An efficient intelligent traffic light control and deviation system for traffic congestion avoidance using multi-agent system

    Rajendran Sathiyaraj Affiliation
    ; Ayyasamy Bharathi Affiliation


An efficient and intelligent road traffic management system is the corner stone for every smart cities. Vehicular Ad-hoc NETworks (VANETs) applies the principles of mobile ad hoc networks in a wireless network for Vehicle-to-vehicle data exchange communication. VANETs supports in providing an efficient Intelligent Transportation System (ITS) for smart cities. Road traffic congestion is a most common problem faced by many of the metropolitan cities all over the world. Traffic on the road networks are widely increasing at a larger rate and the current traffic management systems is unable to tackle this impediment. In this paper, we propose an Efficient Intelligent Traffic Light Control and Deviation (EITLCD) system, which is based on multi-agent system. This proposed system overcomes the difficulties of the existing traffic management systems and avoids the traffic congestion problem compare to the prior scenario. The proposed system is composed of two systems: Traffic Light Controller (TLC) system and Traffic Light Deviation (TLD) system. The TLC system uses three agents to supervise and control the traffic parameters. TLD system deviate the vehicles before entering into congested road. Traffic and travel related information from several sensors are collected through a VANET environment to be processed by the proposed technique. The proposed structure comprises of TLC system and makes use of vehicle measurement, which is feed as input to the TLD system in a wireless network. For route pattern identification, any traditional city map can be converted to planar graph using Euler’s path approach. The proposed system is validated using Nagel–Schreckenberg model and the performance of the proposed system is proved to be better than the existing systems in terms of its time, cost, expense, maintenance and performance.

First published online 26 September 2019

Keyword : traffic control, traffic deviation, multi-agent, sensors, vehicle categorization, traffic light controller, intelligent transportation system

How to Cite
Sathiyaraj, R., & Bharathi, A. (2020). An efficient intelligent traffic light control and deviation system for traffic congestion avoidance using multi-agent system. Transport, 35(3), 327-335.
Published in Issue
Jul 9, 2020
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Aazam, M.; Khan, I.; Alsaffar, A. A.; Huh, E.-N. 2014. Cloud of things: integrating internet of things and cloud computing and the issues involved, in Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST) 14–18th January 2014, Islamabad, Pakistan, 414–419.

Ali, S. S. M.; George, B.; Vanajakshi, L. 2013. An efficient multiple-loop sensor configuration applicable for undisciplined traffic, IEEE Transactions on Intelligent Transportation Systems 14(3): 1151–1161.

Ali, S. S. M.; George, B.; Vanajakshi, L.; Venkatraman, J. 2012. A multiple inductive loop vehicle detection system for heterogeneous and lane-less traffic, IEEE Transactions on Instrumentation and Measurement 61(5): 1353–1360.

Botta, A.; De Donato, W.; Persico, V.; Pescapé, A. 2016. Integration of cloud computing and internet of things: a survey, Future Generation Computer Systems 56: 684–700.

Cai, Y.; Zhang, W.; Wang, H. 2010. Measurement of vehicle queue length based on video processing in intelligent traffic signal control system, in 2010 International Conference on Measuring Technology and Mechatronics Automation, 13–14 March 2010, Changsha, China, 615–618.

Chen, C.; Petty, K.; Skabardonis, A.; Varaiya, P.; Jia, Z. 2001. Freeway performance measurement system: mining loop detector data, Transportation Research Record: Journal of the Transportation Research Board 1748: 96–102.

Cheung, S. Y.; Coleri, S.; Dundar, B.; Ganesh, S.; Tan, C.-W.; Varaiya, P. 2005. Traffic measurement and vehicle classification with single magnetic sensor, Transportation Research Record: Journal of the Transportation Research Board 1917: 173–181.

Chiu, S.; Chand, S. 1993. December. Self-organizing traffic control via fuzzy logic, in Proceedings of 32nd IEEE Conference on Decision and Control, 15–17 December 1993, San Antonio, TX, US, 2: 1897–1902.

Coifman, B. 2001. Improved velocity estimation using single loop detectors, Transportation Research Part A: Policy and Practice 35(10): 863–880.

Coifman, B. 2002. Estimating travel times and vehicle trajectories on freeways using dual loop detectors, Transportation Research Part A: Policy and Practice 36(4): 351–364.

Coifman, B.; Beymer, D.; McLauchlan, P.; Malik, J. 1998. A real-time computer vision system for vehicle tracking and traffic surveillance, Transportation Research Part C: Emerging Technologies 6(4): 271–288.

De Lima, G. R. T.; Silva, J. D. S.; Saotome, O. 2010. Vehicle inductive signatures recognition using a Madaline neural network, Neural Computing and Applications 19(3): 421–436.

Guerrero-Ibáñez, A.; Contreras-Castillo, J.; Buenrostro, R.; Barba Marti, A.; Reyes Muñoz, A. 2010. A policy-based multi-agent management approach for intelligent traffic-light control, in 2010 IEEE Intelligent Vehicles Symposium, 21–24 June 2010, San Diego, CA, US, 694–699.

Guerrero-Ibáñez, J.; Zeadally, S.; Contreras-Castillo, J. 2018. Sensor technologies for intelligent transportation systems, Sensors 18(4): 1212.

Hamidi, H.; Kamankesh, A. 2018. An approach to intelligent traffic management system using a multi-agent system, International Journal of Intelligent Transportation Systems Research 16(2): 112–124.

Hartenstein, H.; Laberteaux, K. (Eds.). 2010. VANET: Vehicular Applications and Inter-Networking Technologies. John Wiley & Sons. 466 p.

He, W.; Yan, G.; Xu, L. D. 2014. Developing vehicular data cloud services in the IoT environment, IEEE Transactions on Industrial Informatics 10(2): 1587–1595.

Houbraken, M.; Logghe, S.; Schreuder, M.; Audenaert, P.; Colle, D.; Pickavet, M. 2017. Automated incident detection using real-time floating car data, Journal of Advanced Transportation 2017: 8241545.

Huang, D.-Y.; Chen, C.-H.; Hu, W.-C.; Yi, S.-C.; Lin, Y.-F. 2012. Feature-based vehicle flow analysis and measurement for a real-time traffic surveillance system, Journal of Information Hiding and Multimedia Signal Processing 3(3): 282–296.

Hunter, T.; Herring, R.; Abbeel, P.; Bayen, A. 2009. Path and ravel time inference from GPS probe vehicle data, in Analyzing Networks and Learning with Graphs: a Workshop in Conjunction with 23nd Annual Conference on Neural Information Processing Systems (NIPS 2009), 11 December 2009, Whistler, BC, Canada, 1–8.

Iscaro, G.; Nakamiti, G. 2013. A supervisor agent for urban traffic monitoring, in 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 25–28 February 2013, San Diego, CA, US, 167–170.

Jia, Z.; Chen, C.; Coifman, B.; Varaiya, P. 2001. The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors, in 2001 IEEE Intelligent Transportation Systems: Proceeding, 25–29 August 2001, Oakland, CA, US, 536–541.

Kaewkamnerd, S.; Chinrungrueng, J.; Pongthornseri, R.; Dumnin, S. 2010. Vehicle classification based on magnetic sensor signal, in 2010 IEEE International Conference on Information and Automation, 20–23 June 2010, Harbin, China, 935–939.

Ki, Y.-K.; Baik, D.-K. 2006. Vehicle-classification algorithm for single-loop detectors using neural networks, IEEE Transactions on Vehicular Technology 55(6): 1704–1711.

Lamas-Seco, J. J.; Castro, P. M.; Dapena, A.; Vazquez-Araujo, F. J. 2015. Vehicle classification using the discrete Fourier transform with traffic inductive sensors, Sensors 15(10): 27201–27214.

Liu, H. X.; He, X.; Recker, W. 2007. Estimation of the time-dependency of values of travel time and its reliability from loop detector data, Transportation Research Part B: Methodological 41(4): 448–461.

Lu, X.-Y.; Varaiya, P.; Horowitz, R.; Guo, Z.; Palen, J. 2012. Estimating traffic speed with single inductive loop event data, Transportation Research Record: Journal of the Transportation Research Board 2308: 157–166.

Meta, S.; Cinsdikici, M. G. 2010. Vehicle-classification algorithm based on component analysis for single-loop inductive detector, IEEE Transactions on Vehicular Technology 59(6): 2795–2805.

Oh, C.; Park, S.; Ritchie, S. G. 2006. A method for identifying rear-end collision risks using inductive loop detectors, Accident Analysis & Prevention 38(2): 295–301.

Pan, X.; Guo, Y.; Men, A. 2010. Traffic surveillance system for vehicle flow detection, in 2010 Second International Conference on Computer Modeling and Simulation, 22–24 January 2010, 314–318.

Salama, A. S.; Saleh, B. K.; Eassa, M. M. 2010. Intelligent cross road traffic management system (ICRTMS), in 2010 2nd International Conference on Computer Technology and Development, 2–4 November 2010, Cairo, Egypt, 27–31.

Samadi, S.; Rad, A. P.; Kazemi, F. M.; Jafarian, H. 2012. Performance evaluation of intelligent adaptive traffic control systems: a case study, Journal of Transportation Technologies 2(3): 248–259.

Taghvaeeyan, S.; Rajamani, R. 2014. Portable roadside sensors for vehicle counting, classification, and speed measurement, IEEE Transactions on Intelligent Transportation Systems 15(1): 73–83.

Tao, F.; Zuo, Y.; Xu, L. D.; Zhang, L. 2014. IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing, IEEE Transactions on Industrial Informatics 10(2): 1547–1557.

Tong, M.; Tang, M. 2010. LEACH-B: an improved leach protocol for wireless sensor network, in 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), 23–25 September 2010, Chengdu, China, 1–4.

Wang, M.; Liang, H.; Zhang, R.; Deng, R.; Shen, X. 2014. Mobility-aware coordinated charging for electric vehicles in VANET-enhanced smart grid, IEEE Journal on Selected Areas in Communications 32(7): 1344–1360.

Wang, M.; Shan, H.; Lu, R.; Zhang, R.; Shen, X.; Bai, F. 2015. Real-time path planning based on hybrid-VANET-enhanced transportation system, IEEE Transactions on Vehicular Technology 64(5): 1664–1678.

Wang, S.; Li, R.; Guo, M. 2018. Application of nonparametric regression in predicting traffic incident duration, Transport 33(1): 22–31.

Yao, B.; Hu, P.; Zhang, M.; Jin, M. 2014. A support vector machine with the tabu search algorithm for freeway incident detection, International Journal of Applied Mathematics and Computer Science 24(2): 397–404.

Zheng, B.; Sayin, M. O.; Lin, C.; Shiraishi, S.; Zhu, Q. 2017. Timing and security analysis of VANET-based intelligent transportation systems, in 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 13–16 November 2017, Irvine, CA, US, 984–991.

Zhu, L.; Jin, S. 2011. Speed estimation with single loop detector using typical effective vehicle length, in 2011 International Conference on Multimedia Technology, 26–28 July 2011, Hangzhou, China, 4096–4099.