| Rakshit, Arkaprabha, Karmakar, Pritam, Mishra, Sushruta, Yang, Tiansheng, Sun, Ruikai and Rathore, Rajkumar Singh 2025. A tactical traffic management solution for smart cities using reinforcement learning. Presented at: Proceedings of the International Conference On Innovative Computing And Communication, New Delhi, India, 14-15 February 2025. Published in: Hassanien, Aboul Ella, Anand, Sameer, Jaiswal, Ajay and Kumar, Prabhat eds. Innovative Computing and Communications. Lecture Notes in Networks and Systems , vol.1434 Springer, pp. 309-318. 10.1007/978-981-96-7523-4_22 |
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Business (Including Economics) |
| Publisher: | Springer |
| ISBN: | 9789819675227 |
| ISSN: | 2367-3370 |
| Last Modified: | 30 Oct 2025 15:26 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181980 |
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