Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning

Aude Hofleitner, Ryan Herring and Alexandre Bayen

Transportation Research Part B: Methodological, 2012, vol. 46, issue 9, 1097-1122

Abstract: This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile Millennium system since March 2009 with over 500 probe vehicles reporting their position once per minute in San Francisco, CA.

Keywords: Arterial traffic; Estimation; Forecast; Streaming data; Machine learning; GPS probe data (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (20)

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DOI: 10.1016/j.trb.2012.03.006

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