SHORT-TERM TRAVEL TIME PREDICTION USING DATA FROM INDUCTION LOOPS
Increasing car mobility has lead to an increasing demand for traffic information. This contribution deals with information about travel times. When car drivers are provided with this type of information, the travel times should ideally be the times that they will encounter. As a result travel times must first be measured, and further be predicted on a short-term basis. Since dual induction loop detectors yield spot measurements of flow and speed, travel times produced by data from these sensors can only be estimated, not actually measured. The performance of five algorithms to estimate travel times was assessed using a data set with actually measured travel times. These were collected through license plate recognition. Subsequently two estimation methods that are currently being used as if they produced predictions, i.e. Static Travel Time Estimations (STTE) and Dynamic Travel Time Estimations (DTTE), and a new travel time prediction method using an Artificial Neural Network (ANN) were applied on the A13 motorway from The Hague to Rotterdam and their performance were compared. Results show that the ANN method significantly outperformed DTTE, which in turn outperformed STTE.
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Supplemental Notes:
- Full Conference Proceedings available on CD-ROM.
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Corporate Authors:
1100 17th Street, NW, 12th Floor
Washington, DC United States 20036 -
Authors:
- HUISKEN, G
- van Berkum, E
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Conference:
- 9th World Congress on Intelligent Transport Systems
- Location: Chicago, Illinois
- Date: 2002-10-14 to 2002-10-17
- Publication Date: 2002
Language
- English
Media Info
- Pagination: 12p
Subject/Index Terms
- TRT Terms: Artificial intelligence; Dynamic traffic assignment; Estimating; Induction heating; Information technology; Loop detectors; Neural networks; Static and dynamic tests; Time management; Travel time
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 00960315
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 29 2003 12:00AM