A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 11 Issue 10
Oct.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(10): 2043-2058
Q. Ji, X. Wen, J. Jin, Y. Zhu, and  Y. Lv,  “Urban traffic control meets decision recommendation system: A survey and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 10, pp. 2043–2058, Oct. 2024. doi: 10.1109/JAS.2024.124659
Citation: Q. Ji, X. Wen, J. Jin, Y. Zhu, and  Y. Lv,  “Urban traffic control meets decision recommendation system: A survey and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 10, pp. 2043–2058, Oct. 2024. doi: 10.1109/JAS.2024.124659
shu

Urban Traffic Control Meets Decision Recommendation System: A Survey and Perspective

doi: 10.1109/JAS.2024.124659
Funds:  This work was supported by the National Key Research and Development Program of China (2021YFB2900200), the Key Research and Development Program of Science and Technology Department of Zhejiang Province (2022C01121), and Zhejiang Provincial Department of Transport Research Project (ZJXL-JTT-202223)
More Information
    Abstract

  • Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems. Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level, utilizing their knowledge and expertise. However, this process is cumbersome, labor-intensive, and cannot be applied on a large network scale. Recent studies have begun to explore the applicability of recommendation system for urban traffic control, which offer increased control efficiency and scalability. Such a decision recommendation system is complex, with various interdependent components, but a systematic literature review has not yet been conducted. In this work, we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control, demonstrates the utility and efficacy of such a system in the real world using data and knowledge-driven approaches, and discusses the current challenges and potential future directions of this field.

     

    • FullText(HTML)
  • loading
    • References(125)
  • [1]
    K. Aboudolas, M. Papageorgiou, A. Kouvelas, and E. Kosmatopoulos, “A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks,” Transp. Res. C: Emerging Technol., vol. 18, no. 5, pp. 680–694, Oct. 2010. doi: 10.1016/j.trc.2009.06.003
    [2]
    J. Jin, X. Ma, and I. Kosonen, “A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation,” Adv. Eng. Software, vol. 114, pp. 348–360, Dec. 2017. doi: 10.1016/j.advengsoft.2017.08.005
    [3]
    J. Jin, H. Guo, J. Xu, X. Wang, and F.-Y. Wang, “An end-to-end recommendation system for urban traffic controls and management under a parallel learning framework,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1616–1626, Mar. 2021. doi: 10.1109/TITS.2020.2973736
    [4]
    T. Li, X. Han, and J. Ma, “Cooperative perception for estimating and predicting microscopic traffic states to manage connected and automated traffic,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 13694–13707, Aug. 2022. doi: 10.1109/TITS.2021.3126621
    [5]
    B. Varga, D. Doba, and T. Tettamanti, “Optimizing vehicle dynamics co-simulation performance by introducing mesoscopic traffic simulation,” Simul. Modell. Pract. Theory, vol. 125, p. 102739, May 2023. doi: 10.1016/j.simpat.2023.102739
    [6]
    R. He, Y. Xiao, X. Lu, S. Zhang, and Y. Liu, “ST-3DGMR: Spatio-temporal 3D grouped multiscale ResNet network for region-based urban traffic flow prediction,” Inf. Sci., vol. 624, pp. 68–93, May 2023. doi: 10.1016/j.ins.2022.12.066
    [7]
    M. Keyvan-Ekbatani, M. Papageorgiou, and V. L. Knoop, “Controller design for gating traffic control in presence of time-delay in urban road networks,” Transp. Res. Procedia, vol. 7, pp. 651–668, Aug. 2015.
    [8]
    X. Yin, G. Wu, J. Wei, Y. Shen, H. Qi, and B. Yin, “Deep learning on traffic prediction: Methods, analysis, and future directions,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 4927–4943, Jun. 2022. doi: 10.1109/TITS.2021.3054840
    [9]
    W. Wu, J. Zhang, A. Luo, and J. Cao, “Distributed mutual exclusion algorithms for intersection traffic control,” IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 1, pp. 65–74, Jan. 2015. doi: 10.1109/TPDS.2013.2297097
    [10]
    F. Mao, Z. Li, Y. Lin, and L. Li, “Mastering arterial traffic signal control with multi-agent attention-based soft actor-critic model,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 3, pp. 3129–3144, Mar. 2023. doi: 10.1109/TITS.2022.3229477
    [11]
    E. V. Butilă and R. G. Boboc, “Urban traffic monitoring and analysis using unmanned aerial vehicles (UAVs): A systematic literature review,” Remote Sens., vol. 14, no. 3, p. 620, Jan. 2022. doi: 10.3390/rs14030620
    [12]
    M. S. Hossain, H. Sinha, and R. Mustafa, “A belief rule based expert system to control traffic signals under uncertainty,” in Proc. Int. Conf. Computer and Information Engineering, Rajshahi, Bangladesh, 2015, pp. 83–86.
