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Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network

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Abstract

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches.

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Data Availability

This work was supported by the Department of Science and Technology (DST), ICPS Division, New Delhi, Government of India, who imposed data sharing restrictions on the data underlying our study. So, data cannot be shared publicly because of the confidentiality of the project. Data are only available for researchers who meet the criteria for access to confidential data.

References

  1. Wen F, Zhang G, Sun L, Wang X, Xu X (2019) A hybrid temporal association rules mining method for traffic congestion prediction. Comput Industr Eng 130:779–787

    Article  Google Scholar 

  2. Singh A, Singh S, Aggarwal A (2021) Traffic congestion controller: a fuzzy based approach. In: 2021 International conference on disruptive technologies for multi-disciplinary research and applications (CENTCON), vol 1. IEEE, pp 355–358

  3. Liang X, Yan T, Lee J, Wang G (2018) A distributed intersection management protocol for safety, efficiency, and driver’s comfort. IEEE Internet Things J 5(3):1924–1935

    Article  Google Scholar 

  4. Roberg P (1995) A distributed strategy for eliminating incident-based traffic jams from urban networks. Traffic Eng Control 36(6):348

    Google Scholar 

  5. Pozanco A, Fernández S, Borrajo D (2021) On-line modelling and planning for urban traffic control. Expert Syst 38(5):e12693

    Article  Google Scholar 

  6. Ng KKH, Lee CKM, Zhang SZ, Wu K, Ho W (2017) A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion. Comput Industr Eng 109:151–168

    Article  Google Scholar 

  7. Sharma D K, Mahto R V, Harper C, Alqattan S (2020) Role of rfid technologies in transportation projects: a review. Int J Technol Intell Plan 12(4):349–377

    Google Scholar 

  8. Bai S, Bai X (2021) A general framework for intersection traffic control with backpressure routing. IEEE Access 9:102125–102136

    Article  Google Scholar 

  9. Lim K G, Lee C H, Chin R K Y, Beng Yeo K, Teo K T K (2017) Sumo enhancement for vehicular ad hoc network (vanet) simulation. In: 2017 IEEE 2nd international conference on automatic control and intelligent systems (I2CACIS), pp 86–91

  10. Kang S, Chae Y, Yeon S (2017) Vanet routing algorithm performance comparison using ns-3 and sumo. In: 2017 4th International conference on computer applications and information processing technology (CAIPT), pp 1–5

  11. Wen M, Kumar B V D (2021) An analysis on scheduling of traffic light at urban traffic intersection using fuzzy control algorithm. Int J Transp Eng Technol, 85–91

  12. Jafari S, Shahbazi Z, Byun Y-C (2021) Traffic control prediction design based on fuzzy logic and Lyapunov approaches to improve the performance of road intersection. Processes 9(12):2205

    Article  Google Scholar 

  13. Mir A, Hassan A (2018) Fuzzy inference rule based neural traffic light controller. In: 2018 IEEE International conference on mechatronics and automation (ICMA). IEEE, pp 816–820

  14. Younes M B, Boukerche A (2018) An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems. Wirel Netw 24(7):2451–2463

    Article  Google Scholar 

  15. Polson N G, Sokolov V O (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C: Emerg Technol 79:1–17

    Article  Google Scholar 

  16. Liang X, Du X, Wang G, Han Z (2019) A deep reinforcement learning network for traffic light cycle control. IEEE Trans Veh Technol 68(2):1243–1253

    Article  Google Scholar 

  17. Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of SUMO - Simulation of Urban MObility. Int J Adv Syst Measur 5(3&4):128–138

    Google Scholar 

  18. Wright C, Roberg P (1998) The conceptual structure of traffic jams. Transp Policy 5(1):23–35

    Article  Google Scholar 

  19. Lal G, Divya LG, Nithin KJ, Mathew S, Kuriakose B (2016) Sustainable traffic improvement for urban road intersections of developing countries: a case study of Ettumanoor, India. Procedia Technol 25:115–121

