Genetic algorithm evolve for traffic optimization

Researchers developed a new genetic algorithm for optimizing the timing of signals in urban environments under severe traffic conditions.

Genetic algorithm evolve for traffic optimization

Researchers at the University of Technology Sydney and DATA61 have developed their approach entails the use of genetic algorithms (GAs), a popular computer science technique for solving optimization problems.

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Traffic control signals are the most widespread tools to control and manage road traffic in densely populated urban environments. A traffic signal’s settings, also known as signal control plan, can affect road traffic significantly, particularly when disruptions first arise.

The GA developed by Mao and his colleagues essentially explores all possible traffic signal control plans for a given intersection (e.g. the green time for “right turn” signals, “go straight’ signals, etc.).

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Its key objective is to minimize the total travel time in an area affected by a road accident by identifying the best combination of signal phases across all intersections within that area.

The researchers evaluated genetic algorithm using a four-intersection network designed in AIMSUN, a renowned traffic modeling platform. They constructed three different scenarios in which the GA had to optimize traffic signal timings under both normal conditions and with severe traffic.

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Mao said, “We are also researching to further shorten the computation time and further increase efficiency by coupling the GA with machine learning, which could speed-up the convergence rate towards the best solutions.”

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