For a few years, route optimisation and efficient traffic flow are a big challenge, especially in a current situation when 54.5% population is living in the urban environment all over the world. At peak hours, the traffic jams in urban areas are frequent. Lots of works have been done in finding the shortest path to optimise the route to the destination in minimum time. However, moving vehicles toward the shorter paths causes a severe traffic jam in the city. Therefore, in this study, the authors proposed a framework to enhance the efficiency of the ant colony optimisation (ACO) algorithm to optimise the vehicular traffic, i.e. named as smart traffic distribution ACO. It helps to optimise the route and city traffic efficiently while avoiding congestion in all circumstances using up-to-date city traffic data. Their proposed framework finds the optimal path in such a way that the traffic flow on each road remains normal. The detection of congestion on the road at an early stage and even distribution of traffic on all roads helps to achieve maximum flow, speed, and optimum density of the roads.
Vehicular traffic optimisation and even distribution using ant colony in smart city environment
IET Intelligent Transport Systems ; 12 , 7 ; 594-601
2018-06-18
8 pages
Article (Journal)
Electronic Resource
English
smart city environment , up-to-date city traffic data , traffic flow , traffic jams , ant colony optimization , route optimisation , vehicle routing , vehicular traffic optimisation , urban environment , vehicular traffic optimization , road traffic , smart traffic distribution , ACO algorithm , ant colony optimisation , city traffic optimization
Metadata by IET is licensed under CC BY 3.0
Vehicular traffic optimisation and even distribution using ant colony in smart city environment
Wiley | 2018
|A Dynamic Vehicular Traffic Control Using Ant Colony and Traffic Light Optimization
Springer Verlag | 2014
|An ant colony optimisation model for traffic counting location problem
Online Contents | 2012
|Aeroelastic Tailoring Using Ant Colony Optimisation
AIAA | 2009
|