The knowledge of the actual current state of the road traffic and its short-term evolution for the entire road network is a basic component of ATIS (Advanced Traveler Information Systems) and ATMS (Advanced Traffic Management System) applications. In this view the use of real-time Floating-Car Data (FCD), based on traces of GPS positions, is emerging as a reliable and cost-effective way to gather accurate travel times/speeds in a road network and to improve short-term predictions of travel conditions. The purpose of this paper is to present a large-scale working application of FCD-system, developed and operated by OCTOTelematics, delivering real-time traffic speed information throughout the Italian motorway network and along some important arterial streets located in major Italian metropolitan areas. Traffic speed estimates are deduced at an interval of 3 minutes from GPS traces transmitted in real-time from a large number (and still growing) of privately owned cars (about 600.000) equipped with a specific device covering a range of insurance-related applications. This paper also proposes two algorithms, respectively based on Artificial Neural Networks and Pattern-Matching, designed to on-line perform short-term (15 to 30 minutes) predictions of link travel speeds by using current and near-past link average speeds estimated by the OCTOTelematics FCD system. The Rome ring road (GRA-Grande Raccordo Anulare) was used for testing the feasibility of the two algorithms. Testing results showed that the proposed approaches for short-term predictions are very promising and effective.


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    Title :

    Traffic Estimation And Prediction Based On Real Time Floating Car Data


    Contributors:


    Publication date :

    2008-10-01


    Size :

    711374 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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