Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and Covid-19

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana. Using the Application Programming Interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Laura. Online social networks were based on user influence feature ( mentions or tags) that allows notifying other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into twenty-one components of various sizes, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those who involved heavily and interacted closely with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters

Predicting Hurricane Evacuation Behavior Synthesizing Data from Travel Surveys and Social Media

Evacuation behavior models estimated using post-disaster surveys are not adequate to predict real-time dynamic population response as a hurricane unfolds. With the emergence of ubiquitous technology and devices in recent times, social media data with its higher spatio-temporal coverage has become a potential alternative for understanding evacuation behaviour during hurricanes. However, these data are often associated with selection bias and population representativeness issues. To that extent, the current study proposes a novel data fusion algorithm to combine heterogeneous data sources from transportation systems and social media, in a unified framework to understand and predict real-time population response during hurricanes. Specifically, Twitter data of 2300 users are collected for evacuation response during Hurricane Irma and augmented behaviourally (probabilistically) with a representative National Household Travel Survey (NHTS) data, thus creating a hybrid dataset to improve the representativeness as well as provide a rich set of explanatory variables for understanding the evacuation behavior. The fusion process is conducted using a probabilistic matching method based on a set of common attributes across NHTS and Twitter. The fused dataset is employed to estimate the evacuation model and a comparison exercise is conducted to evaluate the performance of the model via fusion. The model fitness measures clearly demonstrate the improvement in data fit for the evacuation model through the proposed fusion algorithm. Further, we conduct a prediction assessment to illustrate the applicability of the proposed fusion technique and the results clearly highlight the improvement in the evacuation prediction rate achieved through the fused models. The proposed data-driven methods will enhance our ability to predict time-dependent evacuation demand for better hurricane response operations such as targeted warning dissemination and improved evacuation traffic management, allowing emergency plans to be more adaptive

Understanding the Loss in Community Resilience due to Hurricanes using Facebook Data

Significant negative impacts are observed in productivity, economy, and social wellbeing because of the reduced human activity due to extreme events. Community resilience is an important and widely used concept to understand the impacts of an extreme event to population activity. Resilience is generally defined as the ability of a system to manage shocks and return to a steady state in response to an extreme event. In this study, aggregate location data from Facebook in response to Hurricane Ida are analyzed. Using changes in the number of Facebook users before, during, and after the disaster, community resilience is quantified as a function of the magnitude of impact and the time to recover from the extreme situation. Based on the resilience function, the transient loss of resilience in population activity is measured for the affected communities in Louisiana. The loss in resilience of the affected communities are explained by three types of factors, including disruption in physical infrastructures, disaster conditions due to hurricanes, and socio-economic characteristics. A greater loss in community resilience is associated with factors such as disruptions in power and transportation services and disaster conditions. Socioeconomic disparities in loss of resilience are found with respect to median income of a community. Understanding community resilience using decreased population activity levels due to a disaster and the factors associated with losses in resilience will enable us improve hazard preparedness, enhance disaster management practices, and create better recovery policies towards strengthening infrastructure and community resilience

Quantifying human mobility resilience to extreme events using geo-located social media data

Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation’s overall disaster resilience strategies

Applications of Deep Learning Models for Traffic Prediction Problems

Deep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first problem, we present a modeling framework using deep LSTM NN model to predict traffic speeds in freeways during regular traffic condition as well as under extreme traffic demand, such as a hurricane evacuation. The approach is tested using real-world traffic data collected during hurricane Irma\u27s evacuation for the interstate 75 (I-75), a major evacuation route in Florida. We perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as K-Nearest Neighbor, Analytic Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA). We find that LSTM-NN performs better than these parametric and non-parametric models. Apart from the improvement in traffic operation, the proposed method can be integrated with evacuation traffic management systems for a better evacuation operation. In our second problem, we develop a data-driven real-time queue length prediction technique using deep LSTM NN model. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync Adaptive Traffic Control System (ATCS) data to train a Long Short-Term Memory Neural Network model capturing time-dependent patterns of a queue of a signal. To select the best combination of hyperparameters, we use sequential model-based optimization (SMBO) technique. Our experiment results show that the proposed modeling framework performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well. Queue length prediction is a crucial part of an ATCS to optimize control parameters and this method can improve the existing signal optimization technique for ATCS

A pandemia por covid-19 e seus impactos na mobilidade urbana: um estudo de caso utilizando análise estatística espacial

