Measuring Station-Area Functional Profiles of High-Speed Rail Stations through a Node-Place Approach: Nationwide Evidence from China

Abstract High-speed rail (HSR) stations are critical interfaces between national transport networks and urban systems, shaping both regional accessibility and local development. Yet, previous studies seldom offer a nationally comparable assessment of HSR station-area functional profiles, defined here as the combined performance and balance of transportation-related node functions and surrounding urban place functions within station areas. It remains unclear how railway-level attributes and city-level contexts jointly influence these profiles. This study proposes an integrated evaluation framework grounded in the node-place approach to quantify, classify, and explain the functional characteristics of HSR station areas. The framework combines a set of multidimensional indicators capturing transportation connectivity (node) and urban development attributes (place) through a weighted multicriteria evaluation to derive overall functional performance and incorporates an interpretable machine learning model to uncover nonlinear effects and interaction mechanisms among key determinants. A case study is conducted using 90 major HSR stations in China within the 15-min walking zone. Results show that the station-area function evaluation index is low on average but highly heterogeneous across space. Stations in eastern and northeastern China more often exhibit balanced node-place profiles, whereas stations in western regions more frequently display functional deficiencies. A common structural pattern is that node-related functions exceed place-related functions, indicating that network accessibility is not consistently matched by surrounding urban development. Railway-level attributes explain more variance than city-level factors, with station built time emerging as the most influential determinant. Its interactions with distance to the city center, station building area, and urban population shape distinct station-area functional profiles within threshold ranges. These findings provide an evidence base for differentiated station-area planning and targeted investments, supporting the transformation of HSR stations into integrated, multifunctional urban nodes.

Authors

Institutions

Publication Details

Journal
Journal of Transportation Engineering Part A Systems
Published
2026-10-09
DOI
https://doi.org/10.1061/jtepbs.teeng-9811
Primary Topic
Aviation Industry Analysis and Trends
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measuring Station-Area Functional Profiles of High-Speed Rail Stations through a Node-Place Approach: Nationwide Evidence from China

Jing Teng, Enhui Chen, Yupiao Huang
Journal of Transportation Engineering Part A Systems
Aviation Industry Analysis and Trends
article

Measuring Station-Area Functional Profiles of High-Speed Rail Stations through a Node-Place Approach: Nationwide Evidence from China

Jing Teng, Enhui Chen, Yupiao Huang
article en

Abstract

Abstract High-speed rail (HSR) stations are critical interfaces between national transport networks and urban systems, shaping both regional accessibility and local development. Yet, previous studies seldom offer a nationally comparable assessment of HSR station-area functional profiles, defined here as the combined performance and balance of transportation-related node functions and surrounding urban place functions within station areas. It remains unclear how railway-level attributes and city-level contexts jointly influence these profiles. This study proposes an integrated evaluation framework grounded in the node-place approach to quantify, classify, and explain the functional characteristics of HSR station areas. The framework combines a set of multidimensional indicators capturing transportation connectivity (node) and urban development attributes (place) through a weighted multicriteria evaluation to derive overall functional performance and incorporates an interpretable machine learning model to uncover nonlinear effects and interaction mechanisms among key determinants. A case study is conducted using 90 major HSR stations in China within the 15-min walking zone. Results show that the station-area function evaluation index is low on average but highly heterogeneous across space. Stations in eastern and northeastern China more often exhibit balanced node-place profiles, whereas stations in western regions more frequently display functional deficiencies. A common structural pattern is that node-related functions exceed place-related functions, indicating that network accessibility is not consistently matched by surrounding urban development. Railway-level attributes explain more variance than city-level factors, with station built time emerging as the most influential determinant. Its interactions with distance to the city center, station building area, and urban population shape distinct station-area functional profiles within threshold ranges. These findings provide an evidence base for differentiated station-area planning and targeted investments, supporting the transformation of HSR stations into integrated, multifunctional urban nodes.

Journal of Transportation Engineering Part A SystemsVol. 152(12)
Tongji University (CN)
Openalex Percentile: Top 5%
Aviation Industry Analysis and Trends
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.