Data-driven multi-agent vehicle routing in a congested city

Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This paper studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. These data are made available to all users, such that they may be able to learn and predict the effects of congestion for building a route adaptively. This method is further enhanced by combining the traffic information system data with previous routing experiences to determine the fastest route with less exploration. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone.