Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective

Yuliang Guo, Zhenjun Zhu (), Xiaohong Jiang, Ting Chen and Qing Li
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Yuliang Guo: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Zhenjun Zhu: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Xiaohong Jiang: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Ting Chen: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Qing Li: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China

Sustainability, 2024, vol. 16, issue 9, 1-20

Abstract: This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

Keywords: urban rail stations; big data; land use; network features; passenger flow distribution; station classification analysis; management strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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