Dynamic Multi-Objective Navigation in Urban Transportation Network Using Ant Colony Optimization

Abstract

Intelligent Transportation System (ITS) is one of the most important urban systems that its functionality affects other urban systems directly and indirectly. In developing societies, increasing the transportation system efficiency is an important concern, because variety of problems such as heavy traffic condition, rise of the accident rate and the reduced performance happen with the rise of population. Route finding and navigation are two effective tools to reduce the pressure on the transportation system. Better navigation methods can reduce the traffic concentration in specific areas. In most of the cases, transportation networks are changing through time and they don’t have a static status. On the other hand, different users consider different objectives when they want to move through the transportation network. So, this paper proposed a novel method to solve dynamic navigation and route finding problem while considering different objectives. This new method is based on multi-objective Ant Colony Optimization (ACO). Experiments are designed in a simulated network and results are compared with static navigation in single-objective and multi-objective mode. Results indicated that the proposed method is performing very accurate in finding the optimal paths. Also the proposed method for dynamic navigation is performing better than the static navigation. It has improved the trip duration of the 80% of the altered routes and decreased the trip duration of some experiments up to 50%. These results indicate that the proposed method has the ability to solve multi-objective dynamic navigation in urban transportation systems in the presence of high rate traffic information.