This paper is concerned with the task of travel-time prediction for an arbitrary origin-destination pair on a map. Unlike most of the existing studies, which focus only on a particular link (road segment) with heavy traffic, our method allows us to probabilistically predict the travel time along an unknown path (a sequence of links) if the similarity between paths is defined as a kernel function. Our first innovation is to use a string kernel to represent the similarity between paths. Our second new idea is to apply Gaussian process regression for probabilistic travel-time prediction. We tested our approach with realistic traffic data.
Given a collection of trips and their corresponding travel times, one study REF proposes a Gaussian process regression based method to predict the travel times for unseen trips.
Travel-time prediction using Gaussian process regression: A trajectorybased approach
in Proc. of the 9th SIAM international conference on Data Mining (SDM), 2009
Computer Science