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Dynamic Route Guidance Strategy in Transportation Cyber-physical Systems

  • CHEN Zhuoran ,
  • HAN Dingding ,
  • CHEN Zhuoran ,
  • HAN Dingding
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  • School of Information Science and Technology, Fudan University, Shanghai 200433, China

Received date: 2021-05-16

  Revised date: 2021-07-02

  Online published: 2022-02-21

Abstract

Aiming at the potential congestion caused by vehicles taking the same path, a transportation cyber-physical system framework with real-time information is proposed. Q-learning is used as a dynamic route guidance strategy, and the frequency and modes of dynamic route guidance are studied. Simulation results show that the dynamic guidance combined with real-time traffic information can effectively improve the road capacity, and multiple guidance can alleviate the potential congestion in one-shot guidance to a certain extent. The game caused by dynamic guidance varies on the frequency and modes of the guidance.

Cite this article

CHEN Zhuoran , HAN Dingding , CHEN Zhuoran , HAN Dingding . Dynamic Route Guidance Strategy in Transportation Cyber-physical Systems[J]. Complex Systems and Complexity Science, 2022 , 19(1) : 81 -87 . DOI: 10.13306/j.1672-3813.2022.01.011

References

[1] ZANELLA A, BUI N, CASTELLANI A, et al. Internet of things for smart cities[J]. IEEE Internet of Things Journal, 2014, 1(1):22-32.
[2] AL-FUQAHA A, GUIZANI M, MOHAMMADI M, et al. Internet of Things: a survey on enabling technologies, protocols, and applications[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4):2347-2376.
[3] LU K, LIU J T, ZHOU X S, et al. A review of big data applications in urban transit systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(5):2535-2552.
[4] ZHU L, YU F R, WANG Y, et al. Big data analytics in intelligent transportation systems: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(1):383-398.
[5] CASTAÑO F, BERUVIDES G, HABER R E, et al. Obstacle recognition based on machine learning for on-chip LiDAR sensors in a cyber-physical system[J]. Sensors, 2017, 17(9):2109.
[6] LI Y F, ZHANG L, ZHENG H, et al. Nonlane-discipline-based car-following model for electric vehicles in transportation-cyber-physical systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(1):38-47.
[7] WU C Z, PENG L Q, HUANG Z, et al. A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system[J]. Transportation Research Part C: Emerging Technologies, 2014, 47:179-191.
[8] BESSELINK B, TURRI V, VAN DE HOEF S, et al. Cyber-physical control of road freight transport[J]. Proceedings of the IEEE,2016,104(5):1128-1141.
[9] LIN J, YU W, ZHANG N, er al. Data integrity attacks against dynamic route guidance in transportation-based cyber-physical systems: Modeling, analysis, and defense[J]. IEEE Transactions on Vehicular Technology, 2018, 67(9):8738-8753.
[10] KIM K, KUMAR P R. Cyber-physical systems: a perspective at the centennial[J] Proceedings of the IEEE, 2012, 100:1287-1308.
[11] 韩定定, 钱江海, 等. 实证研究复杂网络的拓扑与动力学行为[M]. 北京:北京大学出版社,2012:185-187.
[12] 中国电子技术标准化研究院. 信息物理系统(CPS)典型应用案例集[M]. 北京:电子工业出版社, 2019.
[13] GENDREAU M, GHIANI G, GUERRIERO E. Time-dependent routing problems: a review[J]. Computers & operations research, 2015, 64:189-197.
[14] YAN L, SHEN H Y. TOP: optimizing vehicle driving speed with vehicle trajectories for travel time minimization and road congestion avoidance[J]. ACM Transactions on Cyber-Physical Systems, 2019, 4(2):1-25.
[15] GUAN, Y, ANNASWAMY M A, TSENG H E. A dynamic routing framework for shared mobility services[J]. ACM Transactions on Cyber-Physical Systems, 2019, 4(1):1-28.
[16] DING J W, WANG C F, MENG F H, et al. Real-time vehicle route guidance using vehicle-to-vehicle communication[J]. IET Communications, 2010, 4(7): 870-883.
[17] LI C X, ANAVATTI S G, RAY T. Analytical hierarchy process using fuzzy inference technique for real-time route guidance system[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 15(1):84-93.
[18] PAN J, POPA I S, ZEITOUNI K, et al. Proactive vehicular traffic rerouting for lower travel time[J]. IEEE Transactions on Vehicular. Technology, 2013, 62(8):3551-3568.
[19] YE P J, CHEN C, ZHU F H. Dynamic route guidance using maximum flow theory and its MapReduce implementation[C].2011 14th International IEEE Conference on Intelligent Transportation Systems. Washington D C, USA,2011:180-185.
[20] DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numer. Math.1959,1(1):269-271.
[21] HART P E, NILSSON N J, RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Trans Syst Sci Cybern,1968,4(2):100-107.
[22] PILLAC V, GENDREAU M, GUÉRET C, et al. A review of dynamic vehicle routing problems[J]. European Journal of Operational Research, 2013, 225(1):1-11.
[23] PARULEKAR M, PADTE V, SHAH T, et al. Automatic vehicle navigation using Dijkstra's Algorithm[C].2013 International Conference on Advances in Technology and Engineering (ICATE). Mumbai, India, 2013:1-5.
[24] GUO Z G, ZHANG Y F, LV J X, et al. An online learning collaborative method for traffic forecasting and routing optimization[J]. IEEE Transactions on Intelligent Transportation Systems (Early Access), 2020.
[25] ZHU F H, LV Y S, CHEN Y Y, et al. Parallel transportation systems: toward IoT-enabled smart urban traffic control and management[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(10):4063-4071.
[26] HORVÁTH M T, TAMÁS T, ISTVÁn V. Multiobjective dynamic routing with predefined stops for automated vehicles[J]. International Journal of Computer Integrated Manufacturing, 2019, 3(4/5): 396-405.
[27] LI K, CHEN L S, SHANG S. Towards alleviating traffic congestion: optimal route planning for massive-scale trips[C].Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20). Yokohama, Japan, 2021.
[28] KOH, S S, ZHOU B, YANG P, et al. Reinforcement learning for vehicle route optimization in SUMO[C].2018 IEEE 4th International Conference on Data Science and Systems. Exeter, UK, 2018:1468-1473.
[29] 周志华, 机器学习[M]. 北京:清华大学出版社, 2016:371-393.
[30] LOPEZ P A, BEHRISCH M, BIEKER-WALZ L, et al. Microscopic traffic simulation using SUMO[C].2018 21st International Conference on Intelligent Transportation Systems. Maui, HI, USA, 2018:2575-2582.
[31] 魏明, 杨方廷, 曹正清. 交通仿真的发展及研究现状[J].系统仿真学报,2003(8):1179-1183,1187.
WEI M, YANG F Y, CAO Z Q. A review of development and study on the traffic simulation[J]. Journal of System Simulation, 2003, 8:1179-1183,1187.
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