ADAPTIVE TRAFFIC SYSTEM CONTROLLERS IN TRAFFIC ENGINEERING : A SURVEY
DOI:
https://doi.org/10.55766/sujst-2023-03-e03030Keywords:
Fuzzy based traffic controller, Hydrocarbon (HC), Intelligent Transportation System (ITS), Intelligent Traffic Management System (ITMS)Abstract
In today’s era, traffic congestion is the widest spread problem observed all over the world, arising as consequence of exponential rise in vehicle count at the traffic intersections. This growth has largely affected the people as they are experiencing enhanced delay in travelling time and increased fuel consumption which led to wastage of billions of dollars. The current road infrastructure design and traffic signal controlling using a cycle of fixed time phase of green/red/yellow lights are not adequate to tackle the rising demands of traffic in an optimum way. These traditional traffic signal systems cannot handle the dynamics of road traffic at the intersections and hence results in exceeding delays. Also, the volume of traffic at any intersection at different times of the day is uncertain and hence it is hard to get an exact mathematical model for this problem. Many researchers have proposed some solution to this problem and their work is reviewed extensively in this paper. Due to its ability to deal with uncertainty, fuzzy logic is considered as the most appropriate technique to solve this problem and is highly recommended method for implementing automated traffic controllers. Due to its inherent advantages, most of the research in the field of traffic engineering is carried out using fuzzy logic techniques. Hence, this paper presents a systematic review of various techniques that are used for an effective management of traffic, especially focusing on different fuzzy based traffic controllers and their performance comparison to identify the best input output parameter.
References
Adewoye, O., Ajibade, O., and Olayemi, A. (2015). Modelling of a fuzzy traffic light controller. International Journal of Research in Mechanical and Materials Engineering, 1(1):6-14.
Alam, J. and Pandey, M.K. (2015). Design and analysis of a two stage traffic light system using fuzzy logic. Journal of Information Technology and Software Engineering, 5(03). https://doi.org/10.4172/2165-7866.1000162
Aleko, D.R. and Djahel, S. (2020). An efficient adaptive traffic light control system for urban road traffic congestion reduction in smart cities. Information, 11(2):119. https://doi.org/10.3390/info11020119
Askerzade, I.N. and Mahmud, M. (2011). Design and implementation of group traffic control system using fuzzy logic. International Journal of Research and Reviews in Applied Sciences6(2):196-202.
Atta, A., Abbas, S., Khan, M.A., Ahmed, G., and Farooq, U. (2020). An adaptive approach: Smart traffic congestion control system. Journal of King Saud University-Computer and Information Sciences. 32(9):1,012-1,019. https://doi.org/10.1016/j.jksuci.2018.10.011
Balaji, P.G. and Srinivasan D. (2011). Type-2 fuzzy logic based urban traffic management. Engineering Applications of Artificial Intelligence. 24(1):12-22. https://doi.org/
1016/j.engappai.2010.08.007
Bhatia, M.S. and Aggarwal, A. (2020). Congestion control by reducing wait time at the traffic junction using fuzzy logic controller. International Journal of Sensors Wireless Communications and Control. 10(6):989-1,000. https://doi.org/10.2174/2210327910666200226113614
Bi, Y., Lu, X., Sun, Z., Srinivasan, D., and Sun, Z. (2017). Optimal type-2 fuzzy system for arterial traffic signal control. IEEE Transactions on Intelligent Transportation Systems. 19(9):3,009-3,027. https://doi.org/10.1109/
TITS.2017.2762085
Bisset, K.R. and Kelsey, R.L. (1992). Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods. Los Alamos National Lab.(LANL), Los Alamos, NM (United States).