    [13]
    B.-L. Ye, W. Wu, K. Ruan, L. Li, T. Chen, H. Gao, and Y. Chen, “A survey of model predictive control methods for traffic signal control,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 623–640, May 2019. doi: 10.1109/JAS.2019.1911471
    [14]
    S. S. S. M. Qadri, M. A. Gökçe, and E. Öner, “State-of-art review of traffic signal control methods: Challenges and opportunities,” Eur. Transp. Res. Rev., vol. 12, p. 55, Oct. 2020. doi: 10.1186/s12544-020-00439-1
    [15]
    H. Wei, G. Zheng, V. Gayah, and Z. Li, “Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation,” ACM SIGKDD Explor. Newsl., vol. 22, no. 2, pp. 12–18, Jan. 2021. doi: 10.1145/3447556.3447565
    [16]
    X. Fan, C. Xiang, L. Gong, X. He, Y. Qu, S. Amirgholipour, Y. Xi, P. Nanda, and X. He, “Deep learning for intelligent traffic sensing and prediction: Recent advances and future challenges,” CCF Trans. Pervasive Comput. Interact., vol. 2, no. 4, pp. 240–260, Sept. 2020. doi: 10.1007/s42486-020-00039-x
    [17]
    F. Zhu, Y. Lv, Y. Chen, X. Wang, G. Xiong, and F.-Y. Wang, “Parallel transportation systems: Toward IoT-enabled smart urban traffic control and management,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 10, pp. 4063–4071, Oct. 2020. doi: 10.1109/TITS.2019.2934991
    [18]
    A. M. Nagy and V. Simon, “Survey on traffic prediction in smart cities,” Pervasive Mobile Comput., vol. 50, pp. 148–163, Oct. 2018. doi: 10.1016/j.pmcj.2018.07.004
    [19]
    Y. Chen, Y. Lv, and F.-Y. Wang, “Traffic flow imputation using parallel data and generative adversarial networks,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 4, pp. 1624–1630, Apr. 2020. doi: 10.1109/TITS.2019.2910295
    [20]
    A. Haghighat and A. Sharma, “A computer vision-based deep learning model to detect wrong-way driving using panɃtiltɃzoom traffic cameras,” Comput. Aided Civil Infrastruct. Eng., vol. 38, no. 1, pp. 119–132, Jan. 2023. doi: 10.1111/mice.12819
    [21]
    L. Chen, I. Grimstead, D. Bell, J. Karanka, L. Dimond, P. James, L. Smith, and A. Edwardes, “Estimating vehicle and pedestrian activity from town and city traffic cameras,” Sensors, vol. 21, no. 13, p. 4564, Jul. 2021. doi: 10.3390/s21134564
    [22]
    M. Umair, M. U. Farooq, R. H. Raza, Q. Chen, and B. Abdulhai, “Efficient video-based vehicle queue length estimation using computer vision and deep learning for an urban traffic scenario,” Processes, vol. 9, no. 10, p. 1786, Oct. 2021. doi: 10.3390/pr9101786
    [23]
    C. Meng, X. Yi, L. Su, J. Gao, and Y. Zheng, “City-wide traffic volume inference with loop detector data and taxi trajectories,” in Proc. 25th ACM SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, Redondo Beach, USA, 2017, pp. 1.
