A post-disaster recovery process necessitates significant financial and time investment. Previous studies have found the importance of post-disaster spatial recovery heterogeneity, but the recovery heterogeneity has not been extended to the directed recovery relationships despite the significance of sequential recovery plans. Identifying a causal structure between county-level time series data can reveal spatial relationships in the post-disaster recovery process. This study uses a causal discovery method to reveal the spatiotemporal relationships between counties before, during, and after Hurricane Irma in 2017. This study proposes node aggregation methods at different time scales to obtain internally validated causal links. This paper utilizes points of interest data with daily location information from mobile phones and county-level daily nighttime light data. We find intra-regional homogeneity, inter-regional heterogeneity, and a hierarchical structure among urban, suburban, and rural counties based on a network motif analysis. Subsequently, this article suggests county-level post-disaster sequential recovery plans using the causal graph methods. These results help policymakers develop recovery scenarios and estimate the corresponding spatial recovery impacts.

Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.

Frequent and intensive disasters make the repeated and uncertain post-disaster recovery process. Despite the importance of the successful recovery process, previous simulation studies on the post-disaster recovery process did not explore the sufficient number of household return decision model types, population sizes, and the corresponding critical transition conditions of the system. This paper simulates the recovery process in the agent-based model with multilayer networks to reveal the impact of household return decision model types and population sizes in a toy network. After that, this paper applies the agent-based model to the five selected counties affected by Hurricane Harvey in 2017 to check the urban-rural recovery differences by types of household return decision models. The agent-based model yields three conclusions. First, the threshold model can successfully substitute the binary logit model. Second, high thresholds and less than 1,000 populations perturb the recovery process, yielding critical transitions during the recovery process. Third, this study checks the urban-rural recovery value differences by different decision model types. This study highlights the importance of the threshold models and population sizes to check the critical transitions and urban-rural differences in the recovery process.