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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64334

Smart Traffic Management System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64334

Volume:

10 |

5 |

May 2026

Pages:

1-2

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RBS

Rohit Balu Sonone

Computer Engineering ( Data Science)

Zeal College of Engineering and Research Narhe ,Pune 411041

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Mentors

PBC

Prof. Balaji Chaugule

Computer Engineering ( Data Science)

Zeal College of Engineering and Research Narhe ,Pune 411041

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Abstract

The increasing urbanization and rapid growth in vehicular traffic have led to significant challenges in managing traffic congestion, reducing travel delays, and minimizing environmental impacts. A Smart Traffic Management System (STMS) leverages advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Big Data analytics to optimize traffic flow, enhance road safety, and improve overall transportation efficiency. This paper presents an innovative STMS framework that integrates real-time data collection from sensors, cameras, and connected vehicles, along with predictive analytics and adaptive traffic control algorithms. The system dynamically adjusts traffic signal timings, provides intelligent route suggestions, and prioritizes emergency vehicles, thereby reducing congestion and response times. Simulation results demonstrate a substantial improvement in traffic conditions, reduced carbon emissions, and enhanced commuter satisfaction. The proposed STMS offers a scalable and cost-effective solution for modern urban centers, paving the way for smarter, greener, and more sustainable cities.

KEYWORDS

Internet of Things (IoT) Artificial Intelligence (AI) and Big Data

Cite This Article

Rohit Balu Sonone, Prof. Balaji Chaugule, "Smart Traffic Management System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-2, May 2026.

Full Article/Conclusion

The Smart Traffic Management System (STMS) effectively addresses the challenges of urban traffic congestion by leveraging IoT, AI, and real-time data analytics. Simulation results demonstrated significant improvements in traffic flow, reduced waiting times, enhanced fuel efficiency, and lower greenhouse gas emissions. The system's ability to prioritize emergency vehicles further underscores its potential to improve urban mobility and safety. While challenges such as sensor reliability and infrastructure costs remain, the STMS offers a scalable and sustainable solution for modern cities. Future enhancements, including integration with smart city frameworks and advancements in sensor technology, will further optimize its performance, paving the way for smarter and more efficient urban transportation systems.

References

1.                Alam, M., Ferreira, J., & Fonseca, J. (2016). Intelligent traffic management system for urban areas using video analytics. IEEE Transactions on Intelligent Transportation Systems, 17(3), 849858. 2.                Chen, C., Jiang, H., & Liu, M. (2018). A real-time IoT-enabled traffic management system for smart cities. Journal of Transportation Technologies, 8(2), 3447. 3.                Kesting, A., Treiber, M., & Helbing, D. (2010). Enhanced intelligent driver model to access the impact of adaptive cruise control systems. Transportation Research Part C: Emerging Technologies, 18(1), 6274. 4.                SUMO (Simulation of Urban Mobility). (2022). An open-source traffic simulation tool. Retrieved from https://www.eclipse.org/sumo. 5.                Wang, L., Zhang, Z., & Wang, Q. (2019). Deep learning-based adaptive traffic signal control: A survey. Applied Sciences, 9(13), 2615. 6.                Zhang, Y., & Zhang, L. (2020). IoT-based real-time adaptive traffic control for urban intersections. Sensors, 20(5), 1502.

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Publication Date

5/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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