The Key Technologies of New Generation Urban Traffic Control System Review and Prospect: Case by China
Source abstract
Abstract
Due to the limitations of its technology and theory, the traditional traffic control system has been unable to adapt to the needs of new technology and traffic development and needs to be reformed and reconstructed. From the national scientific and technological research and development plan to the traffic control system development projects of relevant enterprises, the common problem is that the advanced signal control system plays an insufficient role in practical application. The existing signal control system excessively relies on the use of IT technology but ignores the basic theory of traffic control and the essential consideration of the traffic environment and optimal regulation of road traffic flow, which greatly limits the scientific and practical value of a traffic control system in China. This narrative review analyzes recent developments and emerging trends in urban traffic control technologies through literature synthesis spanning 2009–2025. With the rapid and large-scale development and application of new transportation technologies such as vehicle–infrastructure networking, vehicle–infrastructure collaboration, and automatic driving, the real-time interaction between the traffic controller and the controlled party has new support. Given these technological advances, there is an urgent need to address the limitations of existing traffic signal control systems. Transportation technology development must leverage rich traffic control interaction conditions and comprehensive data to create next-generation systems. These new traffic optimization control systems should demonstrate high refinement, precision, better responsiveness, and enhanced intelligence. This paper can play a key role and influence for China to lead the development of urban road traffic control systems in the future. The promotion and application of the new generation of urban road traffic signal optimization control systems will improve the efficiency of the road network to a greater extent, reduce operating costs, prevent and alleviate road traffic congestion, and reduce energy consumption and emissions. At the same time, it will also provide the entry point and technical support for the development of vehicle–infrastructure networking and coordination and the automatic driving industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
Intelligent Transportation System Research Center, Tongji University, 4801 Cao’an Road, Shanghai 201800, China
Funding
National Natural Science Foundation of China (General Program), Research on Basic Problem of Vehicle-Infrastructure Cooperative Traffic Control for Special Vehicles · China Postdoctoral Science Foundation, Cooperative Optimization on Right-of-Way at Signalized Intersections in Heterogeneous traffic Environment · Guangxi Major Science and Technology Special Subproject, Reutilization of Pinglu Canal Cross-Line Bridges and Optimization of Traffic Organization
References (82)
Show source-provided references
1. Big-data empowered traffic signal control could reduce urban carbon emission10.1038/s41467-025-56701-4 ↗
2. Environmentally friendly traffic control strategy—A case study in Xi’an city10.1016/j.jclepro.2019.119397 ↗
3. An empirical analysis on the arterial fundamental diagram10.1016/j.trb.2010.06.003 ↗
4. Real-time queue length estimation for congested signalized intersections10.1016/j.trc.2009.02.003 ↗
5. Rosca, C.M., Stancu, A., and Gortoescu, I.A. (2025). Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation. Appl. Sci., 15.10.3390/app15084301 ↗
6. Almusawi, A., Albdairi, M., and Qadri, S.S.S.M. (2024). Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control. Appl. Sci., 14.10.3390/app14198851 ↗
7. Cooperative traffic signal control using a distributed agent-based deep reinforcement learning with incentive communication10.1109/TITS.2024.3352730 ↗
8. Cooperative traffic signal control through a counterfactual multi-agent deep actor critic approach10.1016/j.trc.2024.104528 ↗
9. (1991). The third-generation real-time adaptive urban traffic control system has been deployed and operationalized in Nanjing. J. Tongji Univ. Nat. Sci., 2, 256.
10. (2025, March 13). “Three Transformations and Three Constants” of Traffic Signal Systems over the Past Two Decades. Available online: https://www.7its.com/?m=home&c=View&a=index&aid=26849.
