Guest Editors
Dr. Jiaming Pei
Email: jiamingpei0262@ieee.org
Affiliation: School of Computer Science, The University of Sydney, Sydney, 2006, Australia
Homepage:
Research Interests: intelligent transportation system, neurosymbolic, machine learning, data mining
Prof. Shahid Mumtaz
Email: shahid.mumtaz@ntu.ac.uk
Affiliation: Department of Engineering, Nottingham Trent University, Nottingham, NG1 4FQ, United Kingdom
Homepage:
Research Interests: 5G-NR, quantum communication, machine Learning, smart IoT, satellite communication
Summary
Urban transportation systems are fundamental infrastructures of modern society and are naturally modeled as complex networks, consisting of nodes (intersections, stations) and edges (roads, railways, flight paths). With the rapid expansion of cities and increasing mobility demand, transportation networks face significant challenges such as congestion propagation, cascading failures, and robustness against disruptions.
Complex network theory provides powerful tools to analyze the topological structure, dynamic behavior, and resilience of transportation systems. Recent advances in computational methods, data-driven modeling, and intelligent optimization have further opened new avenues for research in transportation networks. By integrating complex systems science, big data analytics, and intelligent algorithms, researchers can now better understand traffic dynamics, identify critical nodes, and design more robust and efficient systems.
This Special Issue aims to collect high-quality contributions on the latest theoretical developments, modeling techniques, and practical applications of complex networks in transportation. Both original research articles and reviews are welcome.
Topics of Interest (include but not limited to):
· Complex network modeling of transportation systems (road, rail, air, maritime)
· Topological properties and robustness analysis of traffic networks
· Congestion formation and cascading failure in transportation systems
· Multilayer and interdependent transportation networks (e.g., road–public transit, logistics–energy)
· Data-driven approaches and machine learning for traffic flow prediction
· Complex network methods for smart city and intelligent transportation systems
· Community detection and modular structures in transportation networks
· Resilience enhancement strategies and optimization of transportation infrastructures
· Critical node and bottleneck identification using network metrics
· Simulation and visualization techniques for large-scale transportation networks
Keywords
complex networks, intelligent transportation systems, traffic dynamics, network robustness, congestion propagation
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