Calle-Laguna, A.J., Du, J., and Rakha, H.A. (2019). Computing optimum traffic signal cycle length considering vehicle delay and fuel consumption. Transportation Research Interdisciplinary Perspectives. 3:100021. https://doi.org/
1016/j.trip.2019.100021
Cao, J. and Wang, Y. (2016). Fuzzy control of intersection signal based on optimized genetic algorithm. In: 2016 International Conference on Civil, Transportation and Environment, Jan, 2016; Atlantis Press, p. 538-546. https://doi.org/10.2991/iccte-16.2016.89
Castá, J.A., Martí, S.I., Menchaca, J.L., Terá, J.D., Treviñ, M.G., Pé, J., and Agundis, D.U. (2018). Fuzzy rules to improve traffic light decisions in urban roads. Journal of Intelligent Learning Systems and Applications, 10(2):36-45. https://doi.org/10.4236/jilsa.2018.102003
CEIC Data. (2021). India Motor Vehicles Sales Growth. https://www.ceicdata.com/en/indicator/india/motor-vehicles-sales-growth. Accessed 02 January 2022.
Celtek, S.A., Durdu, A., and Alı, M.E.M. (2020). Real-time traffic signal control with swarm optimization methods. Measurement, 166:108206. https://doi.org/10.1016/
j.measurement.2020.108206
Cheng, J., Wu, W., Cao, J., and Li, K. (2017). Fuzzy group-based intersection control via vehicular networks for smart transportations. IEEE Transactions on Industrial Informatics, 13(2):751-758. https://doi.org/10.1109/
TII.2016.2590302
Cheng, S.T., Li, J.P., Horng, G.J., and Wang, K.C. (2014). The adaptive road routing recommendation for traffic congestion avoidance in smart city. Wireless personal communications. 77(1):225-46. https://doi.org/10.1007/
s11277-013-1502-4
Chiu, S. and Chand, S. (1993). Self-organizing traffic control via fuzzy logic. In: Proceedings of 32nd IEEE Conference on Decision and Control; Dec 15, 1993, IEEE, p. 1,897-1,902.
Das, A., Dash, P., Mishra, B.K. (2018). An Innovation Model for Smart Traffic Management System Using Internet of
Things (IoT). In: Sangaiah, A., Thangavelu, A., Meenakshi Sundaram, V. (eds) Cognitive Computing for Big Data Systems Over IoT. Lecture Notes on Data Engineering and Communications Technologies, Springer, Cham. 14:355-370 https://doi.org/10.1007/978-3-319-70688-7_15
Dereli, T., Cetinkaya, C., and Celik, N. (2018). Designing a fuzzy logic controller for a single intersection: a case study in Gaziantep. Sigma Journal of Engineering and Natural Sciences, 36(3):767-781.
Dipak, K.D. (2020). Bengaluru has world’s worst traffic congestion, Mumbai at number 4. Accessed from: https://timesofindia.indiatimes.com/india/bengaluru-has-worlds-worst-traffic-congestion-mumbai-at-number-4/
articleshow/73747725.cms. Accessed date: 02 January 2022.
Eze, U.F., Emmanuel, I., and Stephen, E. (2014). Fuzzy logic model for traffic congestion. IOSR Journal of Mobile Computing & Application. 1(1):15-20. https://doi.org/
9790/0050-0111520
Falcocchio, J.C. and Levinson, H.S. (2015). Road Traffic Congestion: A Concise Guide. Springer Tracts on Transportation and Traffic (STTT, volume 7) Springer, 425p. https://doi.org/10.1007/978-3-319-15165-6.
Favilla, J., Machion, A., and Gomide, F. (1993). Fuzzy traffic control: adaptive strategies. In: Proceedings 2nd IEEE International Conference on Fuzzy Systems; Mar 28, 1993, IEEE, p. 506-511.
Federal Highway Administration, (FHWA). (2022). How Do Weather Events Impact Roads? FHWA Road Weather Management. Available from: https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm
Federal Highway Administration. (2019). Operations-Reducing Recurring Congestion. Accessed from: https://ops.fhwa.
dot.gov/program_ areas/reduce-recur-cong.htm Accessed date: 10 December 2019.
