A Review on Swarm Intelligence and Evolutionary Algorithms for Solving the Traffic Signal Control Problem

The rapid development of urban cities coupled with the rise in population has led to an exponentially growing number of vehicles on the roads for the latter to commute. This is adding to the already overbearing problem of traffic congestion. Short term, costly and short-sighted solutions of road infrastructure expansions are no longer suitable. One effective method of road resource allocation is focusing on the widely used traffic signal controllers' timing schedules. Searching for a suitable or an optimal schedule for the prior via brute force to ease traffic congestion might not be the most elegant or feasible solution. Nature-inspired algorithms including evolutionary and swarm intelligence algorithms are gaining a lot of momentum. Many of these algorithms have been used in the last two decades to address different applications in the smart city era including traffic signal control (TSC). This paper conducts a comprehensive literature review on applications of evolutionary and swarm intelligence algorithms to TSC. Surveyed work is categorized based on the set of decision variables, optimization objective(s), problem modeling and solution encoding. The paper, based on gaps identified by the conducted review, identifies promising future research directions and discusses where the future research is headed.

Automated summary

The rapid development of urban cities and the increase in population has led to a significant increase in the number of vehicles on the roads, resulting in severe traffic congestion. Short-term, expensive, and short-sighted road expansions are no longer suitable, and alternative solutions are needed. The use of evolutionary and swarm intelligence algorithms to optimize traffic signal control is an effective method. This paper provides a comprehensive literature review on the applications of these algorithms to traffic signal control, categorizing the surveyed work based on decision variables, optimization objectives, problem modeling, and solution encoding. Based on identified gaps, the paper identifies promising future research directions and discusses the future of research in this field.