A novel traffic routing method using hybrid Ant Colony System based on genetic algorithm

Lu, Dang-Nhac; Nguyen, Thi-Hau; Ngo, Thi-Thu-Trang; Nguyen, Ha-Nam

doi:10.1109/icoin.2017.7899563

Cited by 2 publications

(1 citation statement)

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“…It is essential for drivers to have sufficient knowledge to know the route to go to their desired destination. Traffic routing in urban areas and cities is mainly used to reduce congestion where traffic jam has turned out to be a standout problem in developing nations due to the fast development of economy and populace [6].…”

Section: Introductionmentioning

confidence: 99%

Traffic Routing Optimization using Ant Colony Optimization

Isa¹,

Branch²

2019

IJATCSE

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Traffic congestion is one of the main problems in traffic system and it occurs on most cities around the world. This project focuses on solving recurring congestion that occurs in area around Kuala Terengganu city. Traffic routing can be implemented to reduce traffic congestion by finding the best path for a vehicle. Ant Colony Optimization algorithm is applied to optimize traffic routing in this project. Ant Colony Optimization (ACO) takes inspiration from the foraging behavior of some ant species and these ants release pheromone on the ground in order to mark some favorable path that should be followed by other ants. Simulation of Urban Mobility (SUMO) is used for the simulation of traffic congestion and traffic routing. Traffic Control Interface (TraCI) gives the program that is written in Python to access the traffic while the simulation is running and it allows to retrieve values of road travel time and to re-route a vehicle based on the path that is found by ACO. The efficiency of ACO is compared with Dijkstra algorithm. It is found that average travel time for vehicle simulation in ACO perform 30% faster than Dijkstra.

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Section: Introductionmentioning

confidence: 99%