Machine Learning driven complex network analysis of transport systems

Abstract

A 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.