Peter Stone's Selected Publications

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DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation

DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation.
Haipeng Chen, Bo An, Guni Sharon, Josiah P. Hanna, Peter Stone, Chunyan Miao, and Yeng Chai Soh.
In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), February 2018.

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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.

BibTeX Entry

@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.},
}

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