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Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization by Tuo Mao; Adriana-Simona Mihaita; Fang Chen; Hai L. Vu is a Engineering article available to read on EtoBox.

What is Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization about?

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we pro

Who reads Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization?

Author

Tuo Mao; Adriana-Simona Mihaita; Fang Chen; Hai L. Vu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)