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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Classification of Beijing Metro Stations Based on Multi-source Data and Gaussian Mixture Model
verfasst von : Feng Wan, Jianrui Miao, Shuling Wang
Erschienen in: Green, Smart and Connected Transportation Systems
Verlag: Springer Singapore
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Abstract
At present, the metro plays an important role in people’s daily travel. In order to clarify the function of the metro stations and to improve the service level of the metro, the reasonable classification of metro stations is particularly necessary. In this paper, multi-source data including Internet data and ridership data is obtained, and the data is analyzed to obtain 12 clustering initial variables. After that, 3 common factors are extracted from the 12 initial variables by factor analysis. According to the extracted common factors, 249 metro stations in Beijing are divided into 4 clusters by Gaussian mixture model, and the probability values that a station belongs to each cluster are obtained.