Traffic control optimization using Markov decision processes

Abstract

Traditional traffic light systems often operate inefficiently, especially in high-intensity traffic situations, due to their reliance on a fixed cycle (FC). These traditional traffic light systems follow predetermined cycles, switching between red, yellow and green signals at fixed intervals, without accommodating real time traffic conditions. As a result of inefficient time allocation, traffic jams often build up. Throughout the years, researchers approached this problem using different techniques, many of which using the Markov Decision Process (MDP) framework. The MDP is a framework for analysing the existence and structure of good policies, which are self-thought rules behind choosing an action and for devising procedures for finding such policies. The aim of this dissertation is to identify policies that help manage traffic efficiently by considering various factors contributing to traffic congestion. This is achieved by analysing the evolution of the state of the intersection and by applying MDP based policies to have dynamic control of the traffic light system. The results in this study illustrate that an MDP-driven cycle significantly outperforms a traditional FC, particularly during high-intensity traffic conditions, when it comes to managing traffic efficiently. This is demonstrated in this dissertation by virtue of the MDP-model that has been developed and formulated originally by the author.