Scipediacontent: Scipediacontent moved page Draft Content 925319183 to 238,295kn

2021-03-22T22:55:39Z

Scipediacontent moved page Draft Content 925319183 to 238,295kn

Scipediacontent: Created page with " == Abstract == A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management...."

2021-03-22T22:54:23Z

Created page with " == Abstract == A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management...."

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== Abstract ==

A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_925319183-beopen1299-1141-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.1155/2020/8810753 http://dx.doi.org/10.1155/2020/8810753] under the license http://creativecommons.org/licenses/by/4.0/

* [https://doi.org/10.1155/2020/8810753 https://doi.org/10.1155/2020/8810753]

* [http://downloads.hindawi.com/journals/jat/2020/8810753.pdf http://downloads.hindawi.com/journals/jat/2020/8810753.pdf],
: [http://downloads.hindawi.com/journals/jat/2020/8810753.xml http://downloads.hindawi.com/journals/jat/2020/8810753.xml],
: [http://dx.doi.org/10.1155/2020/8810753 http://dx.doi.org/10.1155/2020/8810753] under the license cc-by

* [http://dx.doi.org/10.1155/2020/8810753 http://dx.doi.org/10.1155/2020/8810753],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/jat/2020/8810753 https://www.hindawi.com/journals/jat/2020/8810753],
: [http://downloads.hindawi.com/journals/jat/2020/8810753.pdf http://downloads.hindawi.com/journals/jat/2020/8810753.pdf],
: [https://academic.microsoft.com/#/detail/3048519985 https://academic.microsoft.com/#/detail/3048519985]

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Scipediacontent: Scipediacontent moved page Draft Content 925319183 to 238,295kn

Scipediacontent: Created page with " == Abstract == A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management...."