    [24]
    Z. Jiang, X. Chen, and Y. Ouyang, “Traffic state and emission estimation for urban expressways based on heterogeneous data,” Transp. Res. D: Transp. Environ., vol. 53, pp. 440–453, Jun. 2017. doi: 10.1016/j.trd.2017.04.042
    [25]
    J. Li, J. Boonaert, A. Doniec, and G. Lozenguez, “Multi-models machine learning methods for traffic flow estimation from floating car data,” Transp. Res. C: Emerging Technol., vol. 132, p. 103389, Nov. 2021. doi: 10.1016/j.trc.2021.103389
    [26]
    K. P. OoKeeffe, A. Anjomshoaa, S. H. Strogatz, P. Santi, and C. Ratti, “Quantifying the sensing power of vehicle fleets,” Proc. Natl. Acad. Sci. USA, vol. 116, no. 26, pp. 12752–12757, Jun. 2019. doi: 10.1073/pnas.1821667116
    [27]
    Y. Yu, Y. Cui, J. Zeng, C. He, and D. Wang, “Identifying traffic clusters in urban networks based on graph theory using license plate recognition data,” Phys. A: Stat. Mech. Appl., vol. 591, p. 126750, Apr. 2022. doi: 10.1016/j.physa.2021.126750
    [28]
    H. Zhang, G. Luo, Y. Li, and F.-Y. Wang, “Parallel vision for intelligent transportation systems in metaverse: Challenges, solutions, and potential applications,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 6, pp. 3400–3413, Jun. 2023. doi: 10.1109/TSMC.2022.3228314
    [29]
    L. Bao, Q. Wang, and Y. Jiang, “Review of digital twin for intelligent transportation system,” in Proc. Int. Conf. Information Control, Electrical Engineering and Rail Transit, Lanzhou, China, 2021, pp. 309–315.
    [30]
    P. Zhou, J. Zhu, Y. Wang, Y. Lu, Z. Wei, H. Shi, Y. Ding, Y. Gao, Q. Huang, Y. Shi, A. Alhilal, L. H. Lee, T. Braud, P. Hui, and L. Wang, “Vetaverse: A survey on the intersection of metaverse, vehicles, and transportation systems,” arXiv preprint arXiv: 2210.15109, 2023.
    [31]
    J. N. Njoku, C. I. Nwakanma, G. C. Amaizu, and D.-S. Kim, “Prospects and challenges of metaverse application in data-driven intelligent transportation systems,” IET Intell. Transp. Syst., vol. 17, no. 1, pp. 1–21, Jan. 2023. doi: 10.1049/itr2.12252
    [32]
    C. Zhao, X. Wang, Y. Lv, Y. Tian, Y. Lin, and F.-Y. Wang, “Parallel transportation in transverse: From foundation models to DeCAST,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 12, pp. 15310–15327, Dec. 2023. doi: 10.1109/TITS.2023.3311585
    [33]
    C. Zhao, X. Dai, Y. Lv, Y. Tian, Y. Ren, and F.-Y. Wang, “Foundation models for transportation intelligence: ITS convergence in transverse,” IEEE Intell. Syst., vol. 37, no. 6, pp. 77–82, Nov.–Dec. 2022. doi: 10.1109/MIS.2022.3221342
    [34]
    G. Chang, Y. Zhang, D. Yao, and Y. Yue, “A summary of short-term traffic flow forecasting methods,” in Proc. Towards Sustainable Transportation Systems, pp. 1696–1707, Nanjing, China, 2011.
    [35]
    X. Luo, L. Niu, and S. Zhang, “An algorithm for traffic flow prediction based on improved SARIMA and GA,” KSCE J. Civ. Eng., vol. 22, no. 10, pp. 4107–4115, May 2018. doi: 10.1007/s12205-018-0429-4
    [36]
    S. Zhang, Z. Kang, Z. Zhang, C. Lin, C. Wang, and J. Li, “A hybrid model for forecasting traffic flow: Using layerwise structure and Markov transition matrix,” IEEE Access, vol. 7, pp. 26002–26012, Feb. 2019. doi: 10.1109/ACCESS.2019.2901118
    [37]
    A. Belhadi, Y. Djenouri, D. Djenouri, and J. C.-W. Lin, “A recurrent neural network for urban long-term traffic flow forecasting,” Appl. Intell., vol. 50, no. 10, pp. 3252–3265, May 2020. doi: 10.1007/s10489-020-01716-1
    [38]
    V. Osipov, V. Nikiforov, N. Zhukova, and D. Miloserdov, “Urban traffic flows forecasting by recurrent neural networks with spiral structures of layers,” Neural Comput. Appl., vol. 32, no. 18, pp. 14885–14897, Mar. 2020. doi: 10.1007/s00521-020-04843-5
    [39]
    Z. Sun, Y. Hu, W. Li, S. Feng, and L. Pei, “Prediction model for short-term traffic flow based on a k-means-gated recurrent unit combination,” IET Intell. Transp. Syst., vol. 16, no. 5, pp. 675–690, May 2022. doi: 10.1049/itr2.12165
    [40]
    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. 31st Int. Conf. Neural Information Processing Systems, Long Beach, USA, 2017, pp. 6000–6010.
