An Inverted Ant Colony Optimization approach to traffic

• Type: Article in International Scientific Journal
• Date: 2014

Authors

José Capela Dias (Author) Other; Penousal Machado (Author) Other; Daniel Castro Silva (Author) FEUP; Pedro Henriques Abreu (Author) Other

Journal

Title: Engineering Applications of Artificial Intelligence

Vol. 36

Pages: 122-133

ISSN: 0952-1976

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 35 Citations

ISI Web of Science

Scopus - 46 Citations

Pubmed / Medline

COMPENDEX

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-009-YEN

DOI: 10.1016/j.engappai.2014.07.005

Abstract (EN): With an ever increasing number of vehicles traveling the roads, traffic problems such as congestions and increased travel times became a hot topic in the research community, and several approaches have been proposed to improve the performance of the traffic networks. This paper introduces the Inverted Ant Colony Optimization (IACO) algorithm, a variation of the classic Ant Colony algorithm that inverts its logic by converting the attraction of ants towards pheromones into a repulsion effect. IACO is then used in a decentralized traffic management system, where drivers become ants that deposit pheromones on the followed paths; they are then repelled by the pheromone scent, thus avoiding congested roads, and distributing the traffic through the network. Using SUMO (Simulation of Urban MObility), several experiments were conducted to compare the effects of using IACO with a shortest time algorithm in artificial and real world scenarios - using the map of a real city, and corresponding traffic data. The effect of the behavior caused by this algorithm is a decrease in traffic density in widely used roads, leading to improvements on the traffic network at a local and global level, decreasing trip time for drivers that adhere to the suggestions made by IACO as well as for those who do not. Considering different degrees of adhesion to the algorithm, IACO has significant advantages over the shortest time algorithm, improving overall network performance by decreasing trip times for both IACO-compliant vehicles (up to 84%) and remaining vehicles (up to 71%). Thus, it benefits individual drivers, promoting the adoption of IACO, and also the global road network. Furthermore, fuel consumption and CO2 emissions from both vehicle types decrease significantly when using IACO (up to 49%).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12