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The resilience of internet service is crucial for ensuring consistent communication, situational awareness, facilitating emergency response in our digitally-dependent society. However, due to empirical data constraints, there has been limited research on internet service disruptions during extreme weather events. To bridge this gap, this study utilizes observational datasets on internet performance to quantitatively assess the extent of internet disruption during two recent extreme weather events. Taking Harris County in the United States as the study region, we jointly analyzed the hazard severity and the associated internet disruptions in the context of two extreme weather events. The results show that the hazard events significantly impacted regional internet connectivity. There exists a pronounced temporal synchronicity between the magnitude of disruption and hazard severity: as the severity of hazards intensifies, internet disruptions correspondingly escalate, and eventually return to baseline levels post-event. The spatial analyses show that internet service disruptions can happen even in areas that are not directly impacted by hazards, demonstrating that the repercussions of hazards extend beyond the immediate area of impact. This interplay of temporal synchronization and spatial variance underscores the complex relationships between hazard severity and Internet disruption. Furthermore, the socio-demographic analysis suggests that vulnerable communities, already grappling with myriad challenges, face exacerbated service disruptions during these hazard events, emphasizing the need for prioritized disaster mitigation strategies and interventions for improving the resilience of internet services. To the best of our knowledge, this research is among the first studies to examine the Internet disruptions during hazardous events using a quantitative observational dataset. The insights obtained hold significant implications for city administrators, guiding them towards more resilient and equitable infrastructure planning. [ABSTRACT FROM AUTHOR]

Lifestyle recovery captures the collective effects of population activities as well as the restoration of infrastructure and business services. This study uses a novel approach to leverage privacy-enhanced location intelligence data, which is anonymized and aggregated, to characterize distinctive lifestyle patterns and to unveil recovery trajectories after 2017 Hurricane Harvey in Harris County, Texas (USA). The analysis integrates multiple data sources to record the number of visits from home census block groups (CBGs) to different points of interest (POIs) in the county during the baseline and disaster periods. For the methodology, the research utilizes unsupervised machine learning and ANOVA statistical testing to characterize the recovery of lifestyles using privacy-enhanced location intelligence data. First, primary clustering using k-means characterized four distinct essential and non-essential lifestyle patterns. For each primary lifestyle cluster, the secondary clustering characterized the impact of the hurricane into four possible recovery trajectories based on the severity of maximum disruption and duration of recovery. The findings further reveal multiple recovery trajectories and durations within each lifestyle cluster, which imply differential recovery rates among similar lifestyles and different demographic groups. The impact of flooding on lifestyle recovery extends beyond the flooded regions, as 59% of CBGs with extreme recovery durations did not have at least 1% of direct flooding impacts. The findings offer a twofold theoretical significance: (1) lifestyle recovery is a critical milestone that needs to be examined, quantified, and monitored in the aftermath of disasters; (2) spatial structures of cities formed by human mobility and distribution of facilities extend the spatial reach of flood impacts on population lifestyles. These provide novel data-driven insights for public officials and emergency managers to examine, measure, and monitor a critical milestone in community recovery trajectory based on the return of lifestyles to normalcy. [ABSTRACT FROM AUTHOR]

Community recovery from hazards occurs through various diffusion processes within social and spatial networks of communities. Existing knowledge regarding the diffusion of recovery in community socio-spatial networks, however, is rather limited. To bridge this gap, we created a network diffusion model to characterize the unfolding of population activity recovery in spatial networks of communities. In particular, this study aims to answer the research question "To what extent can the diffusion model capture the spatial patterns of recovery?" Using population activity recovery data derived from location-based information associated with 2017 Hurricane Harvey in the Houston area, we parameterized the threshold-based network diffusion model using the genetic algorithm and then simulated the recovery diffusion process. The results show that the spatial effects of recovery are rather heterogeneous across different areas; some spatial areas demonstrate a greater spatial effect in their recovery. Also, the results show that low-income and minority areas are community recovery multipliers; with faster recovery in these areas corresponding to accelerated recovery for the entire community. Hence, prioritizing these areas in resource allocation during recovery has the potential to accelerate could expedite the recovery of the entire community's recovery process while promoting recovery equality and equity. [ABSTRACT FROM AUTHOR]

In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resolution aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topological network properties indicate that the network has recovered. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities. [ABSTRACT FROM AUTHOR]

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood. [ABSTRACT FROM AUTHOR]

Infrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi‐agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane‐induced power outages, the model consists of three elements: (1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; (2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and (3) the households component captures the dynamic processes related to preparation, information‐seeking, and response actions of households facing hurricane‐induced power outages. We used empirical data from household surveys from three hurricanes (Harvey, Florence, and Michael) in conjunction with theoretical decision‐making models to abstract and simulate the underlying mechanisms affecting the experienced hardship of households when facing power outages. The multi‐agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine the effects of different factors—such as forewarning durations, social network types, and restoration and resource allocation strategies—on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high‐intensity events, to enhance equitable resilience. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of households, hazards, and infrastructure systems to better integrate human‐centric aspects in resilience planning and assessment of infrastructure systems in disasters. Hence, the proposed model and its results could provide a new tool for infrastructure managers and operators, as well as for disaster managers, in devising hazard mitigation and response strategies to reduce the societal impacts of power outages in an equitable manner. [ABSTRACT FROM AUTHOR]

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner. [ABSTRACT FROM AUTHOR]

The objective of this article is to systematically assess and identify factors affecting risk disparity due to infrastructure service disruptions in extreme weather events. We propose a household service gap model that characterizes societal risks at the household level by examining service disruptions as threats, level of tolerance of households to disruptions as susceptibility, and experienced hardship as an indicator for the realized impacts of risk. The concept of "zone of tolerance" for the service disruptions was encapsulated to account for different capabilities of the households to endure the adverse impacts. The model was tested and validated in the context of power outages through survey data from the residents of Harris County in the aftermath of Hurricane Harvey in 2017. The results show that households' need for utility service, preparedness level, the existence of substitutes, possession of social capital, previous experience with disasters, and risk communication affect the zone of tolerance within which households cope with service outages. In addition, sociodemographic characteristics, such as race and residence type, are shown to influence the zone of tolerance, and hence the level of hardship experienced by the affected households. The results reveal that population subgroups show variations in the tolerance level of service disruptions. The findings highlight the importance of integrating social dimensions into the resilience planning of infrastructure systems. The proposed model and results enable human‐centric hazards mitigation and resilience planning to effectively reduce the risk disparity of vulnerable populations to service disruptions in disasters. [ABSTRACT FROM AUTHOR]

• 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]