A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning

Arkaprabha Rakshit; Pritam Karmakar; Sushruta Mishra; Tiansheng Yang; Ruikai Sun; Rajkumar Singh Rathore

doi:10.1007/978-981-96-7523-4_22

A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning

Arkaprabha Rakshit, Pritam Karmakar, Sushruta Mishra^*, 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

As urbanization accelerates, traffic congestion presents a significant challenge for smart cities, impacting mobility and air quality. The paper is all about traffic management solution using reinforcement learning (RL) for real-time traffic control. Our system is set to change the signal timings based on traffic conditions, pedestrian movements, and environmental factors learning it lively and accurately. Deploying a multi-layered architecture, where the medium manages their role unitedly to enhance traffic flow. Feedback mechanisms are there to process the model for effective intermediation. Simulations project that the approach is noticeable in reducing travel times and crowding. This research helps in the advancement of smarter, more flexible urban transportation systems, assisting ability to move and making the urban life easier.

Original language	English
Title of host publication	Innovative Computing and Communications - Proceedings of ICICC 2025
Editors	Aboul Ella Hassanien, Sameer Anand, Ajay Jaiswal, Prabhat Kumar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	309-318
Number of pages	10
ISBN (Electronic)	9789819675234
ISBN (Print)	9789819675227
DOIs	https://doi.org/10.1007/978-981-96-7523-4_22
Publication status	Published - 1 Oct 2025
Event	8th International Conference on Innovative Computing and Communication, ICICC 2025 - New Delhi, India Duration: 14 Feb 2025 → 15 Feb 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1434 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	8th International Conference on Innovative Computing and Communication, ICICC 2025
Country/Territory	India
City	New Delhi
Period	14/02/25 → 15/02/25

Keywords

Multi layered architecture
Real-time traffic control
Reinforcement learning
Traffic management
Urban transportation systems

UN SDGs

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

Access to Document

10.1007/978-981-96-7523-4_22

Cite this

Rakshit, A., Karmakar, P., Mishra, S., Yang, T., Sun, R., & Rathore, R. S. (2025). A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning. In A. E. Hassanien, S. Anand, A. Jaiswal, & P. Kumar (Eds.), Innovative Computing and Communications - Proceedings of ICICC 2025 (pp. 309-318). (Lecture Notes in Networks and Systems; Vol. 1434 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-7523-4_22

Rakshit, Arkaprabha ; Karmakar, Pritam ; Mishra, Sushruta et al. / A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning. Innovative Computing and Communications - Proceedings of ICICC 2025. editor / Aboul Ella Hassanien ; Sameer Anand ; Ajay Jaiswal ; Prabhat Kumar. Springer Science and Business Media Deutschland GmbH, 2025. pp. 309-318 (Lecture Notes in Networks and Systems).

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abstract = "As urbanization accelerates, traffic congestion presents a significant challenge for smart cities, impacting mobility and air quality. The paper is all about traffic management solution using reinforcement learning (RL) for real-time traffic control. Our system is set to change the signal timings based on traffic conditions, pedestrian movements, and environmental factors learning it lively and accurately. Deploying a multi-layered architecture, where the medium manages their role unitedly to enhance traffic flow. Feedback mechanisms are there to process the model for effective intermediation. Simulations project that the approach is noticeable in reducing travel times and crowding. This research helps in the advancement of smarter, more flexible urban transportation systems, assisting ability to move and making the urban life easier.",

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author = "Arkaprabha Rakshit and Pritam Karmakar and Sushruta Mishra and Tiansheng Yang and Ruikai Sun and Rathore, \{Rajkumar Singh\}",

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Rakshit, A, Karmakar, P, Mishra, S, Yang, T, Sun, R & Rathore, RS 2025, A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning. in AE Hassanien, S Anand, A Jaiswal & P Kumar (eds), Innovative Computing and Communications - Proceedings of ICICC 2025. Lecture Notes in Networks and Systems, vol. 1434 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 309-318, 8th International Conference on Innovative Computing and Communication, ICICC 2025, New Delhi, India, 14/02/25. https://doi.org/10.1007/978-981-96-7523-4_22

A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning. / Rakshit, Arkaprabha; Karmakar, Pritam; Mishra, Sushruta et al.
Innovative Computing and Communications - Proceedings of ICICC 2025. ed. / Aboul Ella Hassanien; Sameer Anand; Ajay Jaiswal; Prabhat Kumar. Springer Science and Business Media Deutschland GmbH, 2025. p. 309-318 (Lecture Notes in Networks and Systems; Vol. 1434 LNNS).

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

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AU - Rathore, Rajkumar Singh

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AB - As urbanization accelerates, traffic congestion presents a significant challenge for smart cities, impacting mobility and air quality. The paper is all about traffic management solution using reinforcement learning (RL) for real-time traffic control. Our system is set to change the signal timings based on traffic conditions, pedestrian movements, and environmental factors learning it lively and accurately. Deploying a multi-layered architecture, where the medium manages their role unitedly to enhance traffic flow. Feedback mechanisms are there to process the model for effective intermediation. Simulations project that the approach is noticeable in reducing travel times and crowding. This research helps in the advancement of smarter, more flexible urban transportation systems, assisting ability to move and making the urban life easier.

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Rakshit A, Karmakar P, Mishra S, Yang T, Sun R, Rathore RS. A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning. In Hassanien AE, Anand S, Jaiswal A, Kumar P, editors, Innovative Computing and Communications - Proceedings of ICICC 2025. Springer Science and Business Media Deutschland GmbH. 2025. p. 309-318. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-7523-4_22