute, as soon as flow in link reaches capacity, in flow pattern of network, at upstream links queue appears and network becomes congested. Cordon Congestion pricing is one of the effective ways that can reduce overcrowding in the congested central districts of networks without any negative impact on whole of the network. The purpose of this study is to present a congestion pricing model considering link capacity constraint. To this purpose, a bi-level model is proposed, where in the upper level the optimal toll is determined, and in the lower level the equilibrium is guaranteed using a traffic assignment model. An algorithm based on particle swarm optimization (PSO) is presented as a solving method for upper level problem and Shahpar’s method is used to solve assignment problem is lower level. The proposed algorithm is applied on Sioux-falls network with and without the capacity constraint. Results show that optimal pricing is affected by road capacity information. In Sioux-falls network from 10 links entering cordon, 6 links get toll, while in the solution, regardless of capacity constraint, 8 links get toll. The total cost of the network in terms of pricing, considering links capacity, improves by 13.1% relative to the lack of pricing. This improvement is 0.38% for a situation where capacity is not seen. The total toll on the cordon is also reduced from 1.86 to 0.86 units.

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References

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Quarterly Journal of Transportation Engineering
Volume 12, Issue 2 - Serial Number 47
January 2021
Pages 377-397

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History

  • Receive Date: 14 January 2019
  • Revise Date: 08 May 2019
  • Accept Date: 08 May 2019

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