Vulnerability analysis of inland waterways network base on complex network theory

ABSTRACT

Inland waterway transportation system has the advantages of low investment, large capacity, low cost, and low energy consumption, which is an important part of modern comprehensive transportation system. The research on the network characteristics of inland waterways is helpful to strengthen the pertinence of inland waterways network maintenance, improve the emergency response capability of inland waterway, and mitigate the adverse impact of emergencies on the waterways network caused by unexpected events. Complex network theory is an appropriate approach for understanding network dynamics, and it has been applied in studying some transportation networks. In this study, it is introduced into the study of the basic characteristics of inland waterways network and the evolution of the network under different attack scenarios. Taking the inland waterways network of Shanghai as an example, the models of inland waterways network are established based on Primal approach and Dual approach. The characteristics of the network models are analyzed and the network characteristics of the waterways network are obtained, and the evolution of network structure under different attack scenarios is simulated. The discoveries are of great reference significance to the management, planning, and emergency preparedness of the inland waterways network of Shanghai.

KEYWORDS:

• Complex network theory
• inland waterways network
• vulnerability study
• network characteristics
• random network

Acknowledgment

The authors would like to thank the anonymous reviewers and editors for their comments and suggestions.

Additional information

Funding

This work was supported by the China Postdoctoral Science Foundation [grant number 2016M591651], the Creative Activity Plan for Science and Technology Commission of Shanghai [grant number 13510501600], [grant number 16040501700], the Innovation Foundation of SMU for PhD Graduates [grant number yc2012067]; and the Fostering Foundation for the Excellent Ph.D. Dissertation of SMU [grant number 2013bxlp006].