Modelling of emergencies on transportation system in the context of climate change

Current effects of emergencies on human be amplified as extreme weather and outbreaks of epidemic disease increasing. The transport system is crucial for daily life and threatened heavily by these emergencies. Despite advances in emergency management for transportation, we still lack an integrated framework to examine the impact of transport system under different types of extreme event.

First, we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point.

Second, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. This approach is illustrated with a case study of the Chinese highway network. The results show that or different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years.

Finally, we propose an analysis framework combining with Simulation of Urban Mobility (SUMO) simulator to evaluate the impact of the road transport system within an urban agglomeration from the views of structure and function under four types of emergencies: natural disaster, traffic accident, public health event and social security incident. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the regions with the highest degree of openness and the strongest economic vitality in China, with convenient transportation conditions. This method is applied to a case study of the GBA urban agglomeration in China. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. These approaches can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.