Software-defined Architecture for Urban Regional Traffic Signal Control

  • Songhang Chen Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences
  • Dan Zhang
  • Fenghua Zhu
Keywords: regional traffic signal control, heterogeneous environment, software-defined RTSC systems, virtualization


Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.


Wang Y, Yang X, Liang H, Liu Y. A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment. Journal of Advanced Transportation. 2018;(1494): 1-12.

Kotwal AR, Lee SJ, and Kim YJ. Traffic Signal Systems: A Review of Current Technology in the United States. Science and Technology. 2013;3(1): 33-41.

Fernandez R, Valenzuela E, Casanello F, et al. Evolution of the TRANSYT Model in a Developing Country. Transportation Research Part A: Policy and Practice. 2006;40(5): 386-398.

Selby DL, Powell R, Scoot, et al. Urban Traffic Control System Incorporating SCOOT: Design and Implementation. ICE Proceedings. 2015;82(5): 903-920.

Hu P, Tian Z, Dayem A, Yang F. Field Evaluation of SCATS Control System in Las Vegas. 11th International Conference of Chinese Transportation Professionals (ICCTP 2011): Towards Sustainable Transportation Systems, 14-17 August 2011, Nanjing, China; 2011.

Gordon RL. Traffic Signal Retiming Practices in the United States. Transp. Research Board; 2010.

Intelligent Energy Europe, Project Report. Intelligent Road and Street Lighting in Europe. The European Association for Creativity & Innovation; 2008.

Zhu F, Chen S, Mao Z, Miao Q. Parallel Public Transportation System and Its Application in Evaluating Evacuation Plans for Large-scale Activities. IEEE Transactions on Intelligent Transportation Systems. 2014;15(4): 1728-1733.

NTCIP. Available from:

Chu T, Qu S, Wang J. Large-scale Traffic Grid Signal Control with Regional Reinforcement Learning. IEEE American Control Conference; 2016.

Shahidehpour M, Li Z, Bahramirad S, Khodaei A. Optimizing Traffic Signal Settings in Smart Cities. IEEE Transactions on Smart Grid. 2017;8(5): 2382-2393.

Li P, Mirchandani P, Zhou X. Solving Simultaneous Route Guidance and Traffic Signal Optimization Problem using Space-phase-time Hypernetwork. Transportation Research Part B: Methodological. 2015;81: 103-130.

Zhang Y, Su R, Sun C, Zhang Y. Modelling and traffic signal control of a heterogeneous traffic network with signalized and non-signalized intersections. IEEE Conference on Control Technology and Applications (CCTA); 2017. p. 1581-1586.

Patel RK, Rowe E. An Overview of ITS Standards and Protocols. Available from:

Yue K, Wang X-L, Zhou A-Y. Underlying Techniques for Web Services: A Survey. Journal of Software. 2004;15(3): 428-442.

Porta S, Crucitti P, Latora V. The Network Analysis of Urban Streets: A Primal Approach. Environment and Planning B: Urban Analytics and City Science. 2006;33(5): 705-725.

Asthana R, Ahuja NJ, Darbari M, Shukla PK. A Critical Review on the Development of Urban Traffic Models & Control Systems. International Journal of Scientific & Engineering Research. 2012;3(1): 1-6.

Chen S, Chen H, Wang DY, Wang S, Zhang J. A kind of regional traffic optimization control method integrating multiple controllable traffic signals. China, 201810790876.9 [P]. 2018.07.18.

Balaji PG, Srinivasan D. Multi-Agent System in Urban Traffic Signal Control. IEEE Computational Intelligence Magazine. 2010;5(4): 43-51.

Li L, Lv Y, Wang F-Y. Traffic Signal Timing via Deep Reinforcement Learning. IEEE/CAA Journal of Automatica Sinica. 2016;3(3): 247-254.

Rahman SM, Ratrout NT. Review of the Fuzzy Logic Based Approach in Traffic Signal Control: Prospects in Saudi Arabia. Journal of Transportation Systems Engineering and Information Technology. 2009;9(5): 58-70.

Dong C, Liu Z, and Liu X. Attribute Reduction to Traffic Flow in Area Traffic Control Based on Rough Sets. Journal of System Simulation. 2006;18(6): 1524-1528.

Srinivasan D, Choy MC, Cheu RL. Neural Networks for Real-Time Traffic Signal Control. IEEE Transactions on Intelligent Transportation Systems. 2006;7(3): 261-272.

Ceylan H, Bell MGH. Traffic Signal Timing Optimization Based on Genetic Algorithm Approach, Including Drivers’ Routing. Transportation Research Part B: Methodological. 2004;38(4): 329-342.

Zheng F, van Zuylen HJ, Liu X, Vine SL. Reliability-Based Traffic Signal Control for Urban Arterial Roads. IEEE Transactions on Intelligent Transportation Systems. 2017;18(3): 643-655.

Garcia-Nieto J, Olivera AC, Alba E. Optimal Cycle Program of Traffic Lights with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation. 2013;17(6): 823-839.

Alvarez I, Poznyak A, Malo A. Urban Traffic Control Problem: A Game Theory Approach. IEEE Conference on Decision and Control; 2008. p. 2168-2172.

Wen W. A Dynamic and Automatic Traffic Light Control Expert System for Solving the Road Congestion Problem. Expert Systems with Applications. 2008;34(4): 2370-2381.

Wang A, Wu X, Ma B, Zhou Z. Rules Self-Adaptive Control System for Urban Traffic Signal Based on Genetic Study Classification Algorithm. International Conference on Artificial Intelligence and Computational Intelligence; 2009. p. 429-433.

Li T, Zhao D, Yi J. Adaptive Dynamic Programming for Multi-Intersections Traffic Signal Intelligent Control. IEEE Conference on Intelligent Transportation Systems; 2008. p. 286-291.

Chen J. A Robust Multi-Objective Compatible Optimization Control Algorithm for Traffic Signal Control. International Conference on Intelligent Transportation Systems (ITSC); 2014. p. 1850-1856.

Zhou Z, Schutter BD, Lin S, Xi Y. Two-Level Hierarchical Model-Based Predictive Control for Large-Scale Urban Traffic Networks. IEEE Transactions on Control Systems Technology. 2017;25(2): 496-508.

Abdulhai B, Pringle R, Karakoulas GJ. Reinforcement Learning for True Adaptive Traffic Signal Control. Journal of Transportation Engineering. 2003;129(3): 278-285.

Qi W. The Regional Traffic Signal Control That based on Pinning Control. Master Thesis. North China University of Technology; 2016.

Wang F-Y. Parallel System Methods for Management and Control of Complex Systems. Control and Decision. 2004;9(5): 485-489.

Wang F-Y. Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications. IEEE Transactions on Intelligent Transportation Systems. 2010;11(3): 630-638.

How to Cite
Chen S, Zhang D, Zhu F. Software-defined Architecture for Urban Regional Traffic Signal Control. PROMET [Internet]. 2020Mar.26 [cited 2020Apr.4];32(2):229-36. Available from: