Region Ridership Characteristic Clustering Using Passenger Flow Data

Region function is an integral part of urban planning. The extraction and mining of ridership characteristic can be regarded as data support of region function recognition. The advance of intelligent transportation technology in metro system enables the collection of spatial-temporal passenger flow data, which conveys human mobility and indicates the similarity between metro stations, also closely related to the region function during different periods. This paper discusses the ridership characteristic clustering using passenger trip pattern and metro station flow pattern extracted from metro passenger flow data. Firstly, we identify the passenger flow centrality and station tide flow from passenger trip pattern and metro station flow pattern, which imply the region function of metro stations. Secondly, by discovering the similarity between region cluster and text analysis, we take advantage of the classical probabilistic graphical model and propose a novel LDA-based region ridership characteristic clustering model, allocating metro stations with similar ridership characteristic into the same region. Thirdly, the experimental results show the passenger flow relationship among regions and recognize the region functions during different periods. The analysis of clustering results gives us a good understanding of how passenger flow circulates during different periods and may enables many valuable services like network design and crowd evacuation.