    [41]
    C. Chen, Y. Liu, L. Chen, and C. Zhang, “Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting,” IEEE Trans. Neural Networks Learn. Syst., vol. 34, no. 10, pp. 6913–6925, Oct. 2023. doi: 10.1109/TNNLS.2022.3183903
    [42]
    H. Yan, X. Ma, and Z. Pu, “Learning dynamic and hierarchical traffic spatiotemporal features with transformer,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 22386–22399, Nov. 2022. doi: 10.1109/TITS.2021.3102983
    [43]
    G. Li, S. Zhong, X. Deng, L. Xiang, S.-H. G. Chan, R. Li, Y. Liu, M. Zhang, C.-C. Hung, and W.-C. Peng, “A lightweight and accurate spatial-temporal transformer for traffic forecasting,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 11, pp. 10967–10980, Nov. 2023. doi: 10.1109/TKDE.2022.3233086
    [44]
    M. Lee, H. Barbosa, H. Youn, P. Holme, and G. Ghoshal, “Morphology of travel routes and the organization of cities,” Nat. Commun., vol. 8, no. 1, p. 2229, Dec. 2017. doi: 10.1038/s41467-017-02374-7
    [45]
    J. Hackl and B. T. Adey, “Estimation of traffic flow changes using networks in networks approaches,” Appl. Network Sci., vol. 4, no. 1, p. 28, May 2019. doi: 10.1007/s41109-019-0139-y
    [46]
    J. Xue, N. Jiang, S. Liang, Q. Pang, T. Yabe, S. V. Ukkusuri, and J. Ma, “Quantifying the spatial homogeneity of urban road networks via graph neural networks,” Nat. Mach. Intell., vol. 4, no. 3, pp. 246–257, Mar. 2022. doi: 10.1038/s42256-022-00462-y
    [47]
    T. S. Jepsen, C. S. Jensen, and T. D. Nielsen, “Graph convolutional networks for road networks,” in Proc. 27th ACM SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, Chicago, USA, 2019, pp. 460–463.
    [48]
    L. Han, B. Du, L. Sun, Y. Fu, Y. Lv, and H. Xiong, “Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting,” in Proc. 27th ACM SIGKDD Conf. Knowledge Discovery & Data Mining, Singapore, Singapore, 2021, pp. 547–555.
    [49]
    S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in Proc. 33rd AAAI Conf. Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conf. and 9th AAAI Symp. Educational Advances in Artificial Intelligence, Honolulu, USA, 2019, pp. 114.
    [50]
    C. Zheng, X. Fan, C. Wang, and J. Qi, “GMAN: A graph multi-attention network for traffic prediction,” in Proc. 34th AAAI Conf. Artificial Intelligence, New York, USA, 2020, pp. 1234–1241.
    [51]
    K. Guo, Y. Hu, Y. Sun, S. Qian, J. Gao, and B. Yin, “Hierarchical graph convolution network for traffic forecasting,” in Proc. 35th AAAI Conf. Artificial Intelligence, Vancouver, Canada, 2021, pp. 151–159.
    [52]
    J. Zhu, X. Han, H. Deng, C. Tao, L. Zhao, P. Wang, T. Lin, and H. Li, “KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 15055–15065, Sept. 2022. doi: 10.1109/TITS.2021.3136287
    [53]
    B. Lu, X. Gan, H. Jin, L. Fu, and H. Zhang, “Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting,” in Proc. 29th ACM Int. Conf. Information and Knowledge Management, Galway, Ireland, 2020, pp. 1025–1034.
    [54]
    Q. Ji and J. Jin, “Reasoning traffic pattern knowledge graph in predicting real-time traffic congestion propagation,” IFAC-PapersOnLine, vol. 53, no. 5, pp. 578–581, Dec. 2020.
    [55]
    J. Jin, D. Rong, Y. Pang, P. Ye, Q. Ji, X. Wang, G. Wang, and F.-Y. Wang, “An agent-based traffic recommendation system: Revisiting and revising urban traffic management strategies,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 11, pp. 7289–7301, Nov. 2022. doi: 10.1109/TSMC.2022.3177027
    [56]
    E. Ozatay, U. Ozguner, and D. Filev, “Velocity profile optimization of on road vehicles: Pontryaginos maximum principle based approach,” Control Eng. Pract., vol. 61, pp. 244–254, Apr. 2017. doi: 10.1016/j.conengprac.2016.09.006
    [57]
    Z. Y. Yang and Z. J. Ding, “Actuated green wave control for grid-like network traffic signal coordination,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Budapest, Hungary, 2016, pp. 953–958.
