Enhanced IOT Based Traffic control system for conscetion reduction and road safety

Abstract

Two of the biggest urban problems in the world are traffic congestion and traffic accidents. This study suggests an Internet of  Thigs (IoT)-based adaptive traffic signal control system that uses real-time traffic flow data to dynamically modify signal timings.

The system gathers information on vehicle density, speed and wait length via a network of carefully positioned sensors. The Q-learning method, a type of reinforcement learning is  then used to process this data in order to optimize signal timings, reduce traffic and enhance road safety. Comparing the suggested method to conventional fixed-time signal control and a simple actuated control system,simulation results using SUMO show how effective it is at lowering average travel time,queue length and the number of pauses.