Research on Analysis Method of Characteristics Generation of Urban Rail Transit

With the development of society and economy, the urban rail transit has become one of the important components of urban transportation system, while the construction of the urban rail greatly improves the public transportation environments. Currently, there are many research focus on the passenger flow predictions according to their corresponding historical data, however, it is hard to assist transport models vary such volumes for a new station planning or being constructed. In view of this limitation, we provide a novel method for urban rail station characteristics analysis in intelligent transportation considering city land usages. Initially, point of interest (POIs) are divided by the proposed RC-tree (Colored R-tree)-based algorithm into the bounded areas for each station. Second, the Diversity and Proportion approaches are proposed to extract the top-k POIs from bounded areas based on their semantic and spatial characteristics. Then, classify the stations based on the similarity of the extracted top-k POIs. Moreover, we made a case study on real dataset, including a large volume of Automatic Fare Collection system (AFC) records for the experimental evaluations, and the results show that the proposed method can verify the rationality of land use and provide support for the application of transportation model technology.