Route classification in travel time estimation based on cellular network signaling

Travel time estimation based on cellular network signaling is a promising technology for delivery of wide area travel times in real-time. The technology has received much attention recently, but few academic research reports has so far been published in the area, which together with uncertain location estimates and environmental dependent performance makes it difficult to assess the potential of the technology. This paper aims to investigate the route classification task in a cellular travel time estimation context in detail. In order to estimate the magnitude of the problem, two classification algorithms are developed, one based on nearest neighbor classification and one based on Bayesian classification. These are then evaluated using field measurements from the GSM network. A conclusion from the results is that the route classification problem is not trivial even in a highway environment, due to effects of multipath propagation and changing radio environment. In a highway environment the classification problem can be solved rather efficiently using e.g., one of the methods described in this paper, keeping the effect on travel time accuracy low. However, in order to solve the route classification task in urban environments more research is required.