Smartphone sensing platform for emergency management

The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The SmartRescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.Comment: 11th International Conference on Information Systems for Crisis Response and Management ISCRAM2014 (2014

A Spatial Agent-based Model for Volcanic Evacuation of Mt. Merapi

Natural disasters, especially volcanic eruptions, are hazardous events that frequently happen in Indonesia. As a country within the “Ring of Fire”, Indonesia has hundreds of volcanoes and Mount Merapi is the most active. Historical studies of this volcano have revealed that there is potential for a major eruption in the future. Therefore, long-term disaster management is needed. To support the disaster management, physical and socially-based research has been carried out, but there is still a gap in the development of evacuation models. This modelling is necessary to evaluate the possibility of unexpected problems in the evacuation process since the hazard occurrences and the population behaviour are uncertain. The aim of this research was to develop an agent-based model (ABM) of volcanic evacuation to improve the effectiveness of evacuation management in Merapi. Besides the potential use of the results locally in Merapi, the development process of this evacuation model contributes by advancing the knowledge of ABM development for large-scale evacuation simulation in other contexts. Its novelty lies in (1) integrating a hazard model derived from historical records of the spatial impact of eruptions, (2) formulating and validating an individual evacuation decision model in ABM based on various interrelated factors revealed from literature reviews and surveys that enable the modelling of reluctant people, (3) formulating the integration of multi-criteria evaluation (MCE) in ABM to model a spatio-temporal dynamic model of risk (STDMR) that enables representation of the changing of risk as a consequence of changing hazard level, hazard extent and movement of people, and (4) formulating an evacuation staging method based on MCE using geographic and demographic criteria. The volcanic evacuation model represents the relationships between physical and human agents, consisting of the volcano, stakeholders, the population at risk and the environment. The experimentation of several evacuation scenarios in Merapi using the developed ABM of evacuation shows that simultaneous strategy is superior in reducing the risk, but the staged scenario is the most effective in minimising the potential of road traffic problems during evacuation events in Merapi. Staged evacuation can be a good option when there is enough time to evacuate. However, if the evacuation time is limited, the simultaneous strategy is better to be implemented. Appropriate traffic management should be prepared to avoid traffic problems when the second option is chosen

Examining ant colony optimization performance for ship evacuation

Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2013 – Universitetet i Agder, GrimstadEvacuating passengers during a fire on board ships is a difficult task and any improvements on current procedures can help save lives. This report describes how an Ant Colony Optimization (ACO) pathfinding algorithm could possibly be used to lead passengers out of this dangerous situation. The ships were modeled from blueprints of real ships and represented as graphs with nodes and vertices. In the simulation passengers were equipped with a smart phone running an application which showed them the way out. The passengers could end up panicking given close proximity to fire or other stressing factors, in which case they would stop following directions. Additionally, high density of passengers in rooms and corridors slowed down the speed of evacuation. The results produced by ACO were compared to Dijkstra’s pathfinding algorithm and were promising. They showed that ACO performed well in dynamic environments and could be used in a crisis situation to guide people out of danger

An attraction-based cellular automaton model for generating spatiotemporal population maps in urban areas

We develop a cellular automaton (CA) model to produce spatiotemporal population maps that estimate population distributions in an urban area during a random working day. The resulting population maps are at 50 m and 5 minutes spatiotemporal resolution, showing clearly how the distribution of population varies throughout a 24-hour period. The maps indicate that some areas of the city, which are sparsely populated during the night, can be densely populated during the day. The developed CA model assumes that the population transition trends follow dynamics and propagation patterns similar to a contagious disease. Thus, our model designed to change the states of each grid cell (stable or dynamic) in a way that is similar to changes in the condition of individuals who are exposed to an infectious disease (susceptible or infected). In addition, the modeling space is informed by several geographic features, such as the transport routes, land-use categories, and population attraction points. The model is geosimulated for the city of Trondheim in Norway, where the synthetic day population could be validated using an estimated day-population map based on the registered workplace addresses and employee statistics. The generated maps can be used to estimate a value for the population-at-risk in the wake of a major disaster that occurs in an urban area at any time of a day. In addition to assessing exposure to hazards, the resulting maps also reveal movement patterns, transition trends, peak hours, and activity levels. Possible applications range from public safety, disaster management, transport modeling, and urban growth studies to strategic energy distribution planning.acceptedVersio

Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents

Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation duration were collected as the input variables for the classification and regression tree (CART) model. Based on this model, the classification result explicitly pointed out that the exit searching efficiency was evolving. By ignoring the last three unimportant factors from the Analytic Hierarchy Process (AHP), the ultimate Dynamic Bayesian Network (DBN) was built with the temporal part of the CART output and the time-independent part of the vehicle characteristics. Simulation showed that the most efficient exit searching period is the middle escape stage, which is 10 seconds after the emergency signal is triggered, and the escape probability clearly increases with the efficient exit searching. Furthermore, receiving emergency escape training contributes to a significant escape probability improvement of more than 10%. Compared with different failure modes, the emergency hammer layout and door reliability have a more significant influence on the escape probability improvement than aisle condition. Based on the simulation results, the escape probability will significantly drop below 0.55 if the emergency hammers, door, and aisle are all in a failure state

