Intelligent Traffic Data Processing for ITS Applications

Abstract

Real-time traffic data are the lifeline sustaining the operation of Advanced Traffic Management and Advanced Traveler Information Systems (ATMS/ATIS). Data are essential to drive algorithms related to congestion/incident detection, travel time forecasting, and real-time route guidance. A common problem in many ATMS/ATIS applications is the sparsity of real-time traffic data, reflecting the financial constraints of acquiring and maintaining large-scale traffic monitoring systems. This paper proposes the use of intelligent processing, or data integration tools, to overcome the data sparsity problem and make the best use of existing data resources. This approach recognizes the elements of uncertainty and vagueness in defining and solving the problem. An example application of the proposed data integration method is presented in the context of a congestion detection algorithm. The method uses an imprecise knowledge representation within the framework of fuzzy operator logic (FOL) and the modified Dempster-Shafer rule of combination. Results indicate that knowledge of a link congestion status (i.e., congested or uncongested) increased several folds after the data integration algorithm was applied. Further work is needed to calibrate the algorithm in the field (in this study simulation was used) and to apply the procedures on large-scale networks.