Spatio-Temporal Patterns of Passengers’ Interests at London Tube Stations
نویسندگان
چکیده
With as many as 3.5 million passengers using the London underground system every day, it is desirable to examine and understand their interests and opinions, and to harness this information to improve the services of Transport for London (TfL). This research aims to achieve a better understanding of passengers’ interests by harvesting text from geo-tagged Tweets sourced over a four week period in 2014 from the vicinity of the stations. An unsupervised topic modelling method Latent Dirichlet Allocation (LDA) is used to generate topics, and k-means is used to cluster stations in order to understand the overall patterns of topics.
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