Financial Express Online. (2018). Traffic jam in 4 metros costs more than entire Rail budget; this city has worst peak-hour congestion. Accessed from: https://www.financialexpress.
com/economy/traffic-jam-in-4-metros-costs-more-than-entire-rail-budget-this-city-has-worst-peak-hour-congestion/1146356. Accessed date: 02 January 2022.
Fleming, S. (2019). Traffic congestion cost the US economy nearly $87 billion in 2018. World Economic Forum 2021. Accessed from: https://www. weforum.org/agenda/2019/
/traffic-congestion-cost-the-us-economy-nearly-87-billion-in-2018. Accessed date: 12 December 2019.
Fonseca, D.J., Moynihan, G.P., and Fernandes, H. (2011). The role of non-recurring congestion in massive hurricane evacuation events. Recent Hurricane Research-Climate, Dynamics, and Societal Impacts, p. 441-458.
Ge, Y. (2014). A two-stage fuzzy logic control method of traffic signal based on traffic urgency degree. Modelling and simulation in engineering. 2014:694185. https://doi.org/
1155/2014/694185
Ghosh, B., Asif, M.T., Dauwels, J., Cai, W., Guo, H., and Fastenrath, U. (2016). Predicting the duration of non-recurring road incidents by cluster-specific models. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC); 2016 Nov 1-4; Rio de Janeiro, Brazil; p. 1522-1527. https://doi.org/10.1109/
ITSC.2016.7795759
Gokulan, B.P. and Srinivasan, D. (2010). Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Transactions on Intelligent Transportation Systems. 11(3):714-727. https://doi.org/10.1109/TITS.2010.2050688
Hartanti, D., Aziza, R.N., and Siswipraptini, P.C. (2019). Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(1):320-327. https://doi.org/10.12928/telkomnika.v17i1.
Homaei, H., Hejazi, S.R., and Dehghan, S.A. (2015). A new traffic light controller using fuzzy logic for a full single junction involving emergency vehicle preemption. Journal of Uncertain Systems, 9(1):49-61.
Jain, A., Yadav, S., Vij, S., Kumar, Y., and Tayal, D.K. (2020). A novel self-organizing approach to automatic traffic
light management system for road traffic network. Wireless Personal Communications, 110(3):1,303-1,321. https://doi.org/10.1007/s11277-019-06787-z
Jovanović, A.D. and Kukić, K.S. (2017). Controlling an isolated oversaturated intersection in real time. Vojnotehnički Glasnik, 65(4):866-881. https://doi.org/10.5937/
vojtehg65-14170
Karakuzu, C. and Demirci, O. (2010). Fuzzy logic based smart traffic light simulator design and hardware implementation. Applied Soft Computing. 10(1):66-73. https://doi.org/10.1016/j.asoc.2009.06.002
Kelsey, R., Bisset, K., Jamshidi, M. (1993). A simulation environment for fuzzy control of traffic systems. IFAC Proceedings Volumes. Jul 1, 1993; 26(2):753-756. https://doi.org/10.1016/S1474-6670(17)48831-8
Khooban, M.H., Vafamand, N., Liaghat, A., and Dragicevic, T. (2017). An optimal general type-2 fuzzy controller for Urban Traffic Network. ISA transactions. 66:335-343. https://doi.org/10.1016/j.isatra.2016.10.011
Koukol, M., Zajíčková, L., Marek, L., and Tuček, P. (2015). Fuzzy logic in traffic engineering: a review on signal control. Mathematical Problems in Engineering. 2015:979160. https://doi.org/10.1155/2015/979160
Kumar, S., Baliyan, A., Tiwari, A., Tripathi, A.K., and Jaiswal, B. (2019). Intelligent traffic controller. International Journal of Information Technology, 28:1-3.
Lai, G.R., Soh, A., Sarkan, H.M., Rahman, R.A., and Hassan, M.K. (2015). Controlling traffic flow in multilane-isolated intersection using ANFIS approach techniques. Journal of Engineering Science and Technology, 10(8):1,009-1,034.
