Ant-based vehicle congestion avoidance system using vehicular networks

Abstract

Vehicle traffic congestion leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Instead of using static algorithms, e.g. Dijkstra and A*, we present a bio-inspired algorithm, food search behavior of ants, which is a promising way of solving traffic congestion in vehicular networks. We have called this the Antbased Vehicle Congestion Avoidance System (AVCAS). AVCAS combines the average travel speed prediction of traffic on roads with map segmentation to reduce congestion as much as possible by finding the least congested shortest paths in order to avoid congestion instead of recovering from it. AVCAS collects real-time traffic data from vehicles and road side units to predict the average travel speed of roads traffic. It utilizes this information to perform an ant-based algorithm on a segmented map resulting in avoidance of congestion. Simulation results conducted on various vehicle densities show that the proposed system outperforms the existing systems in terms of average travel time, which decreased by an average of 11.5%, and average travel speed which increased by an average of 13%. In addition, AVCAS handles accident conditions in a more efficient way and decreases congestion by using alternative paths.

Improve Traffic Management in the Vehicular Ad Hoc Networks by Combining Ant Colony Algorithm and Fuzzy System

“…They claim that both models reduce prediction error to 52% and travel time average to 70% in comparison to other methods. Jabbarpour et al [8] have also published a paper in which congestion avoidance is improved by traffic prediction. Their method combines predicting the average speed of traffic movement in roads with segmented maps and finding the shortest path with minimum congestion using the ant colony algorithm.…”

“…With map segmentation we can manage the dynamic and quick changes of vehicular environments and routing is accomplished for each segment individually instead of the whole map [8].…”

Green vehicle traffic routing system using ant-based algorithm

“…In this paper, we integrate our recently proposed AV-CAS approach (Jabbarpour et al (2014b)) with SIDRA fuel consumption and emission microscopic model (Akcelik and Besley (2003); Akçelik et al (2012)), called AV-CAS+SIDRA, in order to examine its impact on fuel consumption, number of stopped vehicles due to traffic congestion, vehicles' travel distance, speed, time and consequently CO 2 emission. In other words, AVCAS+SIDRA efficiency regarding green environment issues is evaluated in this paper.…”

Dynamic routing using maximal road capacity

“…To minimize the traveling time a dynamic routing algorithm is needed, which is able to adapt to the dynamic changes that take place in the traffic network. Recently, ant colony optimization was applied to the vehicle routing problems with time-dependent travel times [2,3,4,5,6,7,8].…”