Scipediacontent: Scipediacontent moved page Draft Content 308419165 to Bayen et al 2010a

2021-02-01T21:40:09Z

Scipediacontent moved page Draft Content 308419165 to Bayen et al 2010a

Scipediacontent: Created page with " == Abstract == Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse prob..."

2021-02-01T21:40:05Z

Created page with " == Abstract == Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse prob..."

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== 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:

* [https://zenodo.org/record/1274676 https://zenodo.org/record/1274676] under the license https://creativecommons.org/share-your-work/public-domain/cc0/

* [https://zenodo.org/record/1274676/files/article.pdf https://zenodo.org/record/1274676/files/article.pdf]

* [http://xplorestaging.ieee.org/ielx5/5610907/5624963/05624994.pdf?arnumber=5624994 http://xplorestaging.ieee.org/ielx5/5610907/5624963/05624994.pdf?arnumber=5624994],
: [http://dx.doi.org/10.1109/itsc.2010.5624994 http://dx.doi.org/10.1109/itsc.2010.5624994] under the license cc0

* [http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005624994 http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005624994],
: [https://dblp.uni-trier.de/db/conf/itsc/itsc2010.html#HerringHAB10 https://dblp.uni-trier.de/db/conf/itsc/itsc2010.html#HerringHAB10],
: [https://ieeexplore.ieee.org/document/5624994 https://ieeexplore.ieee.org/document/5624994],
: [https://trid.trb.org/view.aspx?id=1096495 https://trid.trb.org/view.aspx?id=1096495],
: [http://ieeexplore.ieee.org/document/5624994 http://ieeexplore.ieee.org/document/5624994],
: [https://doi.org/10.1109/ITSC.2010.5624994 https://doi.org/10.1109/ITSC.2010.5624994],
: [http://www.cs.berkeley.edu/~pabbeel/papers/HerringHofleitnerAbbeelBayen_ITSC2010.pdf http://www.cs.berkeley.edu/~pabbeel/papers/HerringHofleitnerAbbeelBayen_ITSC2010.pdf],
: [https://academic.microsoft.com/#/detail/2031346385 https://academic.microsoft.com/#/detail/2031346385]

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Bayen et al 2010a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 308419165 to Bayen et al 2010a

Scipediacontent: Created page with " == Abstract == Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse prob..."