A Review of Expert Systems Integration in Signal Plan Optimisation

Luka DEDIĆ; Miroslav VUJIĆ

doi:10.7307/ptt.v36i5.601

Authors

Luka DEDIĆ PROMETIS ltd.
Miroslav VUJIĆ University of Zagreb, Faculty of Transport and Traffic Sciences

DOI:

https://doi.org/10.7307/ptt.v36i5.601

Keywords:

urban traffic management, automatic licence plate recognition, computational intelligence, prediction of vehicle trajectories, microsimulation tools

Abstract

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

References

Shen Y, Kwan MP, Chai Y. Investigating commuting flexibility with GPS data and 3D geovisualization: A case study of Beijing, China. Journal of Transport Geography. 2013;32:1–11. DOI: 10.1016/j.jtrangeo.2013.07.007.

Huang Y, et al. Exploring individual travel patterns across private car trajectory data. IEEE Transactions on Intelligent Transportation Systems. 2019;21(12):5036–5050. DOI: 10.1109/TITS.2019.2948188.

Yu H, Yang S, Wu Z, Ma X. Vehicle trajectory reconstruction from automatic license plate reader data. International Journal of Distributed Sensor Networks. 2018;14(2). DOI: 10.1177/1550147718755637.

Lana I, Del Ser J, Velez M, Vlahogianni EI. Road traffic forecasting: Recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine. 2018;10(2):93–109. DOI: 10.1109/MITS.2018.2806634.

Highway Capacity Manual 2010. Washington DC. Transportation Research Board. 2010.

Yeh C, Ritchie SG, Schneider JB. Potential applications of knowledge-based expert systems in transportation planning and engineering. Transportation Research Record. 1986;1076:58–65.

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. DOI: 10.1016/j.eswa.2007.03.007.

Kang-Won L, Gyeong-Chul K. Knowledge-based expert system in traffic signal control systems. Journal of Environmental Studies. 1991;28–29.

Nor Azlan NN, Md Rohani M. Overview of application of traffic simulation model. MATEC Web of Conferences. 2018;150:03006. DOI: 10.1051/matecconf/201815003006.

Alegria F, Girao PS. Vehicle plate recognition for wireless traffic control and law enforcement system. 2006 IEEE International Conference on Industrial Technology. 2006;1800–1804. DOI: 10.1109/ICIT.2006.372504.

Sarfraz M, Ahmed MJ, Ghazi SA. Saudi Arabian license plate recognition system. 2003 International Conference on Geometric Modeling and Graphics – Proceedings. London UK. 2003. p. 36–41. DOI: 10.1109/GMAG.2003.1219663.

Du S, Ibrahim M, Shehata M, Badawy W. Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits and Systems for Video Technology. 2013;23 (2):311–325. DOI: 10.1109/tcsvt.2012.2203741.

Agarwal P, Chopra K, Kashif M, Kumari V. Implementing ALPR for detection of traffic violations: A step towards sustainability. Procedia Computer Science. 2018;132:738–743. DOI: 10.1016/j.procs.2018.05.085.

Thanin K, Mashohor S, Al-Faqheri W. An improved Malaysian automatic license plate recognition (M-ALPR) system using hybrid fuzzy in C++ environment. 2009 Innovative Technologies in Intelligent Systems and Industrial Applications. 2009;51–55. DOI: 10.1109/citisia.2009.5224241.

Ashtari AH, Nordin MJ, Mousavi Kahaki SM. A new reliable approach for Persian license plate detection on colour images. Proceedings of the 2011 International Conference on Electrical Engineering and Informatics. 2011;1–5. DOI: 10.1109/iceei.2011.6021697.

Prabhu BS, Kalambur S, Sitaram D. Recognition of Indian license plate number from live stream videos. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 13-16 Sept. 2017. Karnataka, India. 2017. p. 2359–2365. DOI: 10.1109/icacci.2017.8126199.

