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.

I. Introduction

According to the United Nations Department of Economic and Social Affairs (DESA) [1], more than 68% of the world’s population is expected to move to urban city areas by 2050. People living in the urban cities are expected to commute using their own private vehicles or public transport. Fig. 1 shows the number of passenger cars and commercial vehicles in use worldwide between 2006 and 2015. In 2015 alone, around 947 million passenger cars and 335 million commercial vehicles were in operation on roadways worldwide [2], [3]. Fig. 1.

The number of passenger cars and commercial vehicles in use worldwide between 2006 and 2015.