Abstract

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse probe data represents the vast majority of the data available on arterial roads in most major urban environments. This article proposes a probabilistic modeling framework for estimating and predicting arterial travel time distributions using sparsely observed probe vehicles. We evaluate our model using data from a fleet of 500 taxis in San Francisco, CA, which send GPS data to our server every minute. The sampling rate does not provide detailed information about where vehicles encountered delay or the reason for any delay (i.e. signal delay, congestion delay, etc.). Our model provides an increase in estimation accuracy of 35% when compared to a baseline approach for processing probe vehicle data.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/itsc.2010.5624994 under the license cc0
https://dblp.uni-trier.de/db/conf/itsc/itsc2010.html#HerringHAB10,
https://ieeexplore.ieee.org/document/5624994,
https://trid.trb.org/view.aspx?id=1096495,
http://ieeexplore.ieee.org/document/5624994,
https://doi.org/10.1109/ITSC.2010.5624994,
http://www.cs.berkeley.edu/~pabbeel/papers/HerringHofleitnerAbbeelBayen_ITSC2010.pdf,
https://academic.microsoft.com/#/detail/2031346385
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Document information

Published on 01/01/2010

Volume 2010, 2010
DOI: 10.1109/itsc.2010.5624994
Licence: Other

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