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An Efficient Algorithm to Estimate Real-time Traffic Information based on Multiple Data Sources

Gathering traffic congestion information from all available sources to provide real-time traffic information not only makes reliable traffic predictions for management center, but also supports travelers to help guiding their transit decision. However, the key issue is that the quality of existing multiple traffic data sources are uncertain, and how to use them for performing trusty travel time estimation is a question. In this paper, a novel algorithm is proposed to address this problem. Firstly, through analyzing large amounts of traffic data, the reliability of evidence and its relationship with road network are defined in spatio-temporal dimension. Secondly, after using an improved aggregation method based on Dempster-Shafer evidence theory, the optimized evidences are adopted to estimate each link’s average link travel time. Comparative experiments of the real test-vehicle scheduling signals and real-time system data (supported by some 15000 floating cars and 320 loop detectors) indicate that the new algorithm is proved to be both reasonable and practical. It can be applied in real-time systems to manage large amount of data.