11. Multi-objective optimization of traffic signals based on vehicle trajectory data at isolated intersections10.1016/j.trc.2020.102821 ↗
12. Traffic light optimization with low penetration rate vehicle trajectory data10.1038/s41467-024-45427-4 ↗
13. Spatiotemporal intersection control in a connected and automated vehicle environment10.1016/j.trc.2018.02.001 ↗
14. A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections10.1016/j.trc.2021.103459 ↗
15. Shared-phase-dedicated-lane based intersection control with heterogeneous traffic of human-driven vehicles and connected and automated vehicles10.1016/j.trc.2021.103509 ↗
16. Optimization of isolated intersection signal timing and trajectory planning under heterogeneous traffic environment: The flexible catalysis of connected and automated vehicles10.1016/j.physa.2024.129668 ↗
17. A spatiotemporal control method at isolated intersections under mixed-autonomy traffic conditions10.1111/mice.13127 ↗
18. Moon, S., Koo, S., Lim, Y., and Joo, H. (2024). Routing control optimization for autonomous vehicles in heterogeneous traffic flow based on deep reinforcement learning. Appl. Sci., 14.10.3390/app14052214 ↗
19. A hierarchical vehicle behavior prediction framework with traffic signals and interactive agents10.1109/TITS.2023.3275072 ↗
20. Optimal allocation of turns to lanes at an isolated signal-controlled junction10.1016/j.trb.2010.12.001 ↗
21. Capacity optimization of an isolated intersection under the phase swap sorting strategy10.1016/j.trb.2013.12.001 ↗
22. Performance analysis of the phase swap sorting strategy for an isolated intersection10.1016/j.trc.2017.01.018 ↗
23. Traffic signal timing via deep reinforcement learning10.1109/JAS.2016.7508798 ↗
24. Road Traffic Control Systems: Advances and Prospects
25. Hunt, P.B., Robertson, D.I., Bretherton, R.D., and Winton, R.I. (1981). SCOOT: A Traffic Responsive Method of Coordinating Signals, Transport and Road Research Laboratory.
26. Sims, A.G. (1979, January 11–16). The Sydney coordinated adaptive traffic system. Proceedings of the Engineering Foundation Conference on Research Directions in Computer Control of Urban Traffic Systems, Pacific Grove, CA, USA.
27. Application Study on SCATS and Intelligent Traffic Lights in Shanghai
28. Transport Cybernetics Preliminary: Common Rule and Methodology of Transport System Control
29. Wei, H., Zheng, G., Yao, H., and Li, Z. (2018, January 19–23). Intellilight: A reinforcement learning approach for intelligent traffic light control. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.10.1145/3219819.3220096 ↗
30. An intelligent control system for traffic lights with simulation-based evaluation10.1016/j.conengprac.2016.09.009 ↗
31. Urban Adaptive Traffic Signal Control Methods: A Survey of the State of the Art
32. Research review on cooperative decision-making for vehicle swarms in vehicle-infrastructure cooperative environment
33. Review of Traffic Data Collection Research on Urban Traffic Control
34. Parsimonious shooting heuristic for trajectory control of connected automated traffic part I: Theoretical analysis with generalized time geography10.1016/j.trb.2016.05.007 ↗
35. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections10.1109/TITS.2018.2849029 ↗
36. Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections10.1016/j.trb.2018.04.007 ↗
37. A real-time adaptive signal control in a connected vehicle environment10.1016/j.trc.2015.01.007 ↗
38. Cooperative driving: An ant colony system for autonomous intersection management10.1007/s10489-011-0322-z ↗
39. Signal Priority Control Method of Urban Public Transportation Based on Scats System
40. Modeling and implementation of adaptive transit signal priority on actuated control systems10.1111/j.1467-8667.2010.00677.x ↗
41. A multi-agent reinforcement learning model for maintenance optimization of interdependent highway pavement networks10.1111/mice.13234 ↗
42. Dynamic urban traffic rerouting with fog-cloud reinforcement learning10.1111/mice.13115 ↗
43. Advancing the white phase mobile traffic control paradigm to consider pedestrians10.1111/mice.13178 ↗
44. Safety-critical traffic control by connected automated vehicles10.1016/j.trc.2023.104230 ↗
45. Adaptive traffic signal control using approximate dynamic programming10.1016/j.trc.2009.04.005 ↗
46. Hu, H.C., Smith, S.F., and Goldstein, R. (2019, January 3–8). Cooperative schedule-driven intersection control with connected and autonomous vehicles. Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.10.1109/IROS40897.2019.8967975 ↗
47. Intersection control with connected and automated vehicles: A review10.1108/JICV-06-2022-0023 ↗
48. Combined flexible lane assignment and reservation-based intersection control in field-like traffic conditions10.1080/23249935.2022.2119113 ↗
