Universität zu Köln

Li, Ziyue ORCID: 0000-0003-4983-9352, Yan, Hao ORCID: 0000-0002-4322-7323, Zhang, Chen ORCID: 0000-0002-4767-9597 and Tsung, Fugee ORCID: 0000-0002-0575-8254 (2022). Individualized passenger travel pattern multi-clustering based on graph regularized tensor latent dirichlet allocation. Data Min. Knowl. Discov., 36 (4). S. 1247 - 1279. DORDRECHT: SPRINGER. ISSN 1573-756X

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Abstract

Individual passenger travel patterns have significant value in understanding passenger's behavior, such as learning the hidden clusters of locations, time, and passengers. The learned clusters further enable commercially beneficial actions such as customized services, promotions, data-driven urban-use planning, peak hour discovery, and so on. However, the individualized passenger modeling is very challenging for the following reasons: 1) The individual passenger travel data are multi-dimensional spatiotemporal big data, including at least the origin, destination, and time dimensions; 2) Moreover, individualized passenger travel patterns usually depend on the external environment, such as the distances and functions of locations, which are ignored in most current works. This work proposes a multi-clustering model to learn the latent clusters along the multiple dimensions of Origin, Destination, Time, and eventually, Passenger (ODT-P). We develop a graph-regularized tensor Latent Dirichlet Allocation (LDA) model by first extending the traditional LDA model into a tensor version and then applies to individual travel data. Then, the external information of stations is formulated as semantic graphs and incorporated as the Laplacian regularizations; Furthermore, to improve the model scalability when dealing with massive data, an online stochastic learning method based on tensorized variational Expectation-Maximization algorithm is developed. Finally, a case study based on passengers in the Hong Kong metro system is conducted and demonstrates that a better clustering performance is achieved compared to state-of-the-arts with the improvement in point-wise mutual information index and algorithm convergence speed by a factor of two.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Li, Ziyue UNSPECIFIED orcid.org/0000-0003-4983-9352 UNSPECIFIED Yan, Hao UNSPECIFIED orcid.org/0000-0002-4322-7323 UNSPECIFIED Zhang, Chen UNSPECIFIED orcid.org/0000-0002-4767-9597 UNSPECIFIED Tsung, Fugee UNSPECIFIED orcid.org/0000-0002-0575-8254 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-680840
DOI:	10.1007/s10618-022-00842-3
Journal or Publication Title:	Data Min. Knowl. Discov.
Volume:	36
Number:	4
Page Range:	S. 1247 - 1279
Date:	2022
Publisher:	SPRINGER
Place of Publication:	DORDRECHT
ISSN:	1573-756X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Computer Science, Artificial Intelligence; Computer Science, Information Systems Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68084