Scipediacontent: Scipediacontent moved page Draft Content 939160170 to McIlwraith et al 2018a

2021-02-03T15:01:38Z

Scipediacontent moved page Draft Content 939160170 to McIlwraith et al 2018a

Scipediacontent: Created page with " == Abstract == Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. I..."

2021-02-03T15:01:35Z

Created page with " == Abstract == Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. I..."

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== Abstract ==

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.

Comment: KDD 2018. The first two authors share equal contribution

== Original document ==

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

* [http://arxiv.org/abs/1806.07380 http://arxiv.org/abs/1806.07380]

* [http://dx.doi.org/10.1145/3219819.3219895 http://dx.doi.org/10.1145/3219819.3219895]

* [http://hdl.handle.net/10044/1/73702 http://hdl.handle.net/10044/1/73702]

* [http://spiral.imperial.ac.uk/bitstream/10044/1/73702/2/1806.07380v1.pdf http://spiral.imperial.ac.uk/bitstream/10044/1/73702/2/1806.07380v1.pdf]

* [http://dl.acm.org/ft_gateway.cfm?id=3219895&ftid=1988760&dwn=1 http://dl.acm.org/ft_gateway.cfm?id=3219895&ftid=1988760&dwn=1],
: [http://dx.doi.org/10.1145/3219819.3219895 http://dx.doi.org/10.1145/3219819.3219895] under the license http://www.acm.org/publications/policies/copyright_policy#Background

* [https://dblp.uni-trier.de/db/conf/kdd/kdd2018.html#LiaoZWMCYGW18 https://dblp.uni-trier.de/db/conf/kdd/kdd2018.html#LiaoZWMCYGW18],
: [https://arxiv.org/pdf/1806.07380.pdf https://arxiv.org/pdf/1806.07380.pdf],
: [https://arxiv.org/abs/1806.07380 https://arxiv.org/abs/1806.07380],
: [http://spiral.imperial.ac.uk/bitstream/10044/1/73702/2/1806.07380v1.pdf http://spiral.imperial.ac.uk/bitstream/10044/1/73702/2/1806.07380v1.pdf],
: [https://ui.adsabs.harvard.edu/abs/2018arXiv180607380L/abstract https://ui.adsabs.harvard.edu/abs/2018arXiv180607380L/abstract],
: [https://dl.acm.org/citation.cfm?id=3219895 https://dl.acm.org/citation.cfm?id=3219895],
: [https://spiral.imperial.ac.uk/handle/10044/1/73702 https://spiral.imperial.ac.uk/handle/10044/1/73702],
: [https://doi.acm.org/10.1145/3219819.3219895 https://doi.acm.org/10.1145/3219819.3219895],
: [https://academic.microsoft.com/#/detail/2809366716 https://academic.microsoft.com/#/detail/2809366716]

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McIlwraith et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 939160170 to McIlwraith et al 2018a

Scipediacontent: Created page with " == Abstract == Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. I..."