New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 11
Abstract
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by the National Natural Science Foundation of China (Grant No. U20A20330), the National Natural Science Foundation of China (Grant Nos. 52202387, 52172317, and 52131203), and the China Scholarship Council (Grant No. 202006090332).
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 11 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 21, 2022
Accepted: Jul 14, 2023
Published online: Sep 6, 2023
Published in print: Nov 1, 2023
Discussion open until: Feb 6, 2024
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