    Article  Google Scholar 

  20. Lee M, Atkison T (2021) Vanet applications: past, present, and future. Veh Commun 28:100310

    Google Scholar 

  21. Ahmad I, Noor R M, Ali I, Imran M, Vasilakos A (2017) Characterizing the role of vehicular cloud computing in road traffic management. Int J Distrib Sensor Netw 13(5):1550147717708728

    Article  Google Scholar 

  22. Saharan S, Bawa S, Kumar N (2020) Dynamic pricing techniques for intelligent transportation system in smart cities: a systematic review. Comput Commun 150:603–625

    Article  Google Scholar 

  23. Cao Z, Jiang S, Zhang J, Guo H (2016) A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE Trans Intell Transp Syst 18(7):1958–1973

    Article  Google Scholar 

  24. Hu H, Li X, Zhang Y, Shang C, Zhang S (2019) Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads. Comput Industr Eng 128:861–876

    Article  Google Scholar 

  25. Precup R-E, Doboli S, Preitl S (2000) Stability analysis and development of a class of fuzzy control systems. Eng Appl Artif Intell 13(3):237–247

    Article  Google Scholar 

  26. Zhang Y, Zhang G, Fierro R, Yang Y (2018) Force-driven traffic simulation for a future connected autonomous vehicle-enabled smart transportation system. IEEE Trans Intell Transp Syst 19(7):2221–2233

    Article  Google Scholar 

  27. Bekiaris-Liberis N, Roncoli C, Papageorgiou M (2016) Highway traffic state estimation with mixed connected and conventional vehicles. IEEE Trans Intell Transp Syst 17(12):3484–3497

    Article  Google Scholar 

  28. van der Pol E (2016) Deep reinforcement learning for coordination in traffic light control. Master’s thesis, University of Amsterdam

  29. Sutton RS, Barto AG (1998) Reinforcement learningan introduction. MIT Press, Cambridge

    Google Scholar 

  30. Silver D, Huang A, Maddison C J, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484

    Article  Google Scholar 

  31. Stojčić M (2018) Application of anfis model in road traffic and transportation: a literature review from 1993 to 2018. Oper Res Eng Sci: Theory Applic 1(1):40–61

    Google Scholar 

  32. Adewale A L, Jumoke A F, Adegboye M, Ismail A (2018) An embedded fuzzy logic based application for density traffic control system. Int J Artif Intell Res 2(1):7–16

    Article  Google Scholar 

  33. Kumar N, Rahman S S, Dhakad N (2020) Fuzzy inference enabled deep reinforcement learning-based traffic light control for intelligent transportation system. IEEE Trans Intell Transp Syst

  34. Karaboga D, Kaya E (2019) Adaptive network based fuzzy inference system (anfis) training approaches: a comprehensive survey. Artif Intell Rev 52(4):2263–2293

    Article  Google Scholar 

  35. Qu L, Li W, Li W, Ma D, Wang Y (2019) Daily long-term traffic flow forecasting based on a deep neural network. Exp Syst Applic 121:304–312

    Article  Google Scholar 

  36. Lee S, Kim Y, Kahng H, Lee S-K, Chung S, Cheong T, Shin K, Park J, Kim S B (2020) Intelligent traffic control for autonomous vehicle systems based on machine learning. Expert Syst Appl 144:113074

    Article  Google Scholar 

  37. Gong K, Zhang L, Ni D, Li H, Xu M, Wang Y, Dong Y (2020) An expert system to discover key congestion points for urban traffic. Expert Syst Appl, 113544

  38. D’Andrea E, Marcelloni F (2017) Detection of traffic congestion and incidents from gps trace analysis. Expert Syst Appl 73:43–56

    Article  Google Scholar 

  39. Alkandari A A, Al-Shaikhli I F (2018) Implementation of dynamic fuzzy logic control of traffic light with accident detection and action system using itraffic simulation. Indonesian J Electr Eng Comput Sci 10 (1):100–109

    Article  Google Scholar 

  40. Shang M, Zhou Y, Fujita H (2021) Deep reinforcement learning with reference system to handle constraints for energy-efficient train control. Inf Sci 570:708–721