    [58]
    M. Tajalli, M. Mehrabipour, and A. Hajbabaie, “Network-level coordinated speed optimization and traffic light control for connected and automated vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 11, pp. 6748–6759, Nov. 2021. doi: 10.1109/TITS.2020.2994468
    [59]
    B.-L. Ye, W. Wu, H. Gao, Y. Lu, Q. Cao, and L. Zhu, “Stochastic model predictive control for urban traffic networks,” Appl. Sci., vol. 7, no. 6, p. 588, Jun. 2017. doi: 10.3390/app7060588
    [60]
    L. B. De Oliveira and E. Camponogara, “Multi-agent model predictive control of signaling split in urban traffic networks,” Transp. Res. C: Emerging Technol., vol. 18, no. 1, pp. 120–139, Feb. 2010. doi: 10.1016/j.trc.2009.04.022
    [61]
    Z. Hao, R. Boel, and Z. Li, “Model based urban traffic control, part I: Local model and local model predictive controllers,” Transp. Res. C: Emerging Technol., vol. 97, pp. 61–81, Dec. 2018. doi: 10.1016/j.trc.2018.09.026
    [62]
    Z. Hao, R. Boel, and Z. Li, “Model based urban traffic control, part II: Coordinated model predictive controllers,” Transp. Res. C: Emerging Technol., vol. 97, pp. 23–44, Dec. 2018. doi: 10.1016/j.trc.2018.09.025
    [63]
    B.-L. Ye, W. Wu, and W. Mao, “Distributed model predictive control method for optimal coordination of signal splits in urban traffic networks,” Asian J. Control, vol. 17, no. 3, pp. 775–790, May 2015. doi: 10.1002/asjc.1011
    [64]
    D. Li and B. De Schutter, “Distributed model-free adaptive predictive control for urban traffic networks,” IEEE Trans. Control Syst. Technol., vol. 30, no. 1, pp. 180–192, Jan. 2022. doi: 10.1109/TCST.2021.3059460
    [65]
    Y. Safadi and J. Haddad, “Optimal combined traffic routing and signal control in simple road networks: An analytical solution,” Transportmetrica A: Transp. Sci., vol. 17, no. 3, pp. 308–339, 2021.
    [66]
    K. Ampountolas and R. C. Carlson, “Optimal control of motorway tidal flow,” in Proc. 18th European Control Conf., Naples, Italy, 2019, pp. 3680–3685.
    [67]
    G. Como, E. Lovisari, and K. Savla, “Convexity and robustness of dynamic traffic assignment and freeway network control,” Transp. Res. B: Methodol., vol. 91, pp. 446–465, Sept. 2016. doi: 10.1016/j.trb.2016.06.007
    [68]
    A. Aalipour, H. Kebriaei, and M. Ramezani, “Analytical optimal solution of perimeter traffic flow control based on MFD dynamics: A pontryaginos maximum principle approach,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 9, pp. 3224–3234, Sept. 2019. doi: 10.1109/TITS.2018.2873104
    [69]
    D. Zhao, Y. Dai, and Z. Zhang, “Computational intelligence in urban traffic signal control: A survey,” IEEE Trans. Syst. Man Cybern. C Appl. Rev., vol. 42, no. 4, pp. 485–494, Jul. 2012. doi: 10.1109/TSMCC.2011.2161577
    [70]
    K.-H. Chao, R.-H. Lee, and M.-H. Wang, “An intelligent traffic light control based on extension neural network,” in Proc. 12th Int. Conf. Knowledge-Based Intelligent Information and Engineering Systems, Zagreb, Croatia, 2008, pp. 17–24.
    [71]
    G. B. Castro, A. R. Hirakawa, and J. S. C. Martini, “Adaptive traffic signal control based on bio-neural network,” Procedia Comput. Sci., vol. 109, pp. 1182–1187, May 2017.
