Spatio-temporal Analysis of Dynamic Origin-Destination Data Using Latent Dirichlet Allocation. Application to Vélib' Bikesharing System of Paris.
This paper deals with a data mining approach applied on Bike Sharing System Origin-Destination data, but part of the proposed methodology can be used to analyse other modes of transport that similarly generate Dynamic Origin-Destination (OD) matrices. The transportation network investigated in this paper is the Vélib' Bike Sharing System (BSS) system deployed in Paris since 2007. An approach based on Latent Dirichlet Allocation (LDA), that extract the main features of the spatiotemporal behavior of the BSS is introduced in this paper. Such approach aims to summarize the behavior of the system by extracting few OD-templates, interpreted as typical and temporally localized demand profiles. The spatial analysis of the obtained templates can be used to give insights into the system behavior and the underlying urban phenomena linked to city dynamics.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ80 Statistical Methods.
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Corporate Authors:
500 Fifth Street, NW
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Authors:
- Côme, Etienne
- Randriamanamihaga, Andry
- Oukhellou, Latifa
- Aknin, Patrice
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Conference:
- Transportation Research Board 93rd Annual Meeting
- Location: Washington DC
- Date: 2014-1-12 to 2014-1-16
- Date: 2014
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 18p
- Monograph Title: TRB 93rd Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Data mining; Methodology; Origin and destination; Spatial analysis; Vehicle sharing
- Identifier Terms: Velib
- Geographic Terms: Paris (France)
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01520134
- Record Type: Publication
- Report/Paper Numbers: 14-0647
- Files: TRIS, TRB, ATRI
- Created Date: Mar 26 2014 10:13AM