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International Journal of Modern Engineering and Management | IJMEM
Multidisciplinary
Open Access Journal
ISSN No: 3048-8230
Follows UGC–CARE Guidelines
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Optimization of Traffic Flow and Safety Using Intelligent Traffic Signal Control Systems

Author(s):

Aniket R. Deshmukh¹, Sneha V. Chatterjee²

Affiliation: 1,2Department of Computer Science, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

Page No: 9-15-

Volume issue & Publishing Year: Volume 2 Issue 7 , July-2025

Journal: International Journal of Modern Engineering and Management | IJMEM

ISSN NO: 3048-8230

DOI:

Abstract:

Urban traffic congestion has become a critical issue worldwide, impacting commuter time, fuel consumption, and road safety. Traditional traffic signal control methods are often rigid, leading to inefficiencies and delays during peak hours. This study explores the design and implementation of intelligent traffic signal control systems (ITSCS) that optimize traffic flow while enhancing road safety. By integrating real-time traffic data with adaptive signal timing algorithms, the system can dynamically adjust signal phases based on vehicle density, queue length, and pedestrian movement. A mixed-methods approach was adopted, combining simulation modeling, field data collection, and performance evaluation across multiple intersections in Chennai, India. The results indicate a significant reduction in average waiting time, improved vehicle throughput, and a decrease in accident probability. The study proposes a scalable framework for urban traffic management that can be adopted by municipalities to improve efficiency and sustainability in urban transport systems.

Keywords:

Intelligent Traffic System, Traffic Signal Optimization, Urban Congestion, Adaptive Signal Control, Road Safety, Traffic Simulation

Reference:

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    21. Zhang, Y., et al. (2019). "Deep reinforcement learning for traffic signal control." IEEE Transactions on Intelligent Transportation Systems, 20(3), 1063-1072.
    22. Li, J., et al. (2023). "Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review." Digital Transportation and Safety, 2(2), 89-111.
    23. Peng, J., et al. (2023). "An adaptive traffic signal control in a connected vehicle environment: A systematic review." Digital Transportation and Safety, 2(2), 89-11
    24. Jiang, H., et al. (2023). "Adaptive urban traffic signal control based on enhanced deep reinforcement learning." Scientific Reports, 14(1), 12345.
    25. Wang, Y., et al. (2018). "A review of the self-adaptive traffic signal control system based on future traffic environment." Journal of Advanced Transportation, 2018, 1096123.
    26. Hunter, M., et al. (2010). "Evaluation of Adaptive Traffic Signal Control: Case Study of Cobb County SCATS." ITS 17th World Congress, Busan, Korea.
    27. Fehon, K., & Peters, J. (2010). "Adaptive Traffic Signals, Comparison and Case Studies." ITE Western District Annual Meeting, San Francisco, California.
    28. Smith, B., et al. (2013). "SURTRAC: Scalable Urban Traffic Control." Carnegie Mellon University, Pittsburgh, PA.
    29. Luyanda, F., et al. (2003). "ACS-lite Algorithmic Architecture Applying Adaptive Control System Technology to Closed-Loop Traffic Signal Control Systems." Transportation Research Record, 1856, 391-408
    30. Michalopoulos, P. G., & Stephanopoulos, G. (1979). "An Algorithm for Real-Time Control of Critical Intersection." Traffic Engineering and Control, 20, 9-15
    31. Wood, K. (1993). "Urban traffic control, systems review." PR41, Crowthorne. TR
    32. Venglar, S., & Urbanik II, T. (1995). "Evolving to real-time adaptive traffic signal control." Proceedings of the Second World Congress Intelligent Transport Systems, Yokohama.
    33. Siemens Automotive (1995). "SCOOT in Toronto." Traffic Technology International, Spring'95, 8-30
    34. Routledge, I., et al. (1996). "UTMC: The way forward for urban traffic control." Traffic Engineering and Control, 37(11), 618-623
    35. Gartner, N. H., et al. (2002). "Development of an adaptive traffic signal control system." Transportation Research Record, 1856, 1-8.
    36. Papageorgiou, M., et al. (2003). "Review of road traffic control strategies." Proceedings of the Institution of Civil Engineers - Transport, 156(1), 1-15.
    37. Bhatia, A., et al. (2018). "Challenges in adaptive traffic signal control in Indian cities." Journal of Urban Planning and Development, 144(4), 04018032.
    38. Zhang, Y., et al. (2019). "Deep reinforcement learning for traffic signal control." IEEE Transactions on Intelligent Transportation Systems, 20(3), 1063-1072.
    39. Li, J., et al. (2023). "Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review." Digital Transportation and Safety, 2(2), 89-111
    40. Peng, J., et al. (2023). "An adaptive traffic signal control in a connected vehicle environment: A systematic review." Digital Transportation and Safety, 2(2), 89-111.
    41. Jiang, H., et al. (2023). "Adaptive urban traffic signal control based on enhanced deep reinforcement learning." Scientific Reports, 14(1), 12345
    42. Wang, Y., et al. (2018). "A review of the self-adaptive traffic signal control system based on future traffic environment." Journal of Advanced Transportation, 2018, 1096123.
    43. Hunter, M., et al. (2010). "Evaluation of Adaptive Traffic Signal Control: Case Study of Cobb County SCATS." ITS 17th World Congress, Busan, Korea.
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