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

^*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

Crynodeb

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.

Iaith wreiddiol	Saesneg
Teitl	Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025
Golygyddion	Ajay Jaiswal, Sameer Anand, Aboul Ella Hassanien, Ahmad Taher Azar
Cyhoeddwr	Springer Science and Business Media Deutschland GmbH
Tudalennau	317-327
Nifer y tudalennau	11
ISBN (Electronig)	9789819683437
ISBN (Argraffiad)	9789819683420
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-981-96-8343-7_25
Statws	Cyhoeddwyd - 2 Ion 2026
Digwyddiad	International Conference on Computing Systems and Intelligent Applications, ComSIA 2025 - New Delhi, India Hyd: 28 Maw 2025 → 29 Maw 2025

Cyfres gyhoeddiadau

Enw	Lecture Notes in Networks and Systems
Cyfrol	1500 LNNS
ISSN (Argraffiad)	2367-3370
ISSN (Electronig)	2367-3389

Cynhadledd

Cynhadledd	International Conference on Computing Systems and Intelligent Applications, ComSIA 2025
Gwlad/Tiriogaeth	India
Dinas	New Delhi
Cyfnod	28/03/25 → 29/03/25

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

NDC 9 Diwydiant, Arloesi a Seilwaith
NDC 11 Dinasoedd a Chymunedau Cynaliadwy

Mynediad at Ddogfen

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

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

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. Yn A. Jaiswal, S. Anand, A. E. Hassanien, & A. T. Azar (Gol.), Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025 (tt. 317-327). (Lecture Notes in Networks and Systems; Cyfrol 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. Gol. / Ajay Jaiswal ; Sameer Anand ; Aboul Ella Hassanien ; Ahmad Taher Azar. Springer Science and Business Media Deutschland GmbH, 2026. tt. 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,

day = "2",

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

language = "English",

isbn = "9789819683420",

series = "Lecture Notes in Networks and Systems",

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

pages = "317--327",

editor = "Ajay Jaiswal and Sameer Anand and Hassanien, \{Aboul Ella\} and Azar, \{Ahmad Taher\}",

booktitle = "Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025",

}

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. yn A Jaiswal, S Anand, AE Hassanien & AT Azar (gol.), Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025. Lecture Notes in Networks and Systems, cyfrol. 1500 LNNS, Springer Science and Business Media Deutschland GmbH, tt. 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. gol. / Ajay Jaiswal; Sameer Anand; Aboul Ella Hassanien; Ahmad Taher Azar. Springer Science and Business Media Deutschland GmbH, 2026. t. 317-327 (Lecture Notes in Networks and Systems; Cyfrol 1500 LNNS).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

TY - GEN

T1 - Optimization of Smart Traffic Management System Using Machine Vision-Based Functionalities with CNN Model

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.

KW - Convolutional Neural Network (CNN)

KW - Deep learning

KW - Real-time monitoring

KW - Smart Traffic Management System (STMS)

KW - Sustainability

KW - You Only Look Once (YOLO)

UR - https://www.scopus.com/pages/publications/105028312960

U2 - 10.1007/978-981-96-8343-7_25

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

M3 - Conference contribution

AN - SCOPUS:105028312960

SN - 9789819683420

T3 - Lecture Notes in Networks and Systems

SP - 317

EP - 327

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. Yn Jaiswal A, Anand S, Hassanien AE, Azar AT, golygyddion, Proceedings of International Conference on Computing Systems and Intelligent Applications - ComSIA 2025. Springer Science and Business Media Deutschland GmbH. 2026. t. 317-327. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-8343-7_25