2023
Yue, Yifan; Chen, Jun; Feng, Tao; Ma, Xinwei; Wang, Wei; Bai, Hua
Classification and Determinants of High-Speed Rail Stations Using Multi-Source Data: A Case Study in Jiangsu Province, China Journal Article
In: Sustainable Cities and Society, pp. 104640, 2023, ISSN: 2210-6707.
Abstract | Links | BibTeX | Tags: Built environment, Geographically Weighted Multinomial Logit Model, High-Speed Rail Station, Influential factors, Mobile Phone Data
@article{YUE2023104640,
title = {Classification and Determinants of High-Speed Rail Stations Using Multi-Source Data: A Case Study in Jiangsu Province, China},
author = {Yifan Yue and Jun Chen and Tao Feng and Xinwei Ma and Wei Wang and Hua Bai},
url = {https://www.sciencedirect.com/science/article/pii/S2210670723002512},
doi = {https://doi.org/10.1016/j.scs.2023.104640},
issn = {2210-6707},
year = {2023},
date = {2023-01-01},
journal = {Sustainable Cities and Society},
pages = {104640},
abstract = {High-speed rail (HSR) stations play a vital role in the HSR system. HSR stations not only facilitate the accessibility of interregional transportation but also stimulate population movements across various cities in China. HSR stations in different cities can vary greatly, and an efficient HSR system requires an in-depth understanding of the interrelations between the related influential factors and spatiotemporal passenger flow patterns of different HSR stations. This study adopts a new scheme for clustering HSR stations based on passengers' arrival and departure time series using mobile phone data in Jiangsu, China. To this end, 71 HSR stations are clustered into 3 classes and the spatiotemporal characteristics of passenger flow at different stations are compared. Finally, a geographically weighted multinomial logit model (GWMNL) is built to explore the influence of the built environment, socioeconomic indicators, and HSR station attributes on the classification results of HSR stations related to the time-varying characteristics of passenger flow. The model results show that the number of entertainment POIs, population, population density, area, GDP and building area are significantly associated with the classification results of HSR stations. Additionally, for HSR stations under the same classification result, these variables also have different effects on them in the geographical dimension. According to these findings, quantitative analysis of the linkages between the passenger flow patterns at different HSR stations and the impacting factors would offer implications for planners and policymakers in HSR station planning and associated urban development.},
keywords = {Built environment, Geographically Weighted Multinomial Logit Model, High-Speed Rail Station, Influential factors, Mobile Phone Data},
pubstate = {published},
tppubtype = {article}
}
High-speed rail (HSR) stations play a vital role in the HSR system. HSR stations not only facilitate the accessibility of interregional transportation but also stimulate population movements across various cities in China. HSR stations in different cities can vary greatly, and an efficient HSR system requires an in-depth understanding of the interrelations between the related influential factors and spatiotemporal passenger flow patterns of different HSR stations. This study adopts a new scheme for clustering HSR stations based on passengers’ arrival and departure time series using mobile phone data in Jiangsu, China. To this end, 71 HSR stations are clustered into 3 classes and the spatiotemporal characteristics of passenger flow at different stations are compared. Finally, a geographically weighted multinomial logit model (GWMNL) is built to explore the influence of the built environment, socioeconomic indicators, and HSR station attributes on the classification results of HSR stations related to the time-varying characteristics of passenger flow. The model results show that the number of entertainment POIs, population, population density, area, GDP and building area are significantly associated with the classification results of HSR stations. Additionally, for HSR stations under the same classification result, these variables also have different effects on them in the geographical dimension. According to these findings, quantitative analysis of the linkages between the passenger flow patterns at different HSR stations and the impacting factors would offer implications for planners and policymakers in HSR station planning and associated urban development.