research-article

An Inverted Ant Colony Optimization approach to traffic

Authors:

José Capela Dias,

Penousal Machado,

Daniel Castro Silva,

Pedro Henriques AbreuAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 36, Issue C

Pages 122 - 133

https://doi.org/10.1016/j.engappai.2014.07.005

Published: 01 November 2014 Publication History

Abstract

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%).

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Cited By

Rostami SBroumandnia AKhademzadeh A(2024)An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithmThe Journal of Supercomputing10.1007/s11227-023-05725-y80:6(7812-7848)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11227-023-05725-y
Straub J(2022)Impact of techniques to reduce error in high error rule-based expert system gradient descent networksJournal of Intelligent Information Systems10.1007/s10844-021-00672-758:3(481-512)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10844-021-00672-7
Klügl FBazzan ALujak MDusparic IKlügl FVizzari G(2021)Accelerating route choice learning with experience sharing in a commuting scenarioAI Communications10.3233/AIC-20158234:1(105-119)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/AIC-201582
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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 36, Issue C

November 2014

347 pages

ISSN:0952-1976

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 November 2014

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Cited By

Rostami SBroumandnia AKhademzadeh A(2024)An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithmThe Journal of Supercomputing10.1007/s11227-023-05725-y80:6(7812-7848)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11227-023-05725-y
Straub J(2022)Impact of techniques to reduce error in high error rule-based expert system gradient descent networksJournal of Intelligent Information Systems10.1007/s10844-021-00672-758:3(481-512)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10844-021-00672-7
Klügl FBazzan ALujak MDusparic IKlügl FVizzari G(2021)Accelerating route choice learning with experience sharing in a commuting scenarioAI Communications10.3233/AIC-20158234:1(105-119)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/AIC-201582
Nguyen TJung JHung CHong JBechini ASong E(2021)Inverse pheromone-based decentralized route guidance for connected vehiclesProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3441925(459-463)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3441925
Labib NDanoy GMusial JBrust MBouvry PNotare MSun P(2019)A Multilayer Low-Altitude Airspace Model for UAV Traffic ManagementProceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3345838.3355998(57-63)Online publication date: 25-Nov-2019
https://dl.acm.org/doi/10.1145/3345838.3355998
(2018)Hybrid model for enhancement of passenger information management systemInternational Journal of Networking and Virtual Organisations10.5555/3292902.329291219:2-4(270-288)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3292902.3292912
Jayoma JGerardo BMedina R(2018)Enhancing Inverse Ant Algorithm using Path Elimination RulesProceedings of the 2018 VII International Conference on Network, Communication and Computing10.1145/3301326.3303713(97-101)Online publication date: 14-Dec-2018
https://dl.acm.org/doi/10.1145/3301326.3303713
Ramos GBazzan AEsparcia-Alcázar A(2015)Towards the User Equilibrium in Traffic Assignment Using GRASP with Path RelinkingProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754755(473-480)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754755

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