Jaziar Radianti

Crowd Models for Emergency Evacuation: A Review Targeting Human-Centered Sensing

Emergency evacuation of crowds is a fascinating phenomenon that has attracted researchers from various fields. Better understanding of this class of crowd behavior opens up for improving evacuation policies and smarter design of buildings, increasing safety. Recently, a new class of disruptive technology has appeared: Human-centered sensing which allows crowd behavior to be monitored in real-time, and provides the basis for real-time crowd control. The question then becomes: to what degree can previous crowd models incorporate this development, and what areas need further research? In this paper, we provide a survey that describes some widely used crowd models and discuss their advantages and shortages from the angle of human-centered sensing. Our review reveals important research opportunities that may contribute to an improved and more robust emergency management.

A Study of a Social Behavior inside the Online Black Markets

Illegal activities in cyberspace involving software vulnerabilities have resulted in tangible damage on computer-based environments. Lately, online black market sites for trading stolen goods, credentials, malware and exploit kits have been intensively examined. The market players are identifiably a group of loosely tied individuals but posses shared interests. However, their social behavior has only been discussed in a limited manner. This paper examines the arrangement of the market insiders’ social behavior that enables such forums to continue or discontinue their operation and become a meaningful threat to security. The results reveal that particular formal and informal regulations and procedures, forum lifecycle and common norms of black market participants contributing to keeping the markets running.

Vulnerability Black Markets: Empirical Evidence and Scenario Simulation

This paper discusses the manifest characteristics of online Vulnerability Black Markets (VBM), insider actors, interactions and mechanisms, obtained from masked observation. Because VBM transactions are hidden from general view, we trace their precursors as secondary evidence of their development and activity. More general attributes of VBMs and the exploits they discuss are identified. Finally, we introduce a simulation model that captures how vulnerability discoveries may be placed in a dual legal-black market context. We perform simulations and find that if legal markets expose vulnerabilities that go unresolved, the security and quality of software may suffer more than in the absence of a legal market. Thus the problem scope expands beyond vulnerability trading to one that requires active participation and reaction by software vendors.

Emergent vulnerabilities in Integrated Operations: A proactive simulation study of economic risk☆

Abstract The protection of critical infrastructure requires an understanding of the effects of change on current and future safety and operations. Vulnerabilities may emerge during the rollout of updated techniques and integration of new technology with existing work practices. Managers need to understand how their decisions, often focused on economic priorities, affect the dynamics of vulnerability over time. Such understanding is difficult to obtain, as the historical data typically used for decision support, prediction and forecasting may not be available. We report on the use of group model building and simulation to consider proactively the effects of a 10-year, multi-billion dollar modernization of offshore oil operations. A system dynamics model defines the problem in terms of the structure of work process and knowledge transitions. The model is parameterized with data from workshops conducted with groups of experts in oil production. The simulation suggests that vulnerability increases from the alterations to work process and knowledge associated with change, and only resolves as these alterations are absorbed into normal activities and experience. The forces that generate vulnerability compound in unanticipated ways, leading to possible exponential growth in the cost of incidents. The numerical results, while based on theory and assumptions made in the absence of historical data, indicate that accelerating the IO transition may increase incident costs sufficiently to put the program at risk. The contribution of this paper is three-fold: We describe how group model building and simulation techniques establish a problem domain and focus in the critical infrastructure arena where traditional data-driven methods are not available. The model quantifies the effects of operational and behavioral variables surrounding the effects of technology change, including areas that are difficult to measure directly, such as knowledge and vulnerability. Finally, the ability to consider a wide range of effects and outcomes in the absence of historical data provides a starting point for managerial ideation about the need for vulnerability mitigation.

