Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real time location data for calibration, which are becoming much harder to obtain due to the rising privacy concerns. Meanwhile, web search queries of anonymous users have been collected by web companies. Although such data raise less privacy concerns, they have been under-utilized for various applications. In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. More specifically, we utilize a "session-based query encoder" that learns the representations of each user's web search behavior prior to evacuation. Our proposed approach is empirically tested using web search data collected from users affected by a major flood in Japan. Results are validated using location data collected from mobile phones of the same set of users as ground truth. We show that evacuation decisions can be accurately predicted (84%) using only the users' pre-disaster web search data as input. This study proposes an alternative method for evacuation prediction that does not require highly sensitive location data, which can assist local governments to prepare effective first response strategies.Comment: Accepted in ACM KDD 201

APPLICATION OF CELL PHONE STATISTICS FOR ESTIMATING STRANDED PEOPLE BEHAVIOR AFTER SEVERE EARTHQUAKE IN TOKYO

In this paper, we present a walking home simulation as anticipated after a large earthquake, and analyze people’s behaviors, walking and stopping, including the crowding of facilities by those unable to walk all the way home. For creating the necessary data for this simulation, we construct a method to estimate the spatiotemporal distribution of people with detailed individual information such as sex-age classification, and home location, by assembling population distribution data (Mobile Spatial Statistics and Person Trip survey data). The walking home simulation results verified significant variations in the crowding of facilities for stranded people due to differences in the day of the week and the time of the earthquake. Locations in Tokyo with insufficient numbers of facilities for stranded people were identified and some spatiotemporal characteristics of crowding, such as changes in crowding with time elapsed since the earthquake, were described

CLOTHO: a large-scale Internet of Things based crowd evacuation planning system for disaster management

In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this work, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile Cloud computing platform in order to develop the Crowd Lives Oriented Track and Help Optimizition system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the Cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the Artificial Potential Field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. People are usually in panic, which easily causes the chaos of evacuation and brings secondary disasters. Based on APF, we propose an evacuation planning algorithm names as Artificial Potential Field with Relationship Attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced

Mining human mobility patterns from pervasive spatial and temporal data

Recent advances in communication, sensors and processors have made pervasive systems more computationally powerful and increasingly popular. These systems are deployed everywhere all the time while remaining transparent. Take smartphones as an example; they have become an integral part of human life and people carry them wherever they go. Coupled with the popularity of pervasive systems and user tracking, this has opened up excellent opportunities to analyse human mobility. This can be applied to a broad range of location-based services such as smart navigation and recommendation systems. Data from pervasive systems has temporal, spatial and spatio-temporal aspects that can be leveraged for mining human mobility patterns. Temporal data such as time series from embedded sensors on smartphones does not usually have any information about locations, while time stamps are discarded in spatial data. The list of significant locations visited by the user is an example of spatial data. The third group of data is spatio-temporal data that has both temporal and spatial aspects such as users' trajectories. In this dissertation, we analyse human mobility by mining these three kinds of data. In each chapter, we look at a specific aspect to infer key information about users’ mobility including transition time detection, movement graph summarisation, and trajectory prediction. We analyse temporal information from time series data to extract transition times in daily activities. The transition times denote when user activities change such as when the user goes to work or when the user goes shopping. In addition to applications in location-based services, extracting the transition times helps us to understand human mobility patterns across the whole day. We tackle scalability to enable processing to take place on resource-constrained devices. We introduce Shrink as a new summarisation method to compress large scale graphs. Trajectories and movements of the user can be transformed into a graph in which each node represents stay points and each edge represents distance. Since this graph is very large, Shrink is used to reduce the size of the movement graph while preserving distances between nodes. The property that is preserved in the compressed graph, also known as the coarse graph, is the distance between the nodes. Shrink is a query friendly compression, which means the compressed graph can be queried without decompression. As the complexity of distance-based queries such as shortest path queries is highly dependent on the size of the graph, Shrink improves performance in terms of time and storage. We also investigate the effect of compression on the human mobility mining algorithms and show that the summarisation provides a trade-off between efficiency and granularity. We also analyse spatial-temporal data by predicting user trajectory based on historical data. Specifically, given the historical data and the user’s trajectory in the first part of the current day (e.g. trajectory in the morning), we predict how users will complete their trajectory in that particular day (e.g. predicting the trajectory for the rest of the day or the afternoon). The granularity of the predicted trajectory is the same as the granularity of the given trajectories. We emphasize that the predicted trajectory includes the sequence of future locations, the stay times, and the departure times. This enhances the user experience because by having the detailed trajectory in advance, location-based services can notify users about the consequence of the movement. In summary, this thesis contains efficient algorithms that can be applied to diverse aspects of pervasive signals for mining human mobility. The new algorithms are aimed at problems in transition time detection, summarisation, and prediction. The solutions address the scalability issues and can work in big pervasive temporal and spatial data effectively and accurately

Analyzing, Mining, and Predicting Networked Behaviors

Network structure exists in various types of data in the real world, such as online and offline social networks, traffic networks, computer networks, brain networks, and countless other cases where there are relationships between different entities in the data. What are the roles of network structures in these data? First, the network captures inherent characteristics of the data themselves. This is clear from the definition of the network, which represents the relationship between entities: e.g., the social links among people in a social network describe how they interact with each other; a road network summarizes how the roads are laid out geographically; a brain network obtained from fMRI images represents pairs of brain regions that are active at the same time; a computer network constrains the paths via which internet packages and thus information or viruses can spread. Second, the network structures affect the evolution of the data over time. For example, new friendship links in an online social network are frequently created between friends of friends. Similarly, the current road network structure is without a doubt taken into consideration when roads are added or temporarily closed. As we grow, our brains also grow, including the additions of useful links or the clean up of unnecessary links between brain regions. Third, the network structures act as guidance for many different processes happening in the data. For instance, the links between users on social network dictate how gossips can spread; the roads influence how traffic flows in a city; the links between brain regions affects the way we think and how effectively we do things; the connections between computers route the transfer of any information on the internet.In this thesis, I studied the network effect in various networked behaviors, including analyzing such effect, finding its patterns, and predicting future networked behaviors. First, I gained insights into the data by analyzing the accompanied network structures as well as its evolution. Second, I proposed algorithms for mining different network patterns that help summarize the effect of the network structures on different networked behaviors. Finally, I proposed models to predict the evolution of networked behaviors over time. Toward these tasks, I explored a wide variety of network data, including protein-protein interaction networks, online social networks, collaboration networks, chemical compounds, and traffic networks. Overall, I tackled these network data in different aspects and developed a number of methods for effectively mining and forecasting networked behaviors in data