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

作　　者：Jiahui Jin Xiaoxuan Zhu Biwei Wu Jinghui Zhang Yuxiang Wang 

机构地区：[1]the School of Computer Science and Engineering,Southeast University,Nanjing 211189,China [2]the Department of Computer Science and Engineering,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Tsinghua Science and Technology》2022年第1期91-102,共12页清华大学学报（自然科学版（英文版）

基　　金：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;partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization;the Fundamental Research Funds for the Central Universities。

摘　　要：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.

关 键 词：road pricing traffic congestion al eviation deep reinforcement learning 

分 类 号：F572.5[经济管理—产业经济]