Probabilistic estimation of link travel times in dynamic road networks.

Due to the availability of large historical and real-time traffic data, car navigation systems are becoming more and more advanced in predicting the travel time for various routes and finding the fastest route from a source to a destination given a start time. The most advanced of these systems predict the travel time of the routes, given past traffic patterns in order to find the best route. However, the best route is not necessarily a reliable route as well, i.e., the route with the least variation in possible travel times. The most reliable route is desirable when traveling with a deadline, e.g., to reach a flight at the airport or to arrive on time for an important meeting. To find the most reliable route, one needs to predict the probability distribution of travel times for that route. This in turn requires the estimation of travel time probability distributions for each and every link, given a link-entrance-time. In this paper we address the problem of computing these link travel time distributions. To the best of our knowledge there has not been any study on how to compute probability distributions for links (/edges) in road networks. We show how this first step can affect the accuracy of the travel time distribution over the entire route. Our final challenge is to evaluate the result of different approaches in computing these travel time distributions, which is difficult because the reported travel time is not a single value but a probabilistic distribution highly depending on the trip start time. We thus propose a statistical test that enables us to evaluate these outcomes.