Page 155 - 《软件学报》2021年第8期
P. 155
邵明莉 等:面向优先车辆感知的交通灯优化控制方法 2437
References:
[1] 2018 annual report of traffic operation. 2018 (in Chinese). http://www.jtcx.sh.cn/trafficanalyse.html
[2] Li MW, Li L. Intelligent transportation system in China: The optimal evaluation period of transportation’s application performance.
Journal of Intelligent & Fuzzy Systems, 2020,38(6):6979−6990.
[3] Wu LB, Nie L, Liu BY, Wu N, Zou YF, Ye LY. An intelligent traffic signal control method in VANET. Chinese Journal of
Computers, 2016,39(6):1105−1119 (in Chinese with English abstract).
[4] Chang W, Roy D, Zhao S, Annaswamy A, Chakraborty S. CPS-oriented modeling and control of traffic signals using adaptive back
pressure. In: Proc. of the Design, Automation & Test in Europe Conf. & Exhibition (DATE). IEEE, 2020. 1686−1691.
[5] Zhang ZK, Pang WG, Xie WJ, Lü MS, Wang Y. Deep learning for real-time applications: A survey. Ruan Jian Xue Bao/Journal of
Software, 2020,31(9):2654−2677 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5946.htm [doi: 10.13328/j.
cnki.jos.005946]
[6] Diakaki P, Kotsialos D, Wang Y. Review of road traffic control strategies. Proc. of the IEEE, 2003,91(12):2041−2042.
[7] Sutton RS, Barto AG. Introduction to Reinforcement Learning. Cambridge: MIT Press, 1998.
[8] Thorpe TL. Vehicle traffic light control using sarsa. 1997. http://citeseer.ist.psu.edu/thorpe97vehicle.html
[9] Xu Y, Zhang YL, Sun TT, Su YF. Agent-based decentralized cooperative traffic control toward green-waved effects. Ruan Jian
Xue Bao/Journal of Software, 2012,23(11):2937−2945 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4307.
htm [doi: 10.3724/SP.J.1001.2012.04307]
[10] Lee J, Chung J, Sohn K. Reinforcement learning for joint control of traffic signals in a transportation network. IEEE Trans. on
Vehicular Technology, 2020,69(2):1375−1387.
[11] Guo MY, Wang P, Chan CY, Askary S. A reinforcement learning approach for intelligent traffic signal control at urban
intersections. In: Proc. of the IEEE Intelligent Transportation Systems Conf. (ITSC). 2019. 4242−4247.
[12] Yu D, Wei SG, Rong DC, Chai LG. RA-TSC: Learning adaptive traffic signal control strategy via deep reinforcement learning. In:
Proc. of the IEEE Intelligent Transportation Systems Conf. (ITSC). 2019. 3275−3280.
[13] Rizzo SG, Vantini G, Chawla S. Reinforcement learning with explainability for traffic signal control. In: Proc. of the IEEE
Intelligent Transportation Systems Conf. (ITSC). 2019. 3567−3572.
[14] Cao M, Shuai QQ, Li V. Emergency vehicle-centered traffic signal control in intelligent transportation systems. In: Proc. of the
IEEE Intelligent Transportation Systems Conf. (ITSC). 2019. 4525−4531.
[15] Wang Z, Schaul T, Hessel M, Hasselt H, Lanctoc M, Freitas N. Dueling network architectures for deep reinforcement learning. In:
Proc. of the Int’l Conf. on Machine Learning (ICML). 2016. 1995−2003.
[16] Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double Q-learning. In: Proc. of the 30th AAAI Conf. on
Artificial Intelligence (AAAI). 2016. 2094−2100.
[17] Behrisch M, Bieker L, Erdmann J, Krajzewicz D. Sumo—Simulation of urban mobility: An overview. In: Proc. of the SIMUL.
2011. https://elib.dlr.de/71460/
[18] Singh T. Constrained Markov decision processes for intelligent traffic. In: Proc. of the Int’l Conf. on Computing, Communication
and Networking Technologies (ICCCNT). 2019. 1−7.
[19] Wei H, Zheng G, Yao H, Li ZH. Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proc. of the
24th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining (KDD). 2018. 2496−2505.
[20] Joo H, Ahmed SH, Lim Y. Traffic signal control for smart cities using reinforcement learning. Computer Communications, 2020,
154:324−330.
[21] Zang X, Yao H, Zheng GJ, Xu K, Li ZH. MetaLight: Value-based meta-reinforcement learning for traffic signal control. In: Proc.
of the AAAI Conf. on Artificial Intelligence (AAAI), Vol.34. 2020. 1153−1160.
[22] Yan S, Zhang J, Buescher D, Burgard W. Efficiency and equity are both essential: A generalized traffic signal controller with deep
reinforcement learning. arXiv preprint arXiv:2003.04046, 2020.
[23] Qin X, Khan AM. Control strategies of traffic signal timing transition for emergency vehicle preemption. Transportation Research
Part C: Emerging Technologies, 2012,25:1−17.