49. Xiao, X., Zhang, Y., Wang, X.B., Yang, S., and Chen, T. (2021). Hierarchical longitudinal control for connected and automated vehicles in heterogeneous traffic on a signalized arterial. Sustainability, 13.10.3390/su13168852 ↗
50. Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs10.1109/MCS.2024.3498552 ↗
51. A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial10.1016/j.physa.2024.130099 ↗
52. Connected automated vehicle trajectory optimization along signalized arterial: A decentralized approach under heterogeneous traffic environment10.1016/j.trc.2022.103918 ↗
53. Optimal vehicle speed trajectory on a signalized arterial with consideration of queue10.1016/j.trc.2015.11.001 ↗
54. Multi-objective traffic signal control using network-wide agent coordinated reinforcement learning10.1016/j.eswa.2023.120535 ↗
55. A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework10.1016/j.trc.2014.12.009 ↗
56. Kolat, M., Kővári, B., Bécsi, T., and Aradi, S. (2023). Multi-agent reinforcement learning for traffic signal control: A cooperative approach. Sustainability, 15.10.3390/su15043479 ↗
57. A multi-agent deep reinforcement learning approach for traffic signal coordination10.1049/itr2.12521 ↗
58. Multi-agent deep reinforcement learning collaborative traffic signal control method considering intersection heterogeneity10.1016/j.trc.2024.104663 ↗
59. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning10.1016/j.trc.2021.103059 ↗
60. Traffic signal control with reinforcement learning based on region-aware cooperative strategy10.1109/TITS.2021.3062072 ↗
61. Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review10.48130/DTS-2023-0008 ↗
62. Optimizing Heterogeneous traffic Flow: Longitudinal Control of Connected and Automated Vehicles to Mitigate Traffic Oscillations10.1109/TITS.2024.3522002 ↗
63. Robust fuzzy model predictive control for connected and automated vehicles in mixed platoons using a bidirectional vehicle dynamics strategy10.1016/j.eswa.2024.126057 ↗
64. Stabilizing traffic flow by autonomous vehicles: Stability analysis and implementation considerations10.1016/j.trc.2023.104449 ↗
65. Integrated optimization of traffic signals and vehicle trajectories for heterogeneous traffic at signalized intersections: A two-level hierarchical control framework10.1016/j.trc.2024.104884 ↗
66. Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment10.1111/mice.13371 ↗
67. Mixed platoon control of automated and human-driven vehicles at a signalized intersection: Dynamical analysis and optimal control10.1016/j.trc.2021.103138 ↗
68. Adaptive green split optimization for traffic control with low penetration rate trajectory data10.1080/15472450.2023.2227959 ↗
69. Mu, Z., Chen, Z., Sakr, A.H., Ryu, S., Wang, T., Avedisov, S.S., Guo, R., and Park, B.B. (2021, January 19–22). Assessment of cooperative adaptive cruise control in mixed traffic on arterial roads. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.10.1109/ITSC48978.2021.9565099 ↗
70. Mu, Z., Chen, Z., Ryu, S., Avedisov, S.S., Guo, R., and Park, B.B. (2022, January 5–9). Cooperative platooning with mixed traffic on urban arterial roads. Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany.10.1109/IV51971.2022.9827105 ↗
71. The impact and potential of cooperative and automated driving for intelligent traffic signal corridors: A field-operational-test and simulation experiment10.1016/j.cstp.2020.05.011 ↗
72. Trajectory-based traffic management inside an autonomous vehicle zone10.1016/j.trb.2018.12.012 ↗
73. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles10.1016/j.trc.2019.04.012 ↗
74. Optimal coordination of automated vehicles at intersections: Theory and experiments10.1109/TCST.2018.2871397 ↗
75. Real-time coordination of connected vehicles at intersections using graphical mixed integer optimization10.1049/itr2.12061 ↗
76. Intersection Control in Heterogeneous traffic with Connected Automated Vehicles: A Review of Recent Developments and Research Frontiers
77. Majstorović, Ž., Tišljarić, L., Ivanjko, E., and Carić, T. (2023). Urban Traffic Signal Control under Heterogeneous traffic Flows: Literature Review. Appl. Sci., 13.10.3390/app13074484 ↗
78. Wang, H., Zhu, J., and Gu, B. (2023). Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control. Appl. Sci., 13.10.3390/app13064010 ↗
79. Li, H., Li, S., and Zhang, X. (2023). Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles. Appl. Sci., 13.10.3390/app131910803 ↗
80. Wang, P., Ye, R., Zhang, J., and Wang, T. (2022). An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation. Appl. Sci., 12.10.3390/app12094533 ↗
81. Mixed traffic flow of human driven vehicles and automated vehicles on dynamic transportation networks10.1016/j.trc.2021.103159 ↗
82. Theory-data dual driven car following model in traffic flow mixed of AVs and HDVs10.1016/j.trc.2024.104747 ↗