An innovative gravity-based approach to assess vulnerability of a Hazmat road transportation network: A case study of Guangzhou, China

The transportation of hazardous material (hereinafter referred to as Hazmat) is different from general cargo. If a Hazmat road transportation accident occurs, it will not only cause significant casualties and road network damage but also threaten the safety of the lives and property of the residents near the road. Therefore, a quantitative vulnerability analysis of the Hazmat road transportation network is developed to assess the vulnerability of each link, which can help improve the road transportation risk management level. First, the authors propose an indicator to measure the relevance between two links based on comprehensive analysis of the road transportation network topology and Hazmat road transportation risk characteristics. Second, the authors discover that they cannot identify road vulnerability using only topology and risk characteristics alone. Thus, an Impact Strength model is developed to assess Hazmat road transportation vulnerability. This model is based on the classical Gravity model and considers both topology and risk characteristics. Relevant algorithms are proposed accordingly. Third, the authors use Guangzhou’s Hazmat highway transportation as a case study to verify their Impact Strength model. The related statistics data are collected, and ArcGIS software is employed. By using this model, the authors can calculate the Impact Strength of each link in the whole transportation network. The empirical results verify that this innovative Impact Strength model can help to identify the links with significant vulnerabilities, which can thus help to reduce road transportation risk in advance and build the road transportation risk early-warning mechanism. This innovative model can also be used to support the relevant decision-making process for further analysis.


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  • Accession Number: 01678175
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 25 2018 3:25PM