DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation

To alleviate traffic congestion in urban areas, electronic toll collection (ETC) systems are deployed all over the world. Despite the merits, tolls are usually pre-determined and fixed from day to day, which fail to consider traffic dynamics and thus have limited regulation effect when traffic conditions are abnormal. In this paper, we propose a novel dynamic ETC (DyETC) scheme which adjusts tolls to traffic conditions in realtime. The DyETC problem is formulated as a Markov decision process (MDP), the solution of which is very challenging due to its 1) multi-dimensional state space, 2) multidimensional, continuous and bounded action space, and 3) time-dependent state and action values. Due to the complexity of the formulated MDP, existing methods cannot be applied to our problem. Therefore, we develop a novel algorithm, PG-β, which makes three improvements to traditional policy gradient method by proposing 1) time-dependent value and policy functions, 2) Beta distribution policy function and 3) state abstraction. Experimental results show that, compared with existing ETC schemes, DyETC increases traffic volume by around 8%, and reduces travel time by around 14. 6% during rush hour. Considering the total traffic volume in a traffic network, this contributes to a substantial increase to social welfare.

Authors

• Haipeng Chen College of Computer Science and Technology, Jilin University, Changchun, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
• Bo An Nanyang Technological University Skywork AI
• Guni Sharon Texas A&M University
• Josiah Hanna
• Peter Stone The University of Texas at Austin Sony AI
• Chunyan Miao Nanyang Technological University
• Yeng Soh

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