FISS: function identification of subway stations based on semantics mining and functional clustering

Abstract

In modern cities, the subway system plays an important role in carrying a large proportion of passenger transport. However, there still remain some issues on how to accurately identify the regional functions of subway stations. In this study, the authors propose an approach named FISS for identifying the functions of subway station regions based on semantics mining and functional clustering. First, they extract the passenger's travel patterns of each subway station based on the smart card transaction data and Shanghai subway network data and calculate the relative point of interest (POI) contents of each subway station by using Shanghai POI data, then feed the two above-mentioned results into latent Dirichlet allocation model for obtaining the mobile semantics and the location semantics separately. Furthermore, they carry out standardisation after combining the two semantics, then extract the functional characteristic vectors of subway stations by conducting sparse principal components analysis, and cluster these vectors by using the improved k -means algorithm. At last, they visualise the result after taking subway station's function identification by the interclass passenger flow transfer, the distribution of geographical function proportion and the similarity of inter-cluster. The results demonstrate the accuracy and efficiency of the proposed approach compared with other existing methods.