Praphananurak K, Sanghitkul W, Chovichien V, Watanachaturaporn P. A framework for origin-destination estimation using license plate recognition for Thai rural traffic. 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE). 12-13 Oct. 2017. Phuket, Thailand. 2017. p. 1–5. DOI: 10.1109/iciteed.2017.8250439.

Waterman DA. A guide to expert systems. Boston, USA: Addison–Wesley Longman Publishing Co; 1986.

Hayes–Roth F, Waterman DA, Lenat DB. Building expert systems. Boston, USA: Addison–Wesley Longman Publishing Co; 1998.

Abraham A, Grosan C. Rule–based expert systems. In Intelligent Systems Reference Library. Berlin, Germany: Springer; 2011.

McCarthy J, Minsky M. Quarterly Progress Report 56. Research Laboratory of Electronics, MIT, Cambridge, Mass. 1956.

Colmerauer A. Metamorphosis grammars. Lecture Notes in Computer Science. 1978;133–188. DOI: 10.1007/BFb0031371.

Kowalski RA. Predicate logic as programming language. Information Processing 74, 1973;569–574.

Kowalski, RA. Algorithm = logic + control. Communications of the ACM, 1979;22(7),424–436.

Glasgow J, Browse R. Programming languages for artificial intelligence. Computers & Mathematics with Applications, 1985;11(5):431–448. DOI: 10.1016/0898-1221(85)90049-5.

Zaied AN, Al Othman W. Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait. Expert Systems with Applications, 2011;38(8):9434–9441. DOI: 10.1016/j.eswa.2011.01.130.

Conglin L, Wu W, Yuejin T. Traffic variable estimation and traffic signal control based on soft computation. Proceedings The 7th International IEEE Conference on Intelligent Transportation Systems Washington, USA 2004. (IEEE Cat. No.04TH8749). DOI: 10.1109/ITSC.2004.1399051-

Yusupbekov NR, Marakhimov AR, Igamberdiev HZ, Umarov SX. An adaptive fuzzy-logic traffic control system in conditions of saturated transport stream. The Scientific World Journal, 2016, 1–9. DOI: 10.1155/2016/6719459.

Gong K, et al. An expert system to discover key congestion points for urban traffic. Expert Systems with Applications, 2020;158:113544. DOI: 10.1016/j.eswa.2020.113544.

TfL 2010: Traffic Modelling Guidelines, version 3. London: Transport for London, UK. 2010.

Trafficware: Synchro Studio User’s Manual. Sugar Land, Trafficware Ltd. 2016.

U.S. Department of Transportation, Federal Highway Administration. Manual on uniform traffic control devices for streets and highways. USA: U.S. Department of Transportation, Federal Highway Administration. 2009.

Xiao H. Methodology for selecting microscopic simulators: Comparative evaluation of AIMSUN and VISSIM. Minneapolis, USA. University of Minnesota, Intelligent Transportation Systems Institute. 2005.

Government of South Australia, Department for Planning Transport and Infrastructure. Traffic modelling guidelines – TRANSYT 15. Adelaide, Australia. Government of South Australia, Department for Planning Transport and Infrastructure. 2014.

Ceylan H. Developing combined genetic algorithm: Hill-climbing optimization method for area traffic control. Journal of Transportation Engineering, 2006;132(8),663–671. DOI: 10.1061/(asce)0733-947x(2006)132:8(663).

Lopez PA, et al. Microscopic traffic simulation using SUMO. 21st International Conference on Intelligent Transportation Systems (ITSC) 4-7 Nov 2018. Maui, Hawaii, USA. 2018. p. 2575–2582. DOI: 10.1109/itsc.2018.8569938.

Gao Y. Calibration and comparison of the VISSIM and INTEGRATION microscopic traffic simulation models. PhD thesis. Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA; 2008.

Bloomberg L, Dale J. Comparison of VISSIM and CORSIM traffic simulation models on a congested network. Transportation Research Record: Journal of the Transportation Research Board, 2000;1727(1):52–60. DOI: 10.3141/1727-07.