Li, J. and Zhang, H. (2008). Study on optimal control and simulation for urban traffic based on fuzzy logic. In: 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA); IEEE, 1:936-940. https://doi.org/10.1109/ICICTA.2008.370
Litman, T. (2023). Congestion costing critique: critical evaluation of the “urban mobility report”. Accessed from: https://www.vtpi.org/UMR_critique.pdf.
Mahapatra, D. (2018). Will take 2 years to clear 77 city bottlenecks: Delhi government to SC. The Time of India. Available from: https://timesofindia.indiatimes.com/city/
delhi/will-take-2-years-to-clear-77-delhi-bottlenecks-delhi-
govt-to-sc/articleshow/65061757.cms
Mehan, S. (2011). Introduction of traffic light controller with fuzzy control system. International Journal of Electronics & Communication Technology. 2(3):119-122.
Ministry of Road Transport and Highways, Government of India. (2021). Road Transport Year Book (2017 - 2018 & 2018 - 2019). Accessed from: https://morth.nic.in/sites/default/
files/RTYB-2017-18-2018-19.pdf. Accessed date: 02 January 2022.
Murat, Y.S. and Gedizlioglu E. (2005). A fuzzy logic multi-phased signal control model for isolated junctions. Transportation Research Part C: Emerging Technologies. 13(1):19-36. https://doi.org/10.1016/j.trc.2004.12.004
Nakatsuyama, M., Nagahashi, H., and Nishizuka, N. (1984). Fuzzy logic phase controller for traffic junctions in the one-way arterial road. IFAC Proceedings Volumes. 17(2):2,865-2,870. https://doi.org/10.1016/S1474-6670
(17)61417-4
Ng, S.C. and Kwok, C.P. (2020). An intelligent traffic light system using object detection and evolutionary algorithm
for alleviating traffic congestion in hong kong. International Journal of Computational Intelligence Systems, 13(1):802-809. https://doi.org/10.2991/ijcis.d.
001
Niittymäki, J. (2002). Fuzzy traffic signal control: principles and applications. Helsinki University of Technology.
Niittymäki, J. and Turunen, E. (2003). Traffic signal control on similarity logic reasoning. Fuzzy Sets and Systems. 133(1):109-131. https://doi.org/10.1016/S0165-0114(02)
-8
Olivera, A.C., García-Nieto, J.M., and Alba, E. (2015). Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization. Applied Intelligence. 42(3):389-405. https://doi.org/10.1007/s10489-014-0604-3
Pappis, C.P. and Mamdani, E.H. (1997). A fuzzy logic controller for a trafc junction. IEEE Transactions on Systems, Man, and Cybernetics. 7(10):707-717. https://doi.org/
1109/TSMC.1977.4309605
Press Releases. (2020). INRIX Global Traffic Scorecard: Congestion cost UK economy £6.9 billion in 2019. Accessed from: https://inrix.com/press-releases/2019-traffic-scorecard-uk.
Schrank, D., Eisele, B., Lomax, T., and Bak, J. (2015). Urban Mobility Scorecard. Published jointly by The Texas A&M Transportation Institute and INRIX. Accessed from: https://static.tti.tamu.edu/tti.tamu.edu/documents/umr/archive/mobility-scorecard-2015-wappx.pdf.
Silva, C.M., Aquino, A.L., and Meira, W. (2015). Smart traffic light for low traffic conditions. Mobile networks and applications. 20(2):285-293. https://doi.org/10.1007/
s11036-015-0571-x
Singh, A., Obaidat, M.S., Singh, S., Aggarwal, A., Kaur, K., Sadoun, B., Kumar, M., and Hsiao, K.F. (2022). A simulation model to reduce the fuel consumption through efficient road traffic modelling. Simulation Modelling Practice and Theory, 121:102658. https://doi.org/
1016/j.simpat.2022.102658
Singh, A., Singh, S., and Aggarwal, A. (2021). Traffic congestion controller: a fuzzy based approach. In: 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), Bengaluru, India, 2021, p. 355-358, https://doi.org/
1109/CENTCON52345.2021.9687917
Singh, A., Singh, S., and Aggarwal, A. (2023). An efficient road traffic modelling through a novel real time traffic simulator. Current Applied Science and Technology (Accepted for Publication).
