An Artificial Intelligence Techniques and Simulation Model to Control a Traffic Jam System in Malaysia

Abstract

Traffic jam in Malaysia is a huge and complicated problem nowadays, due to the rapid increase in the demand for transportation. This causes a longer vehicle travel times, increased energy consumption, growing environmental pollution, reduced traffic safety, and a decrease in the efficiency of transportation infrastructure. Hence, controlling the flow of traffic has become a very important issue under a growing pressure to relieve traffic jam. In this study, a new Artificial Intelligence Techniques (AIT) and Simulation Model (SM) are applied in order to elicit a general diagnosis for the traffic congestion problem in Kuala Lumpur and Kuantan. An integrated model involves a Neural Network (NN), Fuzzy Logic (FL), Genetic Algorithm (GA), and Simulation Model (SM) is used. The current traffic demand data will be captured by strategically placed cameras. By receiving and processing data, we plan to use our integrated model to adjust traffic lights timing to optimize traffic flow in coordinated traffic lights systems, in order to minimize the traffic congestion through controlling traffic lights. The results of this study will be reported and used to suggest and apply more efficient transportation policies with the aim of providing useful insights on traffic congestion problem, and to assist the Malaysian decision makers to elaborate the best transportation policies.