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 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.

[1]  Joseph L. Schofer,et al.  Arterial incident detection using fixed detector and probe vehicle data , 1995 .

[2]  Karthik K. Srinivasan,et al.  Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network , 1996 .

[3]  M. Birattari,et al.  Lazy Learning for Local Modeling and Control , 1997 .

[4]  S. Turksma.  The various uses of floating car data , 2000 .

[5]  Der-Horng Lee,et al.  Probe Vehicle Population and Sample Size for Arterial Speed Estimation , 2002 .

[6]  B. Kedem,et al.  Regression Theory for Categorical Time Series , 2003 .

[7]  S. Eisenman,et al.  Using probe data to estimate OD matrices , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[8]  Toshiyuki Yamamoto,et al.  EN-ROUTE UPDATING METHODOLOGY OF TRAVEL TIME PREDICTION USING ACCUMULATED PROBE-CAR DATA , 2004 .

[9]  B.S. Kerner,et al.  Traffic state detection with floating car data in road networks , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[10]  Yixuan Li,et al.  Motorway incident detection using probe vehicles , 2005 .

[11]  Dobrivoje Popovic,et al.  Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control) , 2005 .

[12]  Sul.I. Bajwa,et al.  Performance evaluation of an adaptive travel time prediction model , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[13]  Zhongya Wei,et al.  Spatial and Temporal Analysis of Probe Vehicle-based Sampling for Real-time Traffic Information System , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[14]  Mingyan Liu,et al.  Surface street traffic estimation , 2007, MobiSys '07.

[15]  I. Iervolino,et al.  Computer Aided Civil and Infrastructure Engineering , 2009 .