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New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China
| 题名： | New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China |
| 正文语种： | eng |
| 作者： | Yifan Yue;Jun Chen;Tao Feng;Wei Wang;Chunyang Wang;Xinwei Ma |
| 作者单位： | School of Transportation Southeast Univ. No. 2 Southeast University Rd. Nanjing 211189 China;School of Transportation Southeast Univ. No. 2 Southeast University Rd. Nanjing 211189 China;Urban and Data Science Graduate School of Advanced Science and Engineering Hiroshima Univ. 1-5-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8529 Japan Dept. of the Built Environment Eindhoven Univ. of Technology Eindhoven 5600MB Netherlands;School of Transportation Southeast Univ. No. 2 Southeast University Rd. Nanjing 211189 China;Hebei Guangtong Road and Bridge Group Co. Ltd. No. 61 Zhonghuanan Rd. Handan 056001 China;School of Civil and Transportation Engineering Hebei Univ. of Technology No. 5340 Xiping Rd. Tianjin 300401 China |
| 关键词： | High-speed railway (HSR) station; Time series data; Density-Based Spatial Clustering of Applications with Noise (DBSCAN); Evolution characteristics |
| 摘要： | Effective management of the high-speed railways (HSR) system requires an in-depth understanding of the HSR stations in the network, e.g., the time-dependent volume distribution. The classification of HSR stations is the scientific basis for transport policymaking and land-use planning. Existing classification methods cannot meet the needs of temporal variation of passenger flow or the refined design and operation of HSR stations. This study adopts the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to classify HSR stations in different years. Using the data of Jiangsu Province, China, as an example, the time series of arrival and departure passenger flow at HSR stations are clustered via the DBSCAN algorithm, and the HSR stations are clustered into three classes. To determine the hierarchical structure of HSR stations representing the evolution of HSR networks, we use large-scale panel data obtained from mobile phone cellular data across years (July 1-14 from each of the years 2018, 2020, and 2021) to capture and analyze the spatial-temporal evolution characteristics of massive passenger flow at HSR stations. It is indicated that both HSR station hierarchy and passenger flow have the characteristics of spatial-temporal evolution across years, and the classification results are influenced by the geographical positions of cities and HSR layout. Accurate clustering of HSR stations via large-scale actual passenger flow data enables railway authorities and operators to identify critical nodes for efficient HSR network performance. The resulting classification would contribute to an in-depth understanding of the evolution characteristics of passenger flow in different years. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 11 |
| 页码： | 04023108.1-04023108.12 |
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