Reinforcement Learning for Adaptive Traffic Signal Timing in Range-Based TMS (Traffic Management System)

Reinforcement Learning for Adaptive Traffic Signal Timing in Range-Based TMS (Traffic Management System)

Authors- Associate Professor Dr .M Purna Kishore, P. V. Sai Bharadwaj, T. N. V. Satya Sandeep, Sk. Nazeer, P. Siva Krishna, Sk. Abdul Rasheed

Abstract--Conventional traffic signal control systems frequently fail to adapt to fluctuating traffic patterns, resulting in suboptimal traffic flow and heightened congestion levels. This project introduces an innovative solution that harnesses the capabilities of Reinforcement Learning (RL) to enhance traffic signal timing within a Range-Based Traffic Management System (RTMS). The RL agent is designed to optimize signal timing decisions. It receives feedback from the system, and there by reductions in congestion and enhancements in traffic flow. From a process of iterative learning, the RL agent refines its decision- making capabilities, resulting in progressively more efficient traffic signal management. Through this paper we tried to present a progress in the existing manual traffic control system.

DOI: /10.61463/ijset.vol.13.issue2.430