Transportation resilience, as a component of city sustainability, plays a crucial role in the daily management and emergency response of urban road systems. With coastal cities becoming increasingly vulnerable to typhoons, rainstorms, and other disasters, it is essential to assess the resilience of urban road transportation in a refined and differentiated approach. Existing resilience assessment methods often overlook significant biases, neglecting the dynamic response of road traffic and non-stationary characteristics of traffic systems. To address these limitations, we develop a quantitative resilience assessment method for urban road transportation during rainfall that is based on the improved Resilience Triangle. The method is applied to DiDi urban traffic speed and meteorological data of Shenzhen, China, from April to September 2018, with a focus on Typhoon Mangkhut as an extreme weather case. By analyzing transportation resilience variations across road densities, road hierarchies, and rainfall scenarios, we found that road densities and rainfall intensities explain resilience variations better than road hierarchies. Specifically, as accumulative precipitation exceeds 100 mm, a substantial surge in loss of performance is observed. Typhoon rainfalls result in a greater loss in urban road traffic compared to general rainfalls. The results offer valuable insights for urban road planning, traffic emergency management, and transportation resilience construction in the face of increasingly severe weather challenges.

Intensified tropical cyclones (TCs) threaten the socioeconomic development of coastal cities. The coupling of strong wind and precipitation with the TC process usually amplifies the destructive effects of storms. Currently, an integrated analytical framework for TC hazard assessment at the city level that combines the joint statistical characteristics of multiple TC-induced hazards and local environmental features does not exist. In this study, we developed a novel hazard assessment framework with a high spatiotemporal resolution that includes a fine-tuned K-means algorithm for clustering TC tracks and a Copula model to depict the wind–precipitation joint probability distribution of different TC categories. High-resolution wind and precipitation data were used to conduct an empirical study in Shenzhen, a coastal megacity in Guangdong Province, China. The results show that the probabilities of TC-induced wind speed and precipitation exhibit significant spatial heterogeneity in Shenzhen, which can be explained by the characteristics of TC tracks and terrain environment factors. In general, the hazard intensity of TCs landing from the west side is higher than that from the east side, and the greatest TC intensity appears on the southeast coast of Shenzhen, implying that more disaster prevention efforts are needed. The proposed TC hazard assessment method provides a solid base for highly precise risk assessment at the city level.

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience.

Studying the spatiotemporal patterns of urban road traffic under extreme weather is a key step to building a climate-resilient city. Although existing researches model and simulate traffic states from different perspectives, the traffic forecasting of the urban road networks under extreme weather is seldom addressed. In this paper, a novel Knowledge-driven Attribute-augmented Attention Spatiotemporal Graph Convolutional Network framework is proposed to predict urban road traffic under wind and rain especially in tropical cyclone disasters. Considering the disaster conditions, we model the external dynamic hazard attributes and static environment attributes, and designed an attribute-augmented unit to encode and integrate these factors into the deep learning model. The model is combined with the graph convolutional network (GCN), the gated recurrent unit (GRU), and the attention mechanism. Experiments demonstrate that the predictability of traffic speed can be greatly increased by supplementing the disaster-related factors, the prediction accuracy reaches 0.79. The proposed approach outperforms baselines by 12.16%-31.67% on real-world Shenzhen’s traffic datasets. The model also performs robustly on different road vulnerabilities and hazard intensities. The model errors are mainly occurred in the early peak with extreme wind and rain and the coastal area in the southeast of Shenzhen because of the greater uncertainty. The framework and findings provide a valuable reference for the decision-making of traffic management and control prior to a disaster to alleviate traffic congestion and reduce the negative impact of disasters.