Parvaneh Sarshar

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.

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.

A Bayesian network model for evacuation time analysis during a ship fire

We present an evacuation model for ships while a fire happens onboard. The model is designed by utilizing Bayesian networks (BN) and then simulated in GeNIe software. In our proposed model, the most important factors that have significant influence on a rescue process and evacuation time are identified and analyzed. By applying the probability distribution of the considered factors collected from the literature including IMO, real empirical data and practical experiences, the trend of the rescue process and evacuation time can be evaluated and predicted using the proposed model. The results of this paper help understanding about possible consequences of influential factors on the security of the ship and help to avoid exceeding evacuation time during a ship fire.

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

Managing the uncertainties that arise in disasters - such as ship fire - can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd 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 with hazard dynamics, using a ship fire as a proof-of-concept scenario. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior - both descriptive and normative (optimal). Descriptive modeling is based on studies of physical fire models, crowd psychology models, and corresponding flow models, while we identify optimal behavior using Ant-Based Colony Optimization (ACO). Simulation results demonstrate that the DNB model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Furthermore, the ACO provides safe paths, dynamically responding to current threats.

On the Usability of Smartphone Apps in Emergencies

It is very critical that the disaster management smartphone app users be able to interact efficiently and effectively with the app during an emergen-cy. An overview of the challenges face for designing mobile HCI in emergency management tools is presented in this paper. Then, two recently developed emergency management tools, titled GDACSmobile and SmartRescue, are studied from usability and HCI challenges point of view. These two tools use mobile app and smartphone sensors as the main functionality respectively. Both have a smartphone app and a web-based app with different UIs for their different user groups. Furthermore, the functionality of these apps in the format of a designed scenario, fire onboard a passenger ship, will be discussed.

Comparing Different Crowd Emergency Evacuation Models Based on Human Centered Sensing Criteria

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, the authors provide a survey that describes some widely used crowd