DocumentCode :
1383759
Title :
Motorway travel time prediction based on toll data and weather effect integration
Author :
El Faouzi, Nour-Eddin ; Billot, Romain ; Bouzebda, S.
Author_Institution :
Lab. d´inge´nierie circulation Transp. (LICIT), INRETS - ENTPE, Bron, France
Volume :
4
Issue :
4
fYear :
2010
fDate :
12/1/2010 12:00:00 AM
Firstpage :
338
Lastpage :
345
Abstract :
This study reports the main findings of the Travel time Prediction based on electronic Toll collection (ETC) data with wEather effect integration on mOtorways (TPTEO) project aiming at developing and implementing a route planner tool and travel time prediction system on the interurban motorway network managed by French motorway AREA Company. One of the main innovative characteristics of this project is the use of toll collection data (TCD) to derive speed and travel time estimation and prediction. This data source is becoming more and more available on motorway facilities and provides an efficient way not only to estimate and predict travel time but also to achieve the origin-destination (OD) matrices elaboration and exhibit trends in traffic. Based on the previous studies on weather effect quantification, the prediction algorithm is designed to account for the weather impact during the prediction process. The resulting tool is considered to be accurate enough and paves the way for developing weather responsive advanced traffic management and information systems.
Keywords :
autoregressive moving average processes; pattern recognition; regression analysis; road traffic; time series; traffic engineering computing; electronic toll collection data; interurban motorway network; motorway travel time prediction; origin-destination matrices; route planner tool; speed estimation; travel time estimation; travel time prediction system; weather effect integration; weather effect quantification;
fLanguage :
English
Journal_Title :
Intelligent Transport Systems, IET
Publisher :
iet
ISSN :
1751-956X
Type :
jour
DOI :
10.1049/iet-its.2009.0140
Filename :
5640622
Link To Document :
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