Machine Learning driven complex network analysis of transport systems

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dc.contributor.authorXia, Yuqin
dc.contributor.authorWang, Kewei
dc.contributor.authorTanirat, Purin
dc.contributor.authorLee, Bryan
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorLi, Jun
dc.date.accessioned2025-06-25T15:04:11Z
dc.date.available2025-06-25T15:04:11Z
dc.date.freetoread2025-06-25
dc.date.issued2025-07
dc.date.pubOnline2025-05-15
dc.description.abstractA complex network is a system of interconnected nodes linked by edges, exhibiting non-trivial structural features such as community structure or scale-free distributions. This study develops a novel and generic Machine Learning-driven framework that integrates Complex Network Theory and Machine Learning methods for a comprehensive and multifaceted analysis of transport systems. Specifically, four key functional development and analysis are undertaken: 1) Network analysis, using complex network indicators to study the static properties of the transport systems; 2) Network clustering, employing K-means and hierarchical clustering methods to identify underlying community structures; 3) Network resilience, examining the networks' dynamic characteristics and structural evolution under escalating node attacks to evaluate their robustness; 4) Link and feature prediction, developing Graph Convolutional Networks (GCNs) and Multi-Layer Perceptron (MLP) models to predict hidden links and features. The proposed framework is subsequently applied to two distinct transport systems, namely, the China railway network and the Paris multi-modal transport system. The complex network analysis reveals distinct complex network features in network scale, density, and efficiency, yet both demonstrate a power-law distribution. The clustering analysis based on various node and edge properties exhibits a pattern of concentric circles, radiating outward from the urban to peripheral cities in China railway network, while a high density of short-distance connections within central Paris and a prevalence of long-distance connections in the outskirts. The network attack simulations show fine resilience of the Parisian multi-modal system and low resilience of the China railway network. For link prediction, an encoder-decoder model based on GCN and multiple MLPs are developed for various scenarios. The results for the China railway network reveal critical interregional links, emphasizing the need to strengthen regional connectivity, such as expanding the high-speed railway between Hainan Island and the mainland, and establishing a major transportation artery running from south to north. In the Paris transport system, this study predicts an interesting link extending from southern Paris eastward toward northern Seine-et-Marne, indicating a demand for a direct connection. For both networks, the hidden links are largely concentrated in more developed areas, likely driven by strong economic and social interaction demands, highlighting the need for more balanced transport network development. Overall, the results of this study align closely with existing literature and official transport development plans. This research contributes to the theoretical development in Complex Network Analysis using Machine Learning and offers valuable insight to improve the two transport systems.
dc.description.journalNameJournal of Transport Geography
dc.identifier.citationXia Y, Wang K, Tanirat P, et al., (2025) Machine Learning driven complex network analysis of transport systems. Journal of Transport Geography, Volume 127, July 2025, Article number 104270en_UK
dc.identifier.eissn1873-1236
dc.identifier.elementsID673325
dc.identifier.issn0966-6923
dc.identifier.paperNo104270
dc.identifier.urihttps://doi.org/10.1016/j.jtrangeo.2025.104270
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24046
dc.identifier.volumeNo127
dc.languageEnglish
dc.language.isoen
dc.publisherElsevieren_UK
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0966692325001619?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectClusteringen_UK
dc.subjectCommunity detectionen_UK
dc.subjectNetwork resilienceen_UK
dc.subjectLink predictionen_UK
dc.subjectGraph convolutional networksen_UK
dc.subjectMulti-layer perceptronen_UK
dc.subjectChina railway networken_UK
dc.subjectParis multi-modal transport systemen_UK
dc.subject33 Built Environment and Designen_UK
dc.subject35 Commerce, Management, Tourism and Servicesen_UK
dc.subject44 Human Societyen_UK
dc.subject3509 Transportation, Logistics and Supply Chainsen_UK
dc.subject4406 Human Geographyen_UK
dc.subject3304 Urban and Regional Planningen_UK
dc.subjectNetworking and Information Technology R&D (NITRD)en_UK
dc.subjectMachine Learning and Artificial Intelligenceen_UK
dc.subject11 Sustainable Cities and Communitiesen_UK
dc.subjectLogistics & Transportationen_UK
dc.titleMachine Learning driven complex network analysis of transport systemsen_UK
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2025-05-07

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