ACO Using a MCDM Strategy for Route Finding in the City of Guadalajara, México

Abstract

Car traffic problems become more important every day and the shortest distance route is not always the best because streets are saturated with cars, nearly at peak hours; when an event occurs or car traffic overpasses a threshold, streets reduce their fluent flow capacity, thus car drivers must seek new alternatives for their routes. In this work we propose an approach to synthesize street network characteristics and affectations on the streets (as number of available lanes, maximum allowed speed, etc.) into a significative value useful for route decision for path finding. Such variables are processed through a Multi-Criteria Decision Making (MCDM) method, which return a value for each street, that represents a level of quality to hold traffic flow, this is used with an Ant Colony Optimization (ACO) algorithm in order to find alternative routes that use those most fluid streets, those with the “better” characteristics; So, our contribution is the synthesizing of many street characteristics into a value that denote the quality of the street in the search space, helpful for path finding purposes. We use information from the city of Guadalajara, México, to perform experiments and show the advantages of this proposal.