Estimation of Link Speed Using Pattern Classification of GPS Probe Car Data

In the field of Intelligent Transport Systems, there have been many attempts to predict the speed of the vehicle by adopting the pattern of the speed using artificial intelligence and statistical methods. Traffic information has been collected mainly by fixed devices which are costly and hard to maintain. Recently, traffic data is progressively being collected by the probe cars equipped with GPS receivers. Most of probe cars are comprised of public and commercial transportation such as taxis and buses since the private drivers are reluctant to give their location information due to the privacy issues. This creates problem of insufficient number of cars available and the traditional analysis methods used for analyzing the data collected by the fixed devices are not applicable. The aim of this research is to propose and test a new method of calculating the optimal link speed for the collected information from probe cars. We propose the adoption of a fuzzy c-mean method for this purpose. In this paper the GPS speed data are automatically classified into three groups of speed patterns such as low, middle, and high speed and the link speed is predicted from the pattern clusters. In performance tests, the proposed method provides significantly better results than normal average speed data.