Ant-based load balancing in telecommunications networks
Adaptive Behavior
ARA - The Ant-Colony Based Routing Algorithm for MANETs
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Pheromone model: application to traffic congestion prediction
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
IEEE Transactions on Intelligent Transportation Systems
Heuristic techniques for accelerating hierarchical routing on road networks
IEEE Transactions on Intelligent Transportation Systems
A simple and effective method for predicting travel times on freeways
IEEE Transactions on Intelligent Transportation Systems
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
Optimal vehicle routing with real-time traffic information
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Ant colony optimisation for vehicle traffic systems: applications and challenges
International Journal of Bio-Inspired Computation
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Currently most car drivers use static routing devices based on the shortest distance between start and end position. But the shortest route can differ from the shortest route in time. To compute alternative routes it is necessary to have good prediction models of expected congestions and a fast algorithm to compute the shortest path while being able to react to dynamic changes in the network caused by special incidents. In this paper we present a dynamic routing system based on Ant Based Control (ABC). Starting from historical traffic data, ants are used to compute and predict the travel times along the road segments. They are finding the fastest routes not only looking to the past and present traffic conditions but also trying to anticipate and avoid future congestions.