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Tsinghua Science and Technology 2022, 27(1): 91-102 https://doi.org/10.26599/TST.2020.9010062
Open Access | Issue | Published: 17 August 2021

A Dynamic and Deadline-Oriented Road Pricing Mechanism for Urban Traffic Management

Show Author's Information Jiahui Jin,  Xiaoxuan Zhu,  Biwei Wu,  Jinghui Zhang( ),  Yuxiang Wang
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Department of Computer Science and Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Keywords:
deep reinforcement learning, road pricing, traffic congestion alleviation
Cite this article:
Jin J, Zhu X, Wu B, et al. A Dynamic and Deadline-Oriented Road Pricing Mechanism for Urban Traffic Management. Tsinghua Science and Technology, 2022, 27(1): 91-102. https://doi.org/10.26599/TST.2020.9010062
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Abstract Full text About this article

Road pricing is an urban traffic management mechanism to reduce traffic congestion. Currently, most of the road pricing systems based on predefined charging tolls fail to consider the dynamics of urban traffic flows and travelers’ demands on the arrival time. In this paper, we propose a method to dynamically adjust online road toll based on traffic conditions and travelers’ demands to resolve the above-mentioned problems. The method, based on deep reinforcement learning, automatically allocates the optimal toll for each road during peak hours and guides vehicles to roads with lower toll charges. Moreover, it further considers travelers’ demands to ensure that more vehicles arrive at their destinations before their estimated arrival time. Our method can increase the traffic volume effectively, as compared to the existing static mechanisms.


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About this article

A Dynamic and Deadline-Oriented Road Pricing Mechanism for Urban Traffic Management

Show Author's information Jiahui Jin, Xiaoxuan Zhu, Biwei Wu, Jinghui Zhang( ), Yuxiang Wang
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Department of Computer Science and Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract

Road pricing is an urban traffic management mechanism to reduce traffic congestion. Currently, most of the road pricing systems based on predefined charging tolls fail to consider the dynamics of urban traffic flows and travelers’ demands on the arrival time. In this paper, we propose a method to dynamically adjust online road toll based on traffic conditions and travelers’ demands to resolve the above-mentioned problems. The method, based on deep reinforcement learning, automatically allocates the optimal toll for each road during peak hours and guides vehicles to roads with lower toll charges. Moreover, it further considers travelers’ demands to ensure that more vehicles arrive at their destinations before their estimated arrival time. Our method can increase the traffic volume effectively, as compared to the existing static mechanisms.

Keywords: deep reinforcement learning, road pricing, traffic congestion alleviation

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Publication history
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Acknowledgements
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Publication history

Received: 01 December 2020
Accepted: 16 December 2020
Published: 17 August 2021
Issue date: February 2022

Copyright

© The author(s) 2022

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2018AAA0101200), National Natural Science Foundation of China (Nos. 62072099, 61972085, 62072149, and 61872079), Public Welfare Research Program of Zhejiang (No. LGG19F020017), Jiangsu Provincial Key Laboratory of Network and Information Security (No. BM2003201), Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (No. 93K-9), "Zhishan" Scholars Programs of Southeast University, and partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Fundamental Research Funds for the Central Universities. We also thank the Big Data Computing Center of Southeast University for providing the experiment environment and computing facility.

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