A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target

Crowds can be subject to intrinsic and extrinsic sources of risk, and previous records have shown that, in the absence of adequate safety measures, these sources of risk can jeopardise human lives. To mitigate these risks, we propose that implementation of multiple layers of safety measures for crowds—what we label The Swiss Cheese Model of Crowd Safety—should become the norm for crowd safety practice. Such system incorporates a multitude of safety protection layers including regulations and policymaking, planning and risk assessment, operational control, community preparedness, and incident response. The underlying premise of such model is that when one (or multiple) layer(s) of safety protection fail(s), the other layer(s) can still prevent an accident. In practice, such model requires a more effective implementation of technology, which can enable provision of real-time data, improved communication and coordination, and efficient incident response. Moreover, implementation of this model necessitates more attention to the overlooked role of public education, awareness raising, and promoting crowd safety culture at broad community levels, as one of last lines of defence against catastrophic outcomes for crowds. Widespread safety culture and awareness has the potential to empower individuals with the knowledge and skills that can prevent such outcomes or mitigate their impacts, when all other (exogenous) layers of protection (such as planning and operational control) fail. This requires safety campaigns and development of widespread educational programs. We conclude that, there is no panacea solution to the crowd safety problem, but a holistic multi-layered safety system that utilises active participation of all potential stakeholders can significantly reduce the likelihood of disastrous accidents. At a global level, we need to target a Vision Zero of Crowd Safety, i.e., set a global initiative of bringing deaths and severe injuries in crowded spaces to zero by a set year

Tsunami evacuation model for Sumner, Christchurch, New Zealand

Sumner, a coastal suburb located to the south-east of Christchurch, New Zealand, is highly exposed to a number of tsunami hazards. In tsunami mitigation plans, evacuation plays a crucial role in saving human lives, especially for communities located in low-lying coastal areas. The aim of this thesis is to enhance the methodological basis for development of tsunami evacuation plans in Sumner. To achieve this, a numerical simulation output of far-field tsunami impacts in Sumner was used to establish the maximum likely inundation extent and flow depth. This, together with population census data and daily activity patterns specified for the study area, established the spatio-temporal basis for characterising population exposure to the tsunamic hazard. A geospatial evacuation analysis method (Least Cost Path Distance), augmented with variable population exposure and distributed travel speeds, was used to characterise spatial variation in evacuation times and the corresponding numbers of evacuees and vehicles. Three ‘extreme’ end-member scenarios were utilised to address possible evacuation methods; all pedestrians evacuated to 20 metres elevation, all pedestrians to bus stops for evacuation using public transport, and all people evacuated using private vehicles. This thesis has made a methodological contribution to tsunami evacuation simulation by characterising variable spatio-temporal population exposure, and incorporating terrain properties into population and vehicle movements. The methods are equally applicable to other locations, to other hazards, and for both pre- and post-disaster evacuation analyses

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

Can the Quality of the Potential Flood Risk Maps be Evaluated? A Case Study of the Social Risks of Floods in Central Spain

Calibration and validation of flood risk maps at a national or a supra-national level remains a problematic aspect due to the limited information available to carry out these tasks. However, this validation is essential to define the representativeness of the results and for end users to gain confidence in them. In recent years, the use of information derived from social networks is becoming generalized in the field of natural risks as a means of validating results. However, the use of data from social networks also has its drawbacks, such as the biases associated with age and gender and their spatial distribution. The use of information associated with phone calls to Emergency Services (112) can resolve these deficiencies, although other problems are still latent. For example, a bias does exist in the relationship between the size of the population and the number of calls to the Emergency Services. This last aspect determines that global regression models have not been effective in simulating the behavior of related variables (calls to Emergency Services–Potential Flood Risk). Faced with this situation, the use of local regression models (such as locally estimated scatterplot smoothing (LOESS)) showed satisfactory results in the calibration of potential flood risk levels in the Autonomous Community of Castilla-La Mancha (Spain). This provides a new methodological path to the calibration studies of flood risk cartographies at national and supra-national levels. The results obtained through LOESS local regression models allowed us to establish the correct relationship between categorized potential risk levels and the inferred potential risk. They also permitted us to define the cases in which said levels differed ostensibly and where potential risk due to floods assigned to those municipalities led to a lower level of confidence. Therefore, based on the number of calls to the Emergency Service, we can categorize those municipalities that should be the subject of a more detailed study and those whose classification should be revised in future updates