    [72]
    G. Shen and X. Kong, “Study on road network traffic coordination control technique with bus priority,” IEEE Trans. Syst. Man Cybern. C Appl. Rev., vol. 39, no. 3, pp. 343–351, May 2009. doi: 10.1109/TSMCC.2008.2005842
    [73]
    J. C. Spall and D. C. Chin, “Traffic-responsive signal timing for system-wide traffic control,” Transp. Res. C: Emerging Technol., vol. 5, no. 3–4, pp. 153–163, Aug.–Oct. 1997. doi: 10.1016/S0968-090X(97)00012-0
    [74]
    E. Bingham, “Reinforcement learning in neurofuzzy traffic signal control,” Eur. J. Oper. Res., vol. 131, no. 2, pp. 232–241, Jun. 2001. doi: 10.1016/S0377-2217(00)00123-5
    [75]
    D. Srinivasan, M. C. Choy, and R. L. Cheu, “Neural networks for real-time traffic signal control,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 3, pp. 261–272, Sept. 2006. doi: 10.1109/TITS.2006.874716
    [76]
    M. C. Choy, D. Srinivasan, and R. L. Cheu, “Neural networks for continuous online learning and control,” IEEE Trans. Neural Networks, vol. 17, no. 6, pp. 1511–1531, Nov. 2006. doi: 10.1109/TNN.2006.881710
    [77]
    A. G. Barto and S. Mahadevan, “Recent advances in hierarchical reinforcement learning,” Discrete Event Dyn. Syst., vol. 13, no. 1, pp. 41–77, Jan. 2003.
    [78]
    I. Arel, C. Liu, T. Urbanik, and A. G. Kohls, “Reinforcement learning-based multi-agent system for network traffic signal control,” IET Intell. Transp. Syst., vol. 4, no. 2, pp. 128–135, Jun. 2010. doi: 10.1049/iet-its.2009.0070
    [79]
    M. A. Khamis and W. Gomaa, “Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework,” Eng. Appl. Artif. Intell., vol. 29, pp. 134–151, Mar. 2014. doi: 10.1016/j.engappai.2014.01.007
    [80]
    K.-L. A. Yau, J. Qadir, H. L. Khoo, M. H. Ling, and P. Komisarczuk, “A survey on reinforcement learning models and algorithms for traffic signal control,” ACM Comput. Surv., vol. 50, no. 3, p. 34, Jun. 2017.
    [81]
    P. Mannion, J. Duggan, and E. Howley, “An experimental review of reinforcement learning algorithms for adaptive traffic signal control,” in Autonomic Road Transport Support Systems, T. L. McCluskey, A. Kotsialos, J. P. Müller, F. Klügl, O. Rana, and R. Schumann, Eds. Cham, Germany: Springer, 2016, pp. 47–66.
    [82]
    M. Wiering, “Multi-agent reinforcement leraning for traffic light control,” in Proc. 17th Int. Conf. Machine Learning, San Francisco, USA, 2000, pp. 1151–1158.
    [83]
    E. Van Der Pol and F. A. Oliehoek, “Coordinated deep reinforcement learners for traffic light control,” in Proc. 30th Conf. Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 21–38.
    [84]
    M. Liu, J. Deng, M. Xu, X. Zhang, and W. Wang, “Cooperative deep reinforcement learning for tra ic signal control. (2017),” in 7th International Workshop of Urban Computing, Halifax, Canada, 2017.
    [85]
    L. Li, Y. Lv, and F.-Y. Wang, “Traffic signal timing via deep reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 3, pp. 247–254, Jul. 2016. doi: 10.1109/JAS.2016.7508798
    [86]
    C. Chen, H. Wei, N. Xu, G. Zheng, M. Yang, Y. Xiong, K. Xu, and Z. Li, “Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control,” in Proc. 34th AAAI Conf. Artificial Intelligence, New York, USA, 2020, pp. 3414–3421.
    [87]
    V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015. doi: 10.1038/nature14236
    [88]
    D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016. doi: 10.1038/nature16961
    [89]
    T. Chu, J. Wang, L. Codecà, and Z. Li, “Multi-agent deep reinforcement learning for large-scale traffic signal control,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 1086–1095, Mar. 2020. doi: 10.1109/TITS.2019.2901791
    [90]
    H. Wei, C. Chen, G. Zheng, K. Wu, V. Gayah, K. Xu, and Z. Li, “PressLight: Learning max pressure control to coordinate traffic signals in arterial network,” in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Anchorage, USA, 2019, pp. 1290–1298.
    [91]
    J. Jin, D. Rong, Y. Pang, F. Zhu, H. Guo, X. Ma, and F.-Y. Wang, “Precom: A parallel recommendation engine for control, operations, and management on congested urban traffic networks,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 7332–7342, Jul. 2022. doi: 10.1109/TITS.2021.3068874
    [92]
    Z. Mao, J. Li, N. Zheng, K. Tei, and S. Honiden, “Transfer learning method in reinforcement learning-based traffic signal control,” in Proc. 10th Global Conf. Consumer Electronics, Kyoto, Japan, 2021, pp. 304–307.