Sundar, R., Hebbar, S., and Golla, V. (2015). Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sensors Journal. 15(2):1,109-1,113. https://doi.org/
1109/JSEN.2014.2360288
Systematics, C. (2005). Traffic congestion and reliability: Trends and advanced strategies for congestion mitigation. United States. Federal Highway Administration.
TomTom (2021). Tomtom Traffic Index Ranking 2021. Accessed from: https://www.tomtom.com/en_gb/traffic-index/ranking.
Trabia, M.B., Kaseko, M.S., and Ande, M. (1999). A two-stage fuzzy logic controller for traffic signals. Transportation Research Part C: Emerging Technologies. 7(6):353-367. https://doi.org/10.1016/S0968-090X(99)00026-1
Tunc, I. and Soylemez, M.T. (2023). Fuzzy logic and deep Q learning based control for traffic lights. Alexandria Engineering Journal, 67:343-359. https://doi.org/10.1016/
j.aej.2022.12.028
Yao, T., Zhang, C., Zhao, J., Gupta, A., and Mondal, S. (2023). Adaptive signal control for overflow prevention at isolated intersections based on fuzzy control. Transportation Research Record, 2677(5):1,387-1,401. https://doi.org/
1177/03611981221143380
Worldometer, (2020). World Population by Year. Accessed from: https://www.worldometers.info/world-population/
world-population-by-year. Accessed date: 02 January 2022.
Younis, O. and Moayerim N. (2017). Employing cyber-physical systems: Dynamic traffic light control at road intersections. IEEE Internet of Things Journal. 4(6):2,286-2,296. https://doi.org/10.1109/JIOT.2017.2765243
Yusupbekov, N.R., Marakhimov, A.R., Igamberdiev, H.Z., and Umarov, S.X. (2016). An adaptive fuzzy-logic traffic control system in conditions of saturated transport
stream. The Scientific World Journal. 2016:6719459. https://doi.org/10.1155/2016/6719459
Zachariah, B., Ayuba, P., and Damuut, L.P. (2017). Optimization of traffic light control system of an intersection using fuzzy inference system. Science World Journal, 12(4):27-33.
Zaid, A.A., Suhweil, Y., and Al Yaman, M. (2017). Smart controlling for traffic light time. In: 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT); Oct 11, 2017, IEEE, p. 1-5. https://doi.org/10.1109/AEECT.2017.8257768
Zarandi, M.H. and Rezapour, S. (2009). A fuzzy signal controller for isolated intersections. Journal of Uncertain Systems, 3(3):174-182.
Zeng, R., Li, G., and Lin, L. (2007). Adaptive traffic signals control by using fuzzy logic. In: 2nd International Conference on Innovative Computing, Informatio and Control (ICICIC 2007) Kumamoto, Japan, 2007, IEEE,
p. 527-527. https://doi.org/10.1109/ICICIC.2007.118
Zhang, W.B., Wu, B.Z., and Liu, W.J. (2007). Anti-congestion fuzzy algorithm for traffic control of a class of traffic networks. In: 2007 IEEE International Conference on Granular Computing (GRC 2007); 2007 Nov 2, IEEE,
p. 124-124. https://doi.org/10.1109/GrC.2007.138
Zhao, D., Dai, Y., and Zhang, Z. (2012). Computational intelligence in urban traffic signal control: A survey.
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4):485-894. https://doi.org/10.1109/TSMCC.2011.2161577
Zuraime, F.S., Rahman, S.F., Yaakob, A.M., and Rahman, N.R. (2019). Traffic waiting time management using
fuzzy logic approach. In: AIP Conference Proceedings, Aug 21, 2019; AIP Publishing LLC., 2138(1):030042. https://doi.org/10.1063/1.5121079
