# Peter Stone's Selected Publications

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## 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. DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), February 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.

### 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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