    [93]
    N. Xu, G. Zheng, K. Xu, Y. Zhu, and Z. Li, “Targeted knowledge transfer for learning traffic signal plans,” in Proc. 23rd Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining, Macau, China, 2019, pp. 175–187.
    [94]
    F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630–638, Sept. 2010. doi: 10.1109/TITS.2010.2060218
    [95]
    G. Xiong, X. Dong, D. Fan, F. Zhu, K. Wang, and Y. Lv, “Parallel traffic management system and its application to the 2010 Asian games,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 1, pp. 225–235, Mar. 2013. doi: 10.1109/TITS.2012.2210883
    [96]
    Y. Lv, Y. Chen, L. Li, and F.-Y. Wang, “Generative adversarial networks for parallel transportation systems,” IEEE Intell. Transp. Syst. Mag., vol. 10, no. 3, pp. 4–10, Jul. 2018.
    [97]
    Y. F. Zhao, F. Y. Wang, H. Gao, F. H. Zhu, Y. S. Lv, and P. J. Ye, “Content-based recommendation for traffic signal control,” in Proc. 18th Int. Conf. Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 1183–1188.
    [98]
    C. Chen and Z. J. Li, “A hierarchical networked urban traffic signal control system based on multi-agent,” in Proc. 9th IEEE Int. Conf. Networking, Sensing and Control, Beijing, China, 2012, pp. 28–33.
    [99]
    H. Pranevičius and T. Kraujalis, “Knowledge based traffic signal control model for signalized intersection,” Transport, vol. 27, no. 3, pp. 263–267, Sept. 2012.
    [100]
    X. Lu, N. Zhang, C. Tian, B. Yu, and Z. Duan, “A knowledge-based temporal planning approach for urban traffic control,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1907–1918, Mar. 2021. doi: 10.1109/TITS.2020.3041228
    [101]
    I. M. Albatish and S. S. Abu-Naser, “Modeling and controlling smart traffic light system using a rule based system,” in Proc. Int. Conf. Promising Electronic Technologies, Gaza, Palestine, 2019, pp. 55–60.
    [102]
    F. Yang, A. Vereshchaka, Y. Zhou, C. Chen, and W. Dong, “Variational adversarial kernel learned imitation learning,” in Proc. 34th AAAI Conf. Artificial Intelligence, New York, USA, 2020, pp. 6599–6606.
    [103]
    C. Qiu, D. Zhou, Q. Wu, and T. Li, “Imitation learning based deep reinforcement learning for traffic signal control,” in Proc. SPIE 12787, 6th Int. Conf. Advanced Electronic Materials, Computers, and Software Engineering, Shenyang, China, Aug. 2023, pp. 343–349.
    [104]
    X. Li, Z. Guo, X. Dai, Y. Lin, J. Jin, F. Zhu, and F.-Y. Wang, “Deep imitation learning for traffic signal control and operations based on graph convolutional neural networks,” in Proc. 23rd Int. Conf. Intelligent Transportation Systems, Rhodes, Greece, 2020, pp. 1–6.
    [105]
    Y. Huo, Q. Tao, and J. Hu, “Cooperative control for multi-intersection traffic signal based on deep reinforcement learning and imitation learning,” IEEE Access, vol. 8, pp. 199573–199585, Oct. 2020. doi: 10.1109/ACCESS.2020.3034419
    [106]
    W. Li, B. Wang, Z. Liu, Q. Li, and G.-J. Qi, “POINT: Partially observable imitation network for traffic signal control,” Sustain. Cities Soc., vol. 76, p. 103461, Jan. 2022. doi: 10.1016/j.scs.2021.103461
    [107]
    C. P. Pappis and E. H. Mamdani, “A fuzzy logic controller for a trafc junction,” IEEE Trans. Syst. Man Cybern., vol. 7, no. 10, pp. 707–717, Oct. 1977. doi: 10.1109/TSMC.1977.4309605
    [108]
    J. Jin, X. Ma, and I. Kosonen, “An intelligent control system for traffic lights with simulation-based evaluation,” Control Eng. Pract., vol. 58, pp. 24–33, Jan. 2017. doi: 10.1016/j.conengprac.2016.09.009
    [109]
    M. Miletić, B. Kapusta, and E. Ivanjko, “Comparison of two approaches for preemptive traffic light control,” in Proc. Int. Symp. ELMAR, Zadar, Croatia, 2018, pp. 57–62.
