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• An adaptive reinforcement learning model is developed to learn human travel behaviors in regular situations. • The model is capable of simulating the traffic conditions and evaluate the perturbations of disasters on urban mobility. • A case study of the 2017 Hurricane Harvey in Houston is conducted to examine the application of the model. • The model and outcomes of applications can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and evaluating human mobility patterns, such as destination and trajectory selection, can inform emerging congestion and road closures raised by disruptions in emergencies. Data related to human movement trajectories are scarce, especially in the context of emergencies, which places a limitation on applications of existing urban mobility models learned from empirical data. Models with the capability of learning the mobility patterns from data generated in normal situations and which can adapt to emergency situations are needed to inform emergency response and urban resilience assessments. To address this gap, this study creates and tests an adaptive reinforcement learning model that can predict the destinations of movements, estimate the trajectory for each origin and destination pair, and examine the impact of perturbations on humans' decisions related to destinations and movement trajectories. Employing millions of trajectory data from INRIX, the application of the proposed model is shown in the context of Houston and the flooding scenario caused by Hurricane Harvey in August 2017. The results show that the model can achieve more than 76% precision and recall at the model learning stage. The mean percentage error of the travel distance in predicted trajectories is 4.29%, compared to the travel distances in empirical data. In addition, predicted density of vehicles in grid cells are negatively associated with the traffic speed on road segments and inundation intensity in grid cells during the flooding. The results from the simulation further show that the model could predict traffic patterns and congestion resulting from urban flooding. The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. [ABSTRACT FROM AUTHOR]

Traffic congestion occurs daily but the transportation network still functions to meet people's travel needs. We propose that a road meets operation requirements as long as the quality of its links (i.e., speed or travel time) satisfies an acceptable threshold. This paper incorporates the link reliability concept into the road travel performance using the link quality threshold to offer new perspectives on network resilience measurement. We introduce two aggregated macroscopic metrics: network reliability scale index, and network stability, based on the link reliability to quantify the road travel performance change in facing external disturbance. We use the temporal traffic-embedded Harris County, TX transportation network during the 2017 Hurricane Harvey as a case study. We show that the proposed metrics can well capture the transportation network performance variation during flooding. Also, we develop an integrated resilience metric that encapsulates the network resistance, recoverability, and rapidity in facing flooding. The results move us closer to better understanding transportation network resilience behavior in different link quality conditions (q). The findings of this research also provide important insights for city planners and traffic operators to examine transportation network resilience through a reliability and stability lens. • Empirical traffic during disaster reveals unique reliability and stability pattern. • Link reliability captures transportation network resilience reduction and recovery. • Distribution of all link's dysfunction time ratio follows a power-law distribution. • Fluctuation of link reliability exceeds the normal range during Hurricane Harvey. • Each city's transportation resilience is differently sensitive to varying link quality thresholds. [ABSTRACT FROM AUTHOR]

This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using static and dynamic metrics. The static metric is the shortest distance from census block groups to the closest centers. Dynamic metrics are: 1) redundancy (daily unique number of centers visited), 2) frequency (daily number of visits to dialysis centers), and 3) proximity (visits weighted by distance to dialysis centers). The results show that: the extent of dependence of regions on dialysis centers varies; flooding significantly reduces access redundancy and frequency of dialysis centers; regions with a greater minority percentage and lower household income were likely to experience extensive disruptions; high-income regions more quickly revert to pre-disaster levels; larger centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities. [ABSTRACT FROM AUTHOR]

The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans • Using high-resolution traffic data to predict near-future road inundation status in urban floods • Adjusting spatio-temporal graph convolutional networks for road network inundation status predictions • Contributing near-future infrastructure failure prediction capability to the smart flood resilience [ABSTRACT FROM AUTHOR]