    Article  MathSciNet  Google Scholar 

  41. Chang CS, Sim SS (1997) Optimising train movements through coast control using genetic algorithms. IEE Proc-Electric Power Applic 144(1):65–73

    Article  Google Scholar 

  42. Borges D F, Leite Jo ao PRR, Moreira E M, Carpinteiro OAS (2021) Traffic light control using hierarchical reinforcement learning and options framework. IEEE Access 9:99155–99165

    Article  Google Scholar 

  43. Roman R-C, Precup R-E, Petriu E M (2021) Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems. Eur J Control 58:373–387

    Article  MathSciNet  MATH  Google Scholar 

  44. Mousavi S S, Schukat M, Howley E (2017) Traffic light control using deep policy-gradient and value-function-based reinforcement learning. IET Intell Transp Syst 11(7):417–423

    Article  Google Scholar 

  45. Bhatia M S, Aggarwal A (2020) Congestion control by reducing wait time at the traffic junction using fuzzy logic controller. Int J Sensors Wireless Commun Control 10(6):989–1000

    Article  Google Scholar 

  46. Ngo T-T, Huynh-The T, Kim D-S (2019) A novel vanets-based traffic light scheduling scheme for greener planet and safer road intersections. IEEE Access 7:22175–22185

    Article  Google Scholar 

  47. Jang J-S (1993) Anfis adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  48. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC-15(1):116–132

    Article  MATH  Google Scholar 

  49. Al-Hmouz A, Shen J, Al-Hmouz R, Yan J (2011) Modeling and simulation of an adaptive neuro-fuzzy inference system (anfis) for mobile learning. IEEE Trans Learn Technol 5(3):226– 237

    Article  Google Scholar 

  50. Loganathan C, Girija KV (2013) Hybrid learning for adaptive neuro fuzzy inference system. Int J Eng Sci 2(11):6–13

    Google Scholar 

  51. Azim M A, Huda M N (2010) Fuzzy traffic control system. Term Paper Based on Case Study and Implementation of a Fuzzy Application, Queen’s University

  52. Azimirad E, Pariz N, Sistani M B N (2010) A novel fuzzy model and control of single intersection at urban traffic network. IEEE Syst J 4(1):107–111

    Article  Google Scholar 

  53. Kafash M, Menhaj M B, Sharif M J M, Maleki A (2013) Designing fuzzy controller for traffic lights to reduce the length of queues in according to minimize extension of green light time and reduce waiting time. In: 2013 13th Iranian conference on fuzzy systems (IFSC). IEEE, pp 1–6

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Acknowledgements

This work was supported by the Department of Science and Technology (DST), ICPS Division, New Delhi, Government of India through the Project IoT based automated, Real-Time and Effective Traffic Signal Scheduling for Smart City under Grant T-678.

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Authors and Affiliations

  1. Indian Institute of Information Technology, Pune, Near Bopdev Ghat, Kondhwa Annexe, Yewalewadi, 411048, Maharashtra, India

    Dheeraj Jutury

  2. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Haridwar Highway, Roorkee, 247667, Uttarakhand, India

    Neetesh Kumar & Anuj Sachan

  3. Operations Management Group, Indian Institute of Management Lucknow, IIM Rd, Prabandh Nagar, Mubarakpur, Lucknow, 226013, Uttar Pradesh, India

    Yash Daultani

  4. Department, Indian Institute of Information Technology, Pune, Street, City, 411048, Maharashtra, India

    Naveen Dhakad

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Contributions

The first author contributed towards the machine learning component during the implementation and draft writing, and the second author designed the algorithm and formulated the problem itself. The third author prepared the simulation setup on Indian roads using SUMO and Python scripts. The fourth author contributed to problem conceptualization, draft writing, grammar checking, and the overall organization of the draft. The fifth author helped at the early stages of the simulation part.

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Correspondence to Anuj Sachan.

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Jutury, D., Kumar, N., Sachan, A. et al. Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network. Appl Intell 53, 7132–7153 (2023). https://doi.org/10.1007/s10489-022-03827-3

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