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.
Traffic Estimation And Prediction Based On Real Time Floating Car Data
01.10.2008
711374 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Traffic Speed Estimation and Prediction Using Floating Car Data
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