User-based Vehicle Route Guidance in Urban Networks Based on Intelligent Multi Agents Systems and the ANT-Q Algorithm

Guiding vehicles to their destination under dynamic traffic conditions is an important topic in the field of Intelligent Transportation Systems (ITS). Nowadays, many complex systems can be controlled by using multi agent systems. Adaptation with the current condition is an important feature of the agents. In this research, formulation of dynamic guidance for vehicles has been investigated based on the characteristics of ITS and using the user-based approach to meet drivers’ satisfaction. As a result of time-varying flows on traffic networks, a multi agent model and the routing algorithm based on artificial intelligence techniques emphasized on a hybrid algorithm combining Ant Colony and Reinforcement Learning is proposed. The critical result of this paper is the ability of designing an algorithm for better trip planning, routing decisions in a dynamic urban transportation. Finally, the validity of the proposed algorithm is shown by implementation on a sub-network extracted from Tehran traffic map.