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@InProceedings{AAAI18-Chen,
  author = {Haipeng Chen and Bo An and Guni Sharon and Josiah P. Hanna and Peter Stone and Chunyan Miao and Yeng Chai Soh},
  title = {DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation},
  booktitle = {Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18)},
  location = {New Orleans, Lousiana, USA},
  month = {February},
  year = {2018},
    abstract = {
     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.},
}