Towards Developing an Intelligent System to Suggest Optimal Path Based on Historic and Real-time Traffic Data

Traffic congestion is a common scenario in the metropolitan areas specially in developing countries like Bangladesh where people lose valuable time of their busy schedule by getting trapped in heavy traffic. Moreover, reliable traffic congestion avoidance or prediction mechanism for providing real-time traffic jam information and route selection is not up to the mark in Bangladesh. In this paper, we have proposed an intelligent system with a cost function using Ant Colony Optimization (ACO) and a meta-heuristic approach, which will calculate optimal paths of lowest travel cost considering both historic and real time traffic data and different time windows of a day. It will also dynamically re-route the path in case of heavy congestion during travel time for avoiding unusual situations. Experimental results show that the designed algorithm of the proposed system performs accordingly with reliable realtime traffic prediction and it's suggested routes provide better navigation and may save valuable time.