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) multi-dimensional, 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-beta, 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 percent, and reduces travel time by around 14.6 percent during rush hour. Considering the total traffic volume in a traffic network, this contributes to a substantial increase to social welfare.

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In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, Louisiana, USA, February 2018.

@inproceedings{AAAI18-Chen, title={DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation}, author={Haipeng Chen and Bo An and Guni Sharon and Josiah Hanna and Peter Stone and Chunyan Miao and Yeng Chai Soh}, booktitle={Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18)}, month={February}, address={New Orleans, Louisiana, USA}, url="http://www.cs.utexas.edu/users/ai-lab?AAAI18-Chen", year={2018} }