Volume 22 Issue 3
Jun.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 19-40
SHANGGUAN Wei, LI Xin, CHAI Lin-guo, CAO Yue, CHEN Jing-jing, PANG Hao-jie, RUI Tao. Research review on simulation and test of mixed traffic swarm in vehicle-infrastructure cooperative environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 19-40. doi: 10.19818/j.cnki.1671-1637.2022.03.002
Citation: SHANGGUAN Wei, LI Xin, CHAI Lin-guo, CAO Yue, CHEN Jing-jing, PANG Hao-jie, RUI Tao. Research review on simulation and test of mixed traffic swarm in vehicle-infrastructure cooperative environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 19-40. doi: 10.19818/j.cnki.1671-1637.2022.03.002
shu

Research review on simulation and test of mixed traffic swarm in vehicle-infrastructure cooperative environment

doi: 10.19818/j.cnki.1671-1637.2022.03.002

  • School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

More Information
  • Author Bio:

    SHANGGUAN Wei(1979-), male, professor, PhD, wshg@bjtu.edu.cn

  • Received Date: 2021-12-27
  • Publish Date: 2022-06-25
    Abstract
  • The developments of vehicle-infrastructure cooperation and corresponding simulation and test technologies were summarized, and the simulation requirements, classical methods, and technical bottlenecks in the rudiment, infancy, and developing stages were discussed with a focus on the typical simulation results. A new three-layer virtual-real interactive simulation and test framework was proposed based on the traffic subject modeling, swarm behavior simulation, and test result analysis. According to the simulation requirements of mixed traffic subjects, a model for the heterogeneous traffic subjects was constructed, and the operation mechanism of mixed traffic was analyzed to serve as the underlying model support for the simulation system. With the designed virtual-real interactive simulation and test framework, breakthroughs were accomplished in the scenario generation technology for the mixed traffic swarm intelligence, and a simulation method for the mixed traffic swarm intelligence was put forward. Then, simulation tests of decision-making and control methods for different swarm intelligences were carried out in the selected typical traffic scenarios, such as intersections and road sections, to verify the effectiveness of the proposed method. Finally, the future development directions of vehicle-infrastructure cooperation and corresponding suggestions were summarized. Research results show that show that compared with the traditional simulation and test method, the proposed virtual-real interactive simulation and test method reduces the system's simulation granularity from 500 ms to less than 100 ms, the simulation scale increases from 9 nodes and 500 traffic subjects to 150 nodes and 2 000 traffic subjects, and the number of simulated scenarios enhances from 36 to 98. The dynamic adjustment within a range of 0-100% penetration rate of heterogeneous traffic subjects is achieved, and the efficiency, scale, and coverage of the vehicle-infrastructure cooperative simulation and test of mixed traffic are effectively improved. The requirements of vehicle-infrastructure cooperative simulation and test in the new mixed traffic environment are rapidly evolving towards the larger swarm, higher intelligence, and larger scale. Carrying out research on the method and technology for the simulation and test on the vehicle-infrastructure cooperative swarm intelligence based on the virtual-real interaction and operating environment data simulation will effectively promote the development of the next generation of the intelligent traffic system.

     

