Spatio-temporal Analysis of Dynamic Origin-Destination Data Using Latent Dirichlet Allocation: Application to Vélib' Bike Sharing System of Paris
Spatio-temporal Analysis of Dynamic Origin-Destination Data Using Latent Dirichlet Allocation: Application to Vélib' Bike Sharing System of Paris
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发表时间：
2014-01
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通讯作者：
E. Côme;Njato Andry Randriamanamihaga;L. Oukhellou;P. Aknin
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作者：
E. Côme;Njato Andry Randriamanamihaga;L. Oukhellou;P. Aknin
本文讨论了一种应用于共享单车系统起点-目的地数据的数据挖掘方法，但所提出的方法的一部分可以用于分析其他类似生成动态起点-目的地（OD）矩阵的交通方式。本文研究的交通网络是自2007年以来在巴黎部署的Velib自行车共享系统（BSS）系统。本文提出了一种基于潜狄利克雷分配（Latent Dirichlet Allocation， LDA）的方法来提取BSS的主要时空行为特征。这种方法旨在通过提取少量od模板来总结系统的行为，这些模板被解释为典型的和暂时本地化的需求概要。对获得的模板进行空间分析可以用来洞察系统行为和与城市动态相关的潜在城市现象。
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 analyze other modes of transport that similarly generate Dynamic Origin-Destination (OD) matrices. The transportation network investigated in this paper is the Velib’ Bike Sharing System (BSS) system deployed in Paris since 2007. An approach based on Latent Dirichlet Allocation (LDA), that extracts the main features of the spatio-temporal 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.