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 inf...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-01, Vol.23 (1), p.48-63
Main Authors: Shaikh, Palwasha W., El-Abd, Mohammed, Khanafer, Mounib, Gao, Kaizhou
Format: Article
Language:English
Subjects:
Algorithms
bi-level optimization
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Green products
Literature reviews
meta-heuristics
multi-objective
Optimization
Particle swarm optimization
Resource allocation
Roads
Schedules
single-objective
Swarm intelligence
Traffic congestion
Traffic control
Traffic flow
traffic intersection
traffic signal control
Traffic signals
Urban areas
Urban development
Urbanization
Vehicles
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.3014296