@article{Xiao2025, 
author = {Jialiang Xiao and Yongtao Zheng and Wei Wang and Xuedong Hua},
title = {Robustness analysis of a multimodal comprehensive transportation network from the perspectives of infrastructure and operation: A case study},
year = {2025},
journal = {Electronic Research Archive},
volume = {33},
number = {4},
pages = {1902-1945},
keywords = {comprehensive transportation infrastructure network, comprehensive transportation operation network, network modeling method, network robustness analysis},
url = {https://www.sciopen.com/article/10.3934/era.2025086},
doi = {10.3934/era.2025086},
abstract = {The development of a multimodal comprehensive transportation network (CTN) is crucial for enhancing connectivity and resilience in a regional transportation system. While China has established an extensive transportation infrastructure, the robustness of multimodal transportation systems remains insufficiently explored. Existing research primarily examines transportation networks from a single aspect, focusing either on infrastructure attributes or operational characteristics, while largely neglecting their interactions and disparities. To address this gap, this study analyzed the robustness of CTN from two perspectives, including a comprehensive transportation infrastructure network (CINet) and comprehensive transportation operation network (CONet). Based on complex network theory, optimized modeling methods of the networks were proposed. Utilizing multi-source data, statistical characteristics and robustness were comparatively explored in CINet, CONet, and their single-mode networks including highway, railway, navigation, and airway/airline (HINet, RINet, NINet, AINet, HONet, RONet, NONet, AONet) networks of Jiangsu Province. The results reveal that: 1) In Jiangsu, all the networks are not scale-free. All infrastructure networks (INets), except for AINet, do not exhibit small-world properties, while all the operation networks (ONets) are small-world. 2) All the networks are more robust to random attack than other strategies. CINet demonstrates the highest robustness among INets, whereas surprisingly, RONet is the most robust among ONets. Generally, INets exhibit superior robustness compared to ONets. 3) As the number of optimized hubs increases, the network robustness is much stronger, especially under calculated attacks. The improvements of    I  R  C and    I  R  R reach 4.55% and 114.56% in CINet, while reaching 4.10% and 99.24% in CONet, respectively, indicating a significant effect of optimized hub designs in network robustness enhancement. 4) When optimizing the same hubs, network robustness enhancement is more pronounced in CONet than in CINet. These findings highlight the importance of optimized hubs to multimodal comprehensive transportation systems, and provide guidance for network planning and management.}
}