2023
Yue, Yifan; Chen, Jun; Feng, Tao; Wang, Wei; Wang, Chunyang; Ma, Xinwei
In: Journal of Transportation Engineering, Part A: Systems, vol. 149, iss. 11, 2023.
Abstract | Links | BibTeX | Tags: Big data, classification algorithm, High-Speed Rail Station
@article{nokey,
title = {New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China},
author = {Yue, Yifan and Chen, Jun and Feng, Tao and Wang, Wei and Wang, Chunyang and Ma, Xinwei},
url = {https://ascelibrary.org/doi/abs/10.1061/JTEPBS.TEENG-7855},
doi = {https://doi.org/10.1061/JTEPBS.TEENG-7855},
year = {2023},
date = {2023-09-06},
urldate = {2023-09-06},
journal = {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.},
keywords = {Big data, classification algorithm, High-Speed Rail Station},
pubstate = {published},
tppubtype = {article}
}
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.
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.