Streaming route assignment with prior temporal traffic data

The cost of traffic congestions has been significantly high in many countries. Traffic congestion can be minimized by coordinated route allocation to maximize the traffic efficiency in the whole road network. Unfortunately, the existing traffic management systems cannot achieve this type of optimization as vehicles tend to follow the shortest/fastest routes to their destinations. Such individually optimized routes may cause significant congestions in a road network. In the coming era of connected autonomous vehicles, traffic management systems can have access to a huge volume of prior temporal traffic data that depicts the historical traffic conditions collected at regular time intervals. This type of data provides great opportunities for traffic optimization at the network level. We propose a new route assignment algorithm for the era of connected autonomous vehicles. Our algorithm optimizes traffic based on real-time traffic conditions and prior temporal traffic data. Our experiments show that the proposed algorithm can improve traffic efficiency by up to 10% over the state-of-the-art algorithm.