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Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

  • Takao Dantsuji
  • Daisuke Fukuda
  • Nan ZhengEmail author
Article
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Abstract

Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most serious traffic congestion occurs, implementation of a single TDM measure might not be enough to reduce congestion, because the congestion mechanism in this case is highly complex and involves different transportation modes interacting with each other. Implementation of multiple TDM measures has rarely been discussed in the literature. Therefore, in this study, we propose a simulation-based joint optimization framework composed of dedicated bus lanes and vehicular congestion pricing. The objective of the optimization process is to minimize the congestion cost based on an advanced macroscopic flow theory called the multimodal macroscopic fundamental diagram (mMFD), which can capture the macroscopic traffic dynamics of multimodal transportation systems. In the proposed framework, we develop mMFD-based congestion pricing scheme and incorporate traveler’s behavioral model (i.e. joint departure time and mode choices) with the microscopic traffic simulator. We consider the Tokyo central area as a case study. The simulation results indicate that space allocation of 4.7% for the dedicated bus lanes would be optimal for Tokyo’s network, while the optimal congestion pricing scheme indicates that charges of 900 JPY between 7:30 and 8:00 AM and 300 JPY between 8.00 and 8:30 AM should be levied.

Keywords

Road space allocation Congestion pricing Simulation-based optimization 3D-MFD 

Notes

Acknowledgements

This study was supported by JSPS KAKENHI Grant Number JP 18J15178, the Committee on Advanced Road Technology (CART), Ministry of Land, Infrastructure, Transport, and Tourism, Japan, and partially by JSPS Overseas Challenge Program for Young Researchers. The majority of the work was done during the doctoral study of the first author at Tokyo Institute of Technology. The authors would like to thank anonymous reviewers for their constructive comments that helped improve the details of this manuscript substantially.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringTokyo Institute of TechnologyTokyoJapan
  2. 2.Department of Civil Engineering, Institute of Transport StudiesMonash UniversityMelbourneAustralia
  3. 3.Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijingChina

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