Gökçe MA, Öner E, Işık G. Traffic signal optimization with Particle Swarm optimization for signalized roundabouts. SIMULATION, 2015;91(5):456–466. DOI: 10.1177/0037549715581473

Ren Y, et al. An adaptive signal control scheme to prevent intersection traffic blockage. IEEE Transactions on Intelligent Transportation Systems, 2017;18(6):1–10. DOI: 10.1109/tits.2016.2609917.

Labib SM, et al. Integrating data mining and microsimulation modelling to reduce traffic congestion: A case study of signalized intersections in Dhaka, Bangladesh. Urban Science, 2019;3(2):41. DOI: 10.3390/urbansci3020041.

Handbook for the Dimensioning of Road Traffic Systems. Cologne: FGSV der Verlag. 2015.

Zhao D, Dai Y, Zhang Z. Computational intelligence in urban traffic signal control: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012;42(4):485–494. DOI: 10.1109/tsmcc.2011.2161577.

Zadeh LA. Fuzzy sets. In Advances in Fuzzy Systems: Applications and Theory. 1996. p. 394–432.

Trabia MB, Kaseko MS, Ande MA. A two-stage fuzzy logic controller for traffic signals. Transportation Research Part C: Emerging Technologies, 1999;7(6):353–367. DOI: 10.1016/s0968-090x(99)00026-1.

Lee JH, Lee-Kwang H. Distributed and cooperative fuzzy controllers for traffic intersections group. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 1999;29(2):263–271. DOI: 10.1109/5326.760570.

Murat YS, Gedizlioglu E. A fuzzy logic multi-phased signal control model for isolated junctions. Transportation Research Part C: Emerging Technologies, 2005;13(1):19–36. DOI: 10.1016/j.trc.2004.12.004.

Qiao J, Yang N, Gao J. Two-Stage fuzzy logic controller for signalized intersection. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2011;41(1):178–184. DOI: 10.1109/tsmca.2010.2052606.

Van Broekhoven E, De Baets B. Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets and Systems, 2006;157(7):904–918. DOI: 10.1016/j.fss.2005.11.005.

Gokulan BP, Srinivasan D. Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 2010;11(3):714–727. DOI: 10.1109/tits.2010.2050688.

Keong CK. The GLIDE system: Singapore's urban traffic control system. Transport Reviews, 1993;13(4):295–305. DOI: 10.1080/01441649308716854.

Choy MC, Srinivasan D, Cheu RL. Neural networks for continuous online learning and control. IEEE Transactions on Neural Networks, 2006;17(6):1511–1531. DOI: 10.1109/tnn.2006.881710.

Chowdhury FN, Wahi P, Raina R, Kaminedi S. A survey of neural networks applications in automatic control. Proceedings of the 33rd Southeastern Symposium on System Theory. 2001; 349–353. DOI: 10.1109/ssst.2001.918544.

Spall C, Chin DC. Traffic-responsive signal timing for system-wide traffic control. Transportation Research Part C: Emerging Technologies, 1997;5(3-4):153–163. DOI: 10.1016/s0968-090x(97)00012-0.

Shen G, Kong X. Study on road network traffic coordination control technique with bus priority. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2009;39(3):343–351. DOI: 10.1109/tsmcc.2008.2005842.

Gregurić M, Ivanjko E, Mandžuka S. The use of cooperative approach in ramp metering. Promet - Traffic&Transportation, 2016;28(1):11–22. DOI: 10.7307/ptt.v28i1.1889.

Bingham E. Reinforcement learning in neurofuzzy traffic signal control. European Journal of Operational Research, 2001;131(2):232–241. DOI: 10.1016/s0377-2217(00)00123-5.

Engelbrecht AP. Computational intelligence: An introduction, 2nd Edition, Wiley, 2020.