Due to the rapid advance of the disease by Covid-19 and its spread on a global level, the new coronavirus has significantly impacted people's daily activities and created an unprecedented scenario, since several measures were implemented as a way to reduce contagion and spread Covid-19 disease. Thus, a theoretical deepening on the social variables that can influence the spread of the disease, is important for the control measures to Covid-19 to be effective both in the present moment and in the future. Therefore, the work aims to assess the impact of travel patterns, land use and socioeconomic aspects on the spatial distribution of Covid-19 cases. The methodology consists of modeling using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) methods. A case study was carried out in São João del Rei, a medium-sized municipality in the state of Minas Gerais. Initially, a descriptive and spatial study was developed in the cities of Itajubá and São João del Rei, both medium-sized cities located in Minas Gerais, as a way to assess how the pandemic by Covid-19 impacted the travel behavior, and daily activities of its inhabitants, such as also the impact on public and road safety. For this, data from a survey on the daily activities of the population before and during the pandemic applied in 2020 in both cities were used, data from traffic accidents and assaults on public roads collected from the Military Police of each city. It was possible to identify that there are associations between socio-demographic variables and the place where the main activity was carried out during the pandemic by Covid -19 of the respondents through the application of Pearson's chi-square test. Through an exploratory analysis it was identified in both cities a percentage reduction in the use of buses as a means of transport, an increase in short trips of up to 10 minutes, as well as a reduction in the frequency of accidents and robberies on public roads during the analysis period, for both cities. In the city of São João del Rei, the sample with the data showed a better spatial distribution and information on the number of Covid-19 cases was available to the entire population, enabling a more in-depth study with statistical analysis, using the OLS and GWR methods with the variables determined for the city of São João del Rei. The results show a strong association between the number of cases of Covid-19 and several variables of the travel behavior, socioeconomic and land use. The GWR approach proved to be an important tool to explain the spatial distribution of Covid-19 cases in the municipality, showing in most cases a better fit than the OLS method. The study on the association between social variables and the spread of disease was important and remains necessary. The result serves as a subsidy to the planning of urban mobility with measures aimed at health security and service to the population during and after this period of crisis, and thus make more efficient use of public resources with a view to sustainable development.Agência 1Devido ao rápido avanço da doença por Covid-19 e sua disseminação em nível global, o novo coronavírus impactou expressivamente as atividades diárias das pessoas e criou um cenário sem precedentes, uma vez que, várias medidas foram implantadas como forma de reduzir o contágio e disseminação da doença pela Covid-19. Deste modo, um aprofundamento teórico sobre as variáveis sociais que podem influenciar na disseminação da doença, é importante para que as medidas de controle à Covid-19 sejam efetivas tanto no momento atual, quanto no futuro. Portanto, o trabalho tem como objetivo avaliar o impacto do padrão de viagens, aspectos de uso do solo e socioeconômicos na distribuição espacial dos casos de Covid-19. A metodologia é composta por modelagem com a utilização dos métodos de Ordinary Least Squares (OLS) e Geographically Weighted Regression (GWR). Foi realizado um estudo de caso em São João del Rei, um município de médio porte no estado de Minas Gerais. Inicialmente foi desenvolvido um estudo descritivo e espacial nas cidades de Itajubá e São João del Rei, ambas cidades de médio porte localizadas em Minas Gerais, como forma de avaliar como a pandemia por Covid-19 impactou nos deslocamentos e atividades diárias de seus habitantes, como também o impacto gerado na segurança pública e viária. Para isso foram utilizados dados de uma pesquisa sobre as atividades diárias da população antes e durante a pandemia aplicada em 2020 nas duas cidades, dados de acidentes de trânsito e assaltos em vias públicas coletados junto às Polícias Militares de cada cidade. Foi possível identificar que existem associações entre as variáveis sócio demográficas e o local de realização da atividade principal durante a pandemia por Covid -19 dos respondentes através da aplicação do teste Qui-quadrado de Pearson. Por meio de uma análise exploratória identificou-se nas duas cidades uma redução percentual da utilização do ônibus como meio de transporte, um aumento dos deslocamentos de curta duração de até 10 minutos, como também uma redução das frequências de acidentes e assaltos em vias públicas durante o período de análise, para as duas cidades. Na cidade de São João del Rei, a amostra com os dados apresentou melhor distribuição espacial e as informações sobre o número de casos de Covid-19 estavam disponibilizadas à toda população, possibilitando um estudo mais aprofundado com análise estatística, utilizando os métodos OLS e GWR com as variáveis determinadas para a cidade de São João del Rei. Os resultados mostram uma forte associação entre o número de casos de Covid-19 e diversas variáveis do padrão de deslocamento, socioeconômicas e uso do solo. A abordagem GWR mostrou-se uma ferramenta importante para explicar a distribuição espacial dos casos de Covid-19 no município, apresentando na maioria dos casos um melhor ajuste do que o método OLS. O estudo sobre a associação entre variáveis sociais e a propagação de doenças foi importante e continua sendo necessário. O resultado serve como subsídio ao planejamento da mobilidade urbana com medidas voltadas à segurança sanitária e atendimento da população durante e após esse período de crise, e assim fazer uso mais eficiente dos recursos públicos com vista ao desenvolvimento sustentável

Understanding the Socio-infrastructure Systems During Disaster from Social Media Data

Our socio-infrastructure systems are becoming more and more vulnerable due to the increased severity and frequency of extreme events every year. Effective disaster management can minimize the damaging impacts of a disaster to a large extent. The ubiquitous use of social media platforms in GPS enabled smartphones offers a unique opportunity to observe, model, and predict human behavior during a disaster. This dissertation explores the opportunity of using social media data and different modeling techniques towards understanding and managing disaster more dynamically. In this dissertation, we focus on four objectives. First, we develop a method to infer individual evacuation behaviors (e.g., evacuation decision, timing, destination) from social media data. We develop an input output hidden Markov model to infer evacuation decisions from user tweets. Our findings show that using geo-tagged posts and text data, a hidden Markov model can be developed to capture the dynamics of hurricane evacuation decision. Second, we develop evacuation demand prediction model using social media and traffic data. We find that trained from social media and traffic data, a deep learning model can predict well evacuation traffic demand up to 24 hours ahead. Third, we present a multi-label classification approach to identify the co-occurrence of multiple types of infrastructure disruptions considering the sentiment towards a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). We validate our approach for data collected during multiple hurricanes. Fourth, finally we develop an agent-based model to understand the influence of multiple information sources on risk perception dynamics and evacuation decisions. In this study, we explore the effects of socio-demographic factors and information sources such as social connectivity, neighborhood observation, and weather information and its credibility in forming risk perception dynamics and evacuation decisions