Vehicle routing in a congested city using a centralized real-time traffic information system and direct experience

Abstract

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 are able to predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This thesis studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. This data is made available to all users, such that they may be able to predict the effects of congestion when building a route.

The method is further enhanced by combining the traffic information system data

with previous routing experiences. 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.