Ceylan H, Bell MGH. Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transportation Research Part B: Methodological, 2004;38(4):329–342. DOI: 10.1016/s0191-2615(03)00015-8.

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. DOI: 10.1109/tits.2006.874716.

Wei Y, Shao Q, Han Y, Fan B. Intersection signal control approach based on PSO and simulation. 2nd International Conference on Genetic and Evolutionary Computing 2008. 25-26 Sept 2008. 2008. p. 277–280. DOI: 10.1109/wgec.2008.124.

Vujić M, Gregurić M, Dedić L, Koltovska Nečoska D. The impact of unconditional priority for escorted vehicles in traffic networks on sustainable urban mobility. MDPI Sustainability. 2023;16(1):151. DOI: 10.3390/su16010151.

Sutton RS, Barto A. Reinforcement learning: An introduction. The MIT Press, London, UK. 2018.

Chin Y, Bolong N, Yang SS, Teo KT. Q-learning based traffic optimization in management of signal timing plan. International Journal of Simulation: Systems, Science & Technology. 2011;12(3):29–35. DOI: 10.5013/IJSSST.a.12.03.05.

Kusic K, et al. Extended variable speed limit control using multi-agent reinforcement learning. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 20-23. Sept 2020. Rhodes, Greece. 2020. p. 1–8. DOI: 10.1109/itsc45102.2020.929463.

Wiering M, Vreeken J, van Veenen J, Koopman A. Simulation and optimization of traffic in a city. IEEE Intelligent Vehicles Symposium, Parma, Italy 14-17 June 2004. 2004. p. 453–458. DOI: 10.1109/ivs.2004.1336426.

Cao L, et al. Two intersections traffic signal control method based on ADHDP. IEEE International Conference on Vehicular Electronics and Safety (ICVES). 2016:1–5. DOI: 10.1109/ICVES.2016.7548166.

Cai C, Wong CK, Heydecker BG. Adaptive traffic signal control using approximate dynamic programming. Transportation Research Part C: Emerging Technologies, 2009;17 (5):456–474. DOI: 10.1016/j.trc.2009.04.005.

Li T, Zhao D, Yi J. Adaptive dynamic neuro-fuzzy system for traffic signal control. 2008 IEEE International Joint Conference on Neural Networks IEEE World Congress on Computational Intelligence, 01-06. June 2008. Hong Kong, China. 2008. p. 1840–1846. DOI: 10.1109/ijcnn.2008.4634048.

Chen B, Cheng HH. A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2010;11(2):485–497. DOI: 10.1109/tits.2010.2048313.

Wiering M. Multi-agent reinforcement learning for traffic light control. Proceedings of the 17th International Conference of Machin Learning, Stanford University, Stanford, CA, USA, June 29 - July 2, 2000. 2000. p. 1151–1158. 2000.

Ferreira ED, Khosla PK. Multi-agent collaboration using distributed value functions. Proceedings of the IEEE Intelligent Vehicles Symposium 2000, Dearborn, Michigan, USA. (Cat. No.00TH8511). 2000. p. 404–409. DOI: 10.1109/ivs.2000.898377.

Choy MC, Srinivasan D, Cheu RL. Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2003;33(5):597–607. DOI: 10.1109/tsmca.2003.817394.

Roozemond DA. Using intelligent agents for pro-active, real-time urban intersection control. European Journal of Operational Research, 2001;131(2):293–301. DOI: 10.1016/s0377-2217(00)00129-6.

Bazzan ALC, de Oliveira D, da Silva BC. Learning in groups of traffic signals. Engineering Applications of Artificial Intelligence, 2009;23(4):560–568. DOI: 10.1016/j.engappai.2009.11.009.

Gregurić M, Vujić M, Alexopoulos C, Miletić M. Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data. Applied Sciences, 2020;10(11):4011. DOI: 10.3390/app10114011.

Wang FY. Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010;11(3):630–638. DOI:10.1109/tits.2010.2060218.