Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model

Surya Pratap Singh Varma; Aadrika Jaiswal; Nirupam Das; Tiansheng Yang; Ruikai Sun; Rajkumar Singh Rathore

doi:10.1007/978-981-96-8343-7_25

Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model

Surya Pratap Singh Varma
, Aadrika Jaiswal
, Nirupam Das
, Tiansheng Yang^*
, Ruikai Sun
, Rajkumar Singh Rathore

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The rapid developing volume of urban activity has made an urgent need for smart arrangements to oversee congestion and improve road safety. This paper presents a Smart Traffic Management System (STMS) that utilizes computer vision to optimize real-time traffic flow. By analyzing live video streams from various traffic cameras, the system is able to evaluate vehicle congestion, classify different types of vehicles, and detect traffic violations. From the various application of methods such as object detection, motion tracking, and deep learning, the STMS alters traffic signals dynamically, making a difference to ease traffic congestion and improve traffic flow. Moreover the system can identify accidents and alarm the appropriate authorities to encourage convenient intervention. The system’s execution was tested over various urban conditions showing impressive advancements in both traffic management and traffic congestion reduction.

Original language	English
Title of host publication	Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025
Editors	Ajay Jaiswal, Sameer Anand, Aboul Ella Hassanien, Ahmad Taher Azar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	317-327
Number of pages	11
ISBN (Electronic)	9789819683437
ISBN (Print)	9789819683420
DOIs	https://doi.org/10.1007/978-981-96-8343-7_25
Publication status	Published - 2 Jan 2026
Event	International Conference on Computing Systems and Intelligent Applications, ComSIA 2025 - New Delhi, India Duration: 28 Mar 2025 → 29 Mar 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1500 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Computing Systems and Intelligent Applications, ComSIA 2025
Country/Territory	India
City	New Delhi
Period	28/03/25 → 29/03/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

Convolutional Neural Network (CNN)
Deep learning
Real-time monitoring
Smart Traffic Management System (STMS)
Sustainability
You Only Look Once (YOLO)

Access to Document

10.1007/978-981-96-8343-7_25

Cite this

Varma, S. P. S., Jaiswal, A., Das, N., Yang, T., Sun, R., & Rathore, R. S. (2026). Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model. In A. Jaiswal, S. Anand, A. E. Hassanien, & A. T. Azar (Eds.), Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025 (pp. 317-327). (Lecture Notes in Networks and Systems; Vol. 1500 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-8343-7_25

Varma, Surya Pratap Singh ; Jaiswal, Aadrika ; Das, Nirupam et al. / Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model. Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025. editor / Ajay Jaiswal ; Sameer Anand ; Aboul Ella Hassanien ; Ahmad Taher Azar. Springer Science and Business Media Deutschland GmbH, 2026. pp. 317-327 (Lecture Notes in Networks and Systems).

@inproceedings{91b61872f40a4346abaecaf9f3aac713,

title = "Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model",

abstract = "The rapid developing volume of urban activity has made an urgent need for smart arrangements to oversee congestion and improve road safety. This paper presents a Smart Traffic Management System (STMS) that utilizes computer vision to optimize real-time traffic flow. By analyzing live video streams from various traffic cameras, the system is able to evaluate vehicle congestion, classify different types of vehicles, and detect traffic violations. From the various application of methods such as object detection, motion tracking, and deep learning, the STMS alters traffic signals dynamically, making a difference to ease traffic congestion and improve traffic flow. Moreover the system can identify accidents and alarm the appropriate authorities to encourage convenient intervention. The system{\textquoteright}s execution was tested over various urban conditions showing impressive advancements in both traffic management and traffic congestion reduction.",

keywords = "Convolutional Neural Network (CNN), Deep learning, Real-time monitoring, Smart Traffic Management System (STMS), Sustainability, You Only Look Once (YOLO)",

author = "Varma, \{Surya Pratap Singh\} and Aadrika Jaiswal and Nirupam Das and Tiansheng Yang and Ruikai Sun and Rathore, \{Rajkumar Singh\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Computing Systems and Intelligent Applications, ComSIA 2025 ; Conference date: 28-03-2025 Through 29-03-2025",

year = "2026",

month = jan,

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doi = "10.1007/978-981-96-8343-7\_25",

language = "English",

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series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

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Varma, SPS, Jaiswal, A, Das, N, Yang, T, Sun, R & Rathore, RS 2026, Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model. in A Jaiswal, S Anand, AE Hassanien & AT Azar (eds), Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025. Lecture Notes in Networks and Systems, vol. 1500 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 317-327, International Conference on Computing Systems and Intelligent Applications, ComSIA 2025, New Delhi, India, 28/03/25. https://doi.org/10.1007/978-981-96-8343-7_25

Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model. / Varma, Surya Pratap Singh; Jaiswal, Aadrika; Das, Nirupam et al.
Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025. ed. / Ajay Jaiswal; Sameer Anand; Aboul Ella Hassanien; Ahmad Taher Azar. Springer Science and Business Media Deutschland GmbH, 2026. p. 317-327 (Lecture Notes in Networks and Systems; Vol. 1500 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Varma, Surya Pratap Singh

AU - Jaiswal, Aadrika

AU - Das, Nirupam

AU - Yang, Tiansheng

AU - Sun, Ruikai

AU - Rathore, Rajkumar Singh

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2026/1/2

Y1 - 2026/1/2

N2 - The rapid developing volume of urban activity has made an urgent need for smart arrangements to oversee congestion and improve road safety. This paper presents a Smart Traffic Management System (STMS) that utilizes computer vision to optimize real-time traffic flow. By analyzing live video streams from various traffic cameras, the system is able to evaluate vehicle congestion, classify different types of vehicles, and detect traffic violations. From the various application of methods such as object detection, motion tracking, and deep learning, the STMS alters traffic signals dynamically, making a difference to ease traffic congestion and improve traffic flow. Moreover the system can identify accidents and alarm the appropriate authorities to encourage convenient intervention. The system’s execution was tested over various urban conditions showing impressive advancements in both traffic management and traffic congestion reduction.

AB - The rapid developing volume of urban activity has made an urgent need for smart arrangements to oversee congestion and improve road safety. This paper presents a Smart Traffic Management System (STMS) that utilizes computer vision to optimize real-time traffic flow. By analyzing live video streams from various traffic cameras, the system is able to evaluate vehicle congestion, classify different types of vehicles, and detect traffic violations. From the various application of methods such as object detection, motion tracking, and deep learning, the STMS alters traffic signals dynamically, making a difference to ease traffic congestion and improve traffic flow. Moreover the system can identify accidents and alarm the appropriate authorities to encourage convenient intervention. The system’s execution was tested over various urban conditions showing impressive advancements in both traffic management and traffic congestion reduction.

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KW - Deep learning

KW - Real-time monitoring

KW - Smart Traffic Management System (STMS)

KW - Sustainability

KW - You Only Look Once (YOLO)

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T3 - Lecture Notes in Networks and Systems

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BT - Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025

A2 - Jaiswal, Ajay

A2 - Anand, Sameer

A2 - Hassanien, Aboul Ella

A2 - Azar, Ahmad Taher

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Computing Systems and Intelligent Applications, ComSIA 2025

Y2 - 28 March 2025 through 29 March 2025

ER -

Varma SPS, Jaiswal A, Das N, Yang T, Sun R, Rathore RS. Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model. In Jaiswal A, Anand S, Hassanien AE, Azar AT, editors, Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025. Springer Science and Business Media Deutschland GmbH. 2026. p. 317-327. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-8343-7_25

Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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