TY - GEN
T1 - IntelliLight
T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
AU - Wei, Hua
AU - Yao, Huaxiu
AU - Zheng, Guanjie
AU - Li, Zhenhui
N1 - Funding Information:
The work was supported in part by NSF awards #1544455, #1652525, #1618448, and #1639150. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data.
AB - The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data.
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U2 - 10.1145/3219819.3220096
DO - 10.1145/3219819.3220096
M3 - Conference contribution
AN - SCOPUS:85051489900
SN - 9781450355520
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2496
EP - 2505
BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 19 August 2018 through 23 August 2018
ER -