Discovering Mobility Patterns on Bicycle-Based Public Transportation System by Using Probabilistic Topic Models
In this work, we present a new framework to discover the daily mobility routines which are contained in a real-life dataset collected from a bike-sharing system. Our goal is the discovery and analysis of mobility patterns which characterize the behavior of the stations of a bike-sharing system based on the number of available bikes along a day. An unsupervised methodology based on probabilistic topic models has been used to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. In particular, Latent Dirichlet allocation (LDA) has been used to discover mobility patterns. Our database has been collected for almost half a year from the Bicicas bike sharing system in Castellón (Spain). A set of experiments have been conducted to demonstrate the type of patterns that can be effectively discovered by using the proposed framework.
KeywordsTopic Model Latent Dirichlet Allocation Mobility Pattern Mobility Data Latent Topic
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