    • FullText(HTML)
  • loading
    • References(56)
  • [1]
    ZHANG Yi, YAO Dan-ya, LI Li, et al. Technology and application of intelligent vehicle-infrastructure cooperation systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 40-51. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105006.htm
    [2]
    TIAN Bin, ZHAO Xiang-mo, XU Zhi-gang, et al. NRT-V2X: adaptive data dissemination protocol for traffic efficiency of connected and automated highways[J]. China Journal of Highway and Transport, 2019, 32(6): 293-307. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906030.htm
    [3]
    PETTY K F, NOEIMI H, SANWAL K, et al. The freeway service patrol evaluation project: database support programs, and accessibility[J]. Transportation Research Part C: Emerging Technologies, 1996, 4(2): 71-85. doi: 10.1016/0968-090X(96)00001-0
    [4]
    HIROSHIMA Y. Development of collision danger reasoning algorithm, Part 5: the basic technology for ASV (advanced safety vehicle)[J]. JSAE Review, 1995, 16(1): 221-224.
    [5]
    FUKUSHIMA M. The latest trend of V2X driver assistance systems in Japan[J]. Computer Networks, 2011, 55(14): 3134-3141. doi: 10.1016/j.comnet.2011.03.012
    [6]
    WEIB C. V2X communication in Europe—from research projects towards standardization and field testing of vehicle communication technology[J]. Computer Networks, 2011, 55(14): 3103-3119. doi: 10.1016/j.comnet.2011.03.016
    [7]
    ZOU Zhi-jun, YANG Dong-yuan. Preliminary study on dynamic traffic microsimulation[J]. Journal of Tongji University (Natural Science), 1999, 27(3): 305-308. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ199903011.htm
    [8]
    CHUNG-MAN HO C M, GENTLE J E. A comparison of clock pulse and event algorithms for simulation of traffic flow[J]. ACM SIGSIM Simulation Digest, 1976, 8(1): 53-55. doi: 10.1145/1103189.1103204
    [9]
    WADA S, HAYAKAWA H. Kink solution in a fluid model of traffic flow[J]. Journal of the Physical Society of Japan, 1998, 67(3): 763-766. doi: 10.1143/JPSJ.67.763
    [10]
    DEL CASTILLO J M, PINTADO P, BENITEZ F G. The reaction-time of drivers and the stability of traffic flow[J]. Transportation Research Part B: Methodological, 1994, 28(1): 35-60. doi: 10.1016/0191-2615(94)90030-2
    [11]
    PAPAGEORGIOU M. Some remarks on macroscopic traffic flow modelling[J]. Transportation Research Part A: Policy and Practice, 1998, 32(5): 323-329. doi: 10.1016/S0965-8564(97)00048-7
    [12]
    DANIŠOVIČ P, JANČAŘÍKOVÁ E, ŠRÁMEK J, et al. Fire spread models and tunnel traffic & operation simulator[J]. Procedia Engineering, 2017, 19(2): 92-95.
    [13]
    KRONJÄGER W, KONHÄUSER P. Applied traffic flow simulation[J]. IFAC Proceedings Volumes, 1997, 30(8): 777-780. doi: 10.1016/S1474-6670(17)43916-4
    [14]
    PIPES L A. An operational analysis of traffic dynamics[J]. Journal of Applied Physics, 1953, 24(3): 274-281. doi: 10.1063/1.1721265
    [15]
    FUKUI M, ISHIBASH Y. Traffic flow in 1D cellular automaton model including cars moving with high speed[J]. Journal of the Physical Society of Japan, 1996, 65(6): 1868-1870. doi: 10.1143/JPSJ.65.1868
    [16]
    KRAUSS, WAGNER P, GAWRON C. Metastable states in a microscopic model of traffic flow[J]. Physical Review E, 1997, 55(5): 5597-5602. doi: 10.1103/PhysRevE.55.5597
    [17]
    BARLOVIC R, SANTEN L, SCHADSCHNEIDER A, et al. Metastable states in cellular automata for traffic flow[J]. The European Physical Journal B—Condensed Matter and Complex Systems, 1998, 5(3): 793-800. doi: 10.1007/s100510050504
    [18]
    WONG S C, WONG W T, LEUNG C M, et al. Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control[J]. Transportation Research Part B: Methodological, 2002, 36(4): 291-312. doi: 10.1016/S0191-2615(01)00004-2
    [19]
    PARK B B, SCHNEEBERGER J D. Microscopic simulation model calibration and validation: case study of VISSIM simulation model for a coordinated actuated signal system[J]. Transportation Research Record, 2003(1856): 185-192.
    [20]
    KARAGIANNIS G, ALTINTAS O, EKICI E, et al. Vehicular networking: a survey and tutorial on requirements, architectures, challenges, standards and solutions[J]. IEEE Communications Surveys and Tutorials, 2011, 13(4): 584-616. doi: 10.1109/SURV.2011.061411.00019
