Analyzing the Efficacy of Optimization Algorithms in Traffic Simulations

Abstract

As cities continue to grow, especially in developing countries the roads are getting congested due to vehicular traffic, giving rise to various problems including the undetermined mobility and sustainability efficiencies. Congestion and breakdown phenomenon are issues that are pervasive in the transportation system, decreasing throughput, and efficacy. Systems with limited resources require modelling to ensure systems performance, which may be in terms of cost, data transmitted or vehicles discharged. Stochastic traffic is predominant in empirical traffic systems and has reached the consensus. A number of simulation tools based on mathematical models and intelligent algorithms are in practice with respect to transportation systems. The primary objectives of these simulation tools are traffic modelling, planning, and operations of transportation systems. Irrespective of enormous data, high computing power and advanced techniques of intelligent transportation, several simulators are used to solve the traffic congestion problems. This paper reviews different traffic simulation tools and presents a comparative table based on Intelligent Transportation System (ITS) functionalities. The paper also presents an implementation of traffic simulators based on Reinforcement Learning, Genetic Algorithm and Queuing theory.