SGER: Map-Matching and Reactive Routing Algorithms for Traffic Estimation and Prediction Systems

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Map-matching and routing algorithms are essential technical components for Traffic Estimation and Prediction Systems (TREPS). GPS tracking data is used to assess traffic condition, however noise and errors require sophisticated map-matching algorithms to identify corresponding routes in the road network. Current map-matching algorithms are too slow and inaccurate for real-time tracking, and are not quality-tested. Current routing algorithms are based on databases which store static travel-time weights per road segment, although continuous and rapid changes in traffic conditions demand the incorporation of dynamically changing weights. This project (i) follows unconventional approaches for the design of faster and more accurate map-matching algorithms, including novel ideas to evaluate their quality, and (ii) initiates research of reactive routing algorithms that adapt their solution to a dynamically changing weight database, including research of supportive novel database techniques.This research involves localizing high-quality global map-matching algorithms in a provably correct manner, and introduces quality evaluation based on artificial GPS data. Research into reactive routing algorithms will be initiated, which requires novel data warehousing techniques for the joint management of historic and current spatiotemporal data. This research will greatly enhance the knowledge base for TREPS and for both database and algorithms research communities. The area of personal routing and navigation solutions is quickly on the rise since every mobile phone can potentially be turned into a navigational aid. Results of this project have a high potential for utilization in this booming area and in areas such as traffic assessment and prediction, tasks that are performed by traffic management authorities such as municipalities and ministries. This research involves an exploratory international collaboration between Greece and the US.This grant is jointly funded by OISE, Europe and Eurasia, and CCF/TF Cluster.