Multimodal traffic assignment with traffic emission effects

Author:

Meng Meng1,Shao Chunfu2,Wong Yiik Diew3,Zhang Jie4

Affiliation:

1. TUM CREATE, 1 CREATE Way, 10-02 CREATE Tower, Singapore

2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China

3. Centre for Infrastructure Systems, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

4. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

Abstract

Traffic emissions are one of the most serious issues that affect urban sustainable development. Due to the heavy pollution from cars, public transport, including electric bicycles, has been regaining its proper position. A multimodal traffic equilibrium model with emission effects (MTEEE) is presented that considers energy use per person and pollutant emissions. MTEEE is based on a general traffic equilibrium model which jointly reduces congestion and emissions in a multimodal transportation network. Aiming for control of the total amount of carbon dioxide and limiting the link concentration of carbon dioxide, the model is expressed as a mathematical programming problem by combining the concept of environmental capacity with traffic capacity as constraints. The proposed MTEEE model is solved by a particle swarm optimisation (PSO) algorithm. Two experiments are conducted to demonstrate the effectiveness of the proposed model and the convergence properties of the PSO algorithm.

Publisher

Thomas Telford Ltd.

Subject

Civil and Structural Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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3. An investigation into electric vehicle timeshare rental schemes in Beijing, China;Proceedings of the Institution of Civil Engineers - Engineering Sustainability;2020-04-01

4. Which service is better on a linear travel corridor: Park & ride or on-demand public bus?;Transportation Research Part A: Policy and Practice;2018-12

5. Area-wide urban traffic control: A Bee Colony Optimization approach;Transportation Research Part C: Emerging Technologies;2017-04

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