A New Route Selection System: Multiparameter Ant Algorithm Based Vehicle Navigation Approach
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Toward vision-based intelligent navigator: its concept and prototype
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Reliable Pretrip Multipath Planning and Dynamic Adaptation for a Centralized Road Navigation System
IEEE Transactions on Intelligent Transportation Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy control approach for adaptive traffic routing
IEEE Communications Magazine
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SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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International Journal of Bio-Inspired Computation
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Route selection in metropolises based on specific desires is a major problem for city travelers as well as a challenging demand of car navigation systems. This paper introduces a multiparameter route selection system which employs fuzzy logic (FL) for local pheromone updating of an ant colony system (ACS) in detection of optimum multiparameter direction between two desired points, origin and destination (O/D). The importance rates of parameters such as path length and traffic are adjustable by the user. In this system, online traffic data are supplied directly by a traffic control center (TCC) and further minutes traffic data are predicted by employing artificial neural networks (ANNs). The proposed system is simulated on a region of London, United Kingdom, and the results are evaluated.