@article{Chen_An_Sharon_Hanna_Stone_Miao_Soh_2018, title={DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/11337}, DOI={10.1609/aaai.v32i1.11337}, abstractNote={ <p> 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%, 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. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Haipeng and An, Bo and Sharon, Guni and Hanna, Josiah and Stone, Peter and Miao, Chunyan and Soh, Yeng}, year={2018}, month={Apr.} }