A Data Fusion Algorithm for Estimating Link Travel Time

Abstract

The growing demand for real-time traffic information brought about various types of traffic collection mechanisms in the area of Intelligent Transport Systems (ITS). There are, however, two procedures in making various traffic data into information. First, a robust information-making process of utilizing data into the representative information for each traffic collection mechanism is required. Second, the integration process of fusing the “estimated” information into the “representative information” for each link out of each source is also required. That is, both data reduction and/or data-to-information process and a higher-level information fusion are required. This article focuses on the development of an information fusion algorithm based on a voting technique, fuzzy regression, and Bayesian pooling technique for estimating dynamic link travel time in congested urban road networks. The algorithm has been proposed and validated using field experimental data—GPS probes and detector data collected over various roadway segments. It has been found that the estimated link travel time from the proposed algorithm is more accurate than the mere arithmetic mean counterpart from each traffic source. The limitations of the algorithm and future research agenda have also been discussed.