    [21]
    MANIVANNAN P V, RAMAKANTH P. Vision based intelligent vehicle steering control using single camera for automated highway system[J]. Procedia Computer Science, 2018, 133: 839-846. doi: 10.1016/j.procs.2018.07.111
    [22]
    VIVO G, DALMASSO P, VERNACCHIA F. The European Integrated Project "SAFESPOT"—How ADAS applications co-operate for the driving safety[C]//IEEE. 2007 IEEE Intelligent Transportation Systems Conference. New York: IEEE, 2007: 534-539.
    [23]
    SIKDAR B. Comparison of broadcasting schemes for infrastructure to vehicular communications[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 492-502. doi: 10.1109/TITS.2011.2182193
    [24]
    ZHANG Han, CAI Bai-gen, SHANGGUAN Wei, et al. MR-based CVIS scenario design and implementation[J]. System Simulation Technology, 2013, 9(1): 52-60. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFJ201301010.htm
    [25]
    GIPPS P G. A behavioural car-following model for computer simulation[J]. Transportation Research Part B: Methodological, 1981, 15(2): 105-111. doi: 10.1016/0191-2615(81)90037-0
    [26]
    PETROV P, NASHASHIBI F. Modeling and nonlinear adaptive control for autonomous vehicle overtaking[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4): 1643-1656. doi: 10.1109/TITS.2014.2303995
    [27]
    BUTAKOV V A, IOANNOU P. Personalized driver/vehicle lane change models for ADAS[J]. IEEE Transactions on Vehicular Technology, 2015, 64(10): 4422-4431. doi: 10.1109/TVT.2014.2369522
    [28]
    CAI Bai-gen, WANG Cong-cong, SHANGGUAN Wei, et al. Simulation method of information interaction in CVIS[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 111-119. (in Chinese) http://transport.chd.edu.cn/article/id/201403015
    [29]
    TOUTOUH J, GARCÍA-NIETO J, ALBA E. Intelligent OLSR routing protocol optimization for VANETs[J]. IEEE Transactions on Vehicular Technology, 2012, 61(4): 1884-1894. doi: 10.1109/TVT.2012.2188552
    [30]
    KHOKHAR R H, NGADI M A, LATIFF M S, et al. Multi-criteria receiver self-election scheme for optimal packet forwarding in vehicular ad hoc networks[J]. International Journal of Computers Communication and Control, 2014, 7(5): 865. doi: 10.15837/ijccc.2012.5.1342
    [31]
    ZHOU Lian-ke, ZUO De-cheng, CUI Gang, et al. A node-traffic characteristics considered clustering broadcast protocol for VANETs[J]. Chinese High Technology Letters, 2012(5): 468-476. (in Chinese)
    [32]
    LI Si-hui, CAI Bai-gen, SHANGGUAN Wei, et al. Multi-resolution information exchange method in cooperation vehicle-infrastructure system[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 50-57. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201406008.htm
    [33]
    CHENG S T, HORNG G J, CHOU C L. Using cellular automata to form car society in vehicular ad hoc networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1374-1384. doi: 10.1109/TITS.2011.2158211
    [34]
    KALRA N, PADDOCK S M. Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability?[J]. Transportation Research Part A: Policy and Practice, 2016, 94: 182-193. doi: 10.1016/j.tra.2016.09.010
    [35]
    BRIEFS U. Mcity grand opening[J]. Research Review, 2015, 46(3): 1-2.
    [36]
    XU Hui-le, ZHANG Yi, LI Li, et al. Cooperative driving at unsignalized intersections using tree search[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(11): 4563-4571. doi: 10.1109/TITS.2019.2940641
    [37]
    GUO Qiang-qiang, BAN Xue-gang. Macroscopic fundamental diagram based perimeter control considering dynamic user equilibrium[J]. Transportation Research Part B: Methodological, 2020, 136: 87-109. doi: 10.1016/j.trb.2020.03.004
    [38]
    GHIASI A, HUSSAIN O, QIAN Zhen, et al. A mixed traffic capacity analysis and lane management model for connected automated vehicles: a Markov chain method[J]. Transportation Research Part B: Methodological, 2017, 106: 266-292. doi: 10.1016/j.trb.2017.09.022
    [39]
    YANG Chao, LOU Wei, LIU Yi, et al. Resource allocation for edge computing-based vehicle platoon on freeway: a contract-optimization approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15988-16000. doi: 10.1109/TVT.2020.3039851