Emergent Vulnerability in Integrated Operations: A Proactive Simulation Study of Risk and Organizational Learning

The implementation of integrated operations (IO) for oil and gas recovery - a real-time linkage among platform-based facilities, on-shore control centers and suppliers - is anticipated to reduce operating costs by 30%, extend the lifetime of current production fields by five years or longer and maintain Norwegian continental shelf production for 50-100 years. The changes in operating procedures require extensive training to ensure continued personal and environmental safety. Vulnerabilities may emerge during the rollout of updated techniques and integration of IO technology with existing work practices. We focus on user knowledge as key to successful change. A system dynamics simulation is presented that defines work process and knowledge transition. Interviews and historical records assisted in parameterizing the model. The simulation suggests that great care should be taken to facilitate and monitor the rate of knowledge maturation, even in the face of expensive implementation delays, to reduce the risk of catastrophic failure from endemic incidents

Escape planning in realistic fire scenarios with Ant Colony Optimisation

An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when predefined escape routes are blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape routes in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected person — considering dynamic spread of fires, movability impairments caused by the hazards and faulty unreliable data. Special focus in this paper is on empirical tests for the proposed algorithms. This paper brings together the Ant Colony approach with a realistic fire dynamics simulator, and shows that the proposed solution is not only able to outperform comparable alternatives in static and dynamic environments, but also in environments with realistic spreading of fire and smoke causing fatalities. The aim of the solutions is usage by both individuals, such as from a personal smartphone of one of the evacuees, or for emergency personnel trying to assist large groups from remote locations.

Ant colony optimisation for planning safe escape routes

An emergency requiring evacuation is a chaotic event filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when a predefined escape route is blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape route in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected person -- considering both dynamic spread of hazards and congestion avoidance. The solution can be used both on an individual bases, such as from a personal smart phone of one of the evacuees, or from a remote location by emergency personnel trying to assist large groups.

A spatio-temporal probabilistic model of hazard- and crowd dynamics for evacuation planning in disasters

Managing the uncertainties that arise in disasters – such as a ship or building fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior, hazard dynamics, or targeting fully known environments. However, when a disaster strikes, uncertainties about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowds and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd and hazard dynamics, using ship- and building fire as proof-of-concept scenarios. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior, being based on studies of physical fire models, crowd psychology models, and corresponding flow models. Simulation results demonstrate that the DBN model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Our scheme thus opens up for novel in situ threat mapping and evacuation planning under uncertainty, with applications to emergency response.

Publish-subscribe smartphone sensing platform for the acute phase of a disaster: A framework for emergency management support

The advanced sensors embedded in modern smartphones opens up for novel research opportunities, as for instance manifested in the field of mobile phone sensing. Most notable is perhaps research activities within human activity recognition and context-aware applications. Along a similar vein, the SmartRescue project targets monitoring of both hazard developments as well as tracking of people in a disaster, taking advantage of smartphone sensing, processing and communication capabilities. The goal is to help crisis managers and the public in early hazard detection, hazard prediction, and in the forming of risk minimizing evacuation plans when disaster strikes. In this paper we propose a novel smartphone based communication framework for disaster specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a robust content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. The proposed communication platform has been tested at the proof of concept level, with several detailed features providing promising results. We also provide the initial results from the development of this platform and discuss how to enhance the platform to become a disaster monitoring system for practical use.

An Overview of Public Concerns During the Recovery Period after a Major Earthquake: Nepal Twitter Analysis

In responding to disasters, Twitter is extensively used, both for information exchange and mapping the crisis, among citizens, and in relation to national and international humanitarian responders. This paper reports Twitter analysis aimed at identifying the most pressing issues that arose in the short term recovery phase starting about a week after the Nepal earthquake, including the heretofore neglected topic of mismatch between international relief and local cultures. Based on Twitter data collected between April 30th and May 6th 2015. 1,074,864 raw messages apparently related to the Nepal earthquake were retrieved, filtered and analyzed. This exploratory adapts established frameworks for use in the dataset. The results show that our framework can identify several unique problems, including that disaster relief efforts can trigger negative sentiments when they are conducted without understanding of the local cultures.