Traffic Estimation And Prediction Based On Real Time Floating Car Data

The knowledge of the actual current state of the road traffic. and its short-term evolution Cor the entire road network is a basic component of ATIS (Advanced Traveler Information Systems) and ATMS (Advancer Traffic Management System) applications. In this view the use of real-limb Floating-Car Data (FCD), based on traces of GPS positions, is emerging as a reliable and cost-effective way to gather accerate 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 manor 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 end 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.