    [40]
    XU Li-wei, ZHUANG Wei-chao, YIN Guo-dong, et al. Energy-oriented cruising strategy design of vehicle platoon considering communication delay and disturbance[J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 34-53. doi: 10.1016/j.trc.2019.07.019
    [41]
    WANG Zhu-wei, GAO Yu, FANG Chao, et al. Optimal control design for connected cruise control with stochastic communication delays[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15357-15369. doi: 10.1109/TVT.2020.3040321
    [42]
    ZHONG Zi-jia, LEE J Y. The effectiveness of managed lane strategies for the near-term deployment of cooperative adaptive cruise control[J]. Transportation Research Part A: Policy and Practice, 2019, 129: 257-270. doi: 10.1016/j.tra.2019.08.015
    [43]
    GE J I, OROSZ G. Connected cruise control among human- driven vehicles: experiment-based parameter estimation and optimal control design[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 445-459. doi: 10.1016/j.trc.2018.07.021
    [44]
    HAO Liu, KAN Xing-an, SHLADOVER S E, et al. Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 261-279. doi: 10.1016/j.trc.2018.07.027
    [45]
    ZHANG Lin-jun. Cooperative adaptive cruise control in mixed traffic with selective use of vehicle-to-vehicle communication[J]. IET Intelligent Transport Systems, 2018, 12(10): 1243-1254. doi: 10.1049/iet-its.2018.5235
    [46]
    XIAO Lin, WANG Meng, SCHAKEL W, et al. Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks[J]. Transportation Research Part C: Emerging Technologies, 2018, 96: 380-397. doi: 10.1016/j.trc.2018.10.008
    [47]
    GONG Si-yuan, DU Li-li. Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles[J]. Transportation Research Part B: Methodological, 2018, 116: 25-61. doi: 10.1016/j.trb.2018.07.005
    [48]
    LIN Gui-hua, HU Yu, ZOU Yuan-yang. A mixed-mode traffic assignment model with new time-flow impedance function[J]. International Journal of Modern Physics B, 2018, 32(3): 173-185.
    [49]
    HUANG Dong-dong, CUI Miao, ZHANG Guang-chi, et al. Trajectory optimization and resource allocation for UAV base stations under in-band backhaul constraint[J]. EURASIP Journal on Wireless Communications and Networking, 2020, 2020: 831-845.
    [50]
    CHAI Lin-guo, CAI Bai-gen, SHANGGUAN Wei, et al. A construction approach based on kinematic simulation environment for networked intelligent vehicle[J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(1): 66-77. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201801010.htm
    [51]
    CHAI Lin-guo, CAI Bai-gen, SHANGGUAN Wei, et al. Connected and autonomous vehicles coordinating approach at intersection based on space-time slot[J]. Transportmetrica A: Transport Science, 2018, 14(10): 929-951. doi: 10.1080/23249935.2018.1452308
    [52]
    CHEN Jun-jie, CAI Bai-gen, SHANGGUAN Wei, et al. Slot control optimization of intelligent platoon for dual-lane two-way overtaking behavior[J]. Journal of Traffic and Transportation Engineering, 2019, 19(2): 178-190. (in Chinese) http://transport.chd.edu.cn/article/id/201902016
    [53]
    FENG Yi-heng, YU Chun-hui, XU Shao-bing, et al. An augmented reality environment for connected and automated vehicle testing and evaluation[C]//IEEE. 2018 IEEE Intelligent Vehicles Symposium (IV). New York: IEEE, 2018: 1549-1554.
    [54]
    QIU Wei-zhi, SHANGGUAN Wei, CAI Bai-gen, et al. Advance estimate-based traffic state synchronization for parallel testing[C]//IEEE. 2020 IEEE 23rd International Conference on Intelligent Transportation System. New York: IEEE, 2020: 1-6.
    [55]
    QIU Wei-zhi, SHANGGUAN Wei, CHAI Lin-guo, et al. Parallel hierarchical control-based efficiency enhancement for large-scale virtual reality traffic simulation[J]. IEEE Intelligent Transportation Systems Magazine, 2021, DOI: 10.1109/MITS.2021.3051473.
    [56]
    LI Li, WANG Xiao, WANG Kun-feng, et al. Parallel testing of vehicle intelligence via virtual-real interaction[J]. Science Robotics, 2019, 4(28): 4106. doi: 10.1126/scirobotics.aaw4106
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (3054) PDF downloads(419) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return