    [110]
    S. Araghi, A. Khosravi, D. Creighton, and S. Nahavandi, “Optimal fuzzy traffic signal controller for an isolated intersection,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, San Diego, USA, 2014, pp. 435–440.
    [111]
    A. Vogel, I. Oremović, R. šimić, and E. Ivanjko, “Improving traffic light control by means of fuzzy logic,” in Proc. Int. Symp. ELMAR, Zadar, Croatia, 2018, pp. 51–56.
    [112]
    Y. Bi, X. Lu, Z. Sun, D. Srinivasan, and Z. Sun, “Optimal type-2 fuzzy system for arterial traffic signal control,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 9, pp. 3009–3027, Sept. 2018. doi: 10.1109/TITS.2017.2762085
    [113]
    Y. Qin, W. Hua, J. Jin, J. Ge, X. Dai, L. Li, X. Wang, and F. Y. Wang, “AUTOSIM: Automated urban traffic operation simulation via meta-learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1871–1881, Sept. 2023. doi: 10.1109/JAS.2023.123264
    [114]
    P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using SUMO,” in Proc. 21st Int. Conf. Intelligent Transportation Systems, Maui, USA, 2018, pp. 2575–2582.
    [115]
    T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, “Meta-learning in neural networks: A survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 5149–5169, Sept. 2022.
    [116]
    J. Jin, D. Rong, T. Zhang, Q. Ji, H. Guo, Y. Lv, X. Ma, and F.-Y. Wang, “A GAN-based short-term link traffic prediction approach for urban road networks under a parallel learning framework,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 16185–16196, Sept. 2022. doi: 10.1109/TITS.2022.3148358
    [117]
    X. Li, P. Ye, J. Jin, F. Zhu, and F.-Y. Wang, “Data augmented deep behavioral cloning for urban traffic control operations under a parallel learning framework,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 5128–5137, Jun. 2022. doi: 10.1109/TITS.2020.3048151
    [118]
    Z. Zhang, Y. Sun, Z. Wang, Y. Nie, X. Ma, P. Sun, and R. Li, “Large language models for mobility in transportation systems: A survey on forecasting tasks,” arXiv preprint arXiv: 2405.02357, 2024.
    [119]
    X. Zhou, M. Liu, B. L. Zagar, E. Yurtsever, and A. C. Knoll, “Vision language models in autonomous driving and intelligent transportation systems,” arXiv preprint arXiv: 2310.14414, 2023.
    [120]
    X. Guo, Q. Zhang, J. Jiang, M. Peng, H. Yang, and M. Zhu, “Towards responsible and reliable traffic flow prediction with large language models,” arXiv preprint arXiv: 2404.02937, 2024.
    [121]
    Y. Ren, Y. Chen, S. Liu, B. Wang, H. Yu, and Z. Cui, “TPLLM: A traffic prediction framework based on pretrained large language models,” arXiv preprint arXiv: 2403.02221, 2024.
    [122]
    M. Wang, A. Pang, Y. Kan, M.-O. Pun, C. S. Chen, and B. Huang, “LLM-assisted light: Leveraging large language model capabilities for human-mimetic traffic signal control in complex urban environments,” arXiv preprint arXiv: 2403.08337, 2024.
    [123]
    S. Lai, Z. Xu, W. Zhang, H. Liu, and H. Xiong, “Large language models as traffic signal control agents: Capacity and opportunity,” arXiv preprint arXiv: 2312.16044, 2023.
    [124]
    S. Zhang, D. Fu, W. Liang, Z. Zhang, B. Yu, P. Cai, and B. Yao, “TrafficGPT: Viewing, processing and interacting with traffic foundation models,” Transp. Policy, vol. 150, pp. 95–105, May 2024. doi: 10.1016/j.tranpol.2024.03.006
    [125]
    P. Wang, X. Wei, F. Hu, and W. Han, “TransGPT: Multi-modal generative pre-trained transformer for transportation,” arXiv preprint arXiv: 2402.07233, 2024.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (219) PDF downloads(72) Cited by()

    Highlights

    • Presents a survey on decision recommendation systems in traffic management
    • Illustrates key components in traffic control decision recommendation systems
    • Highlights the use of human- and data-driven methods for traffic optimization
    • Discusses challenges and future directions in intelligent urban traffic control

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return