File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Fine-grained analysis of transport-demographic relationships: County-level responses to multimodal connectivity across metropolitan and peripheral China

TitleFine-grained analysis of transport-demographic relationships: County-level responses to multimodal connectivity across metropolitan and peripheral China
Authors
KeywordsComputational urban science
Multimodal transport network
Network centrality
Population dynamics
Urban modelling
Urban systems
Issue Date23-Jun-2025
PublisherElsevier
Citation
Transportation Research Part A: Policy and Practice, 2025, v. 199 How to Cite?
Abstract

Despite extensive research on transport-induced demographic change, few studies have systematically investigated how different transport modes shape population distribution across diverse geographical contexts at fine spatial scales. This study explores the relationship between multimodal transport networks and population density using 20 years of county-level panel data, focusing on differential impacts in metropolitan and peripheral counties across China. Employing high-dimensional fixed effects models and centrality measures derived from aviation, high-speed rail and conventional rail networks, our results show that higher degree centrality (i.e., more direct connections) is positively associated with population concentration (+0.018%), while higher inverse closeness centrality (i.e., greater average shortest distance to all other nodes) is negatively associated (−0.005%). These effects are not instantaneous but emerge with significant five-year lags. Metropolitan counties experience approximately 4% greater population gains from improved connectivity than peripheral regions. While connectivity effects are observed across all transport modes, high-speed rail exhibits relatively consistent and positive associations with population growth over longer time lags, although its effects in metropolitan areas are generally weaker than those of aviation and conventional rail. Complementing the regression results, LightGBM-based SHAP analysis reveals substantial spatial heterogeneity: even within the same classification (metropolitan or peripheral), counties with advantageous network positions—such as regional hubs—exhibit markedly stronger demographic responses. The findings offer valuable guidance for urban planning and transport policy, emphasising the need for targeted, mode-sensitive investment strategies that account for regional disparities in transport access and development potential.


Persistent Identifierhttp://hdl.handle.net/10722/358133
ISSN
2023 Impact Factor: 6.3
2023 SCImago Journal Rankings: 2.182
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQu, Junxi-
dc.contributor.authorMa, Xiaoyi-
dc.contributor.authorZhou, Yang-
dc.contributor.authorChen, Xianlong-
dc.contributor.authorYang, Tianren-
dc.date.accessioned2025-07-24T00:30:39Z-
dc.date.available2025-07-24T00:30:39Z-
dc.date.issued2025-06-23-
dc.identifier.citationTransportation Research Part A: Policy and Practice, 2025, v. 199-
dc.identifier.issn0965-8564-
dc.identifier.urihttp://hdl.handle.net/10722/358133-
dc.description.abstract<p>Despite extensive research on transport-induced demographic change, few studies have systematically investigated how different transport modes shape population distribution across diverse geographical contexts at fine spatial scales. This study explores the relationship between multimodal transport networks and population density using 20 years of county-level panel data, focusing on differential impacts in metropolitan and peripheral counties across China. Employing high-dimensional fixed effects models and centrality measures derived from aviation, high-speed rail and conventional rail networks, our results show that higher degree centrality (i.e., more direct connections) is positively associated with population concentration (+0.018%), while higher inverse closeness centrality (i.e., greater average shortest distance to all other nodes) is negatively associated (−0.005%). These effects are not instantaneous but emerge with significant five-year lags. Metropolitan counties experience approximately 4% greater population gains from improved connectivity than peripheral regions. While connectivity effects are observed across all transport modes, high-speed rail exhibits relatively consistent and positive associations with population growth over longer time lags, although its effects in metropolitan areas are generally weaker than those of aviation and conventional rail. Complementing the regression results, LightGBM-based SHAP analysis reveals substantial spatial heterogeneity: even within the same classification (metropolitan or peripheral), counties with advantageous network positions—such as regional hubs—exhibit markedly stronger demographic responses. The findings offer valuable guidance for urban planning and transport policy, emphasising the need for targeted, mode-sensitive investment strategies that account for regional disparities in transport access and development potential.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofTransportation Research Part A: Policy and Practice-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectComputational urban science-
dc.subjectMultimodal transport network-
dc.subjectNetwork centrality-
dc.subjectPopulation dynamics-
dc.subjectUrban modelling-
dc.subjectUrban systems-
dc.titleFine-grained analysis of transport-demographic relationships: County-level responses to multimodal connectivity across metropolitan and peripheral China-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.tra.2025.104572-
dc.identifier.scopuseid_2-s2.0-105009269704-
dc.identifier.volume199-
dc.identifier.eissn1879-2375-
dc.identifier.isiWOS:001520988500001-
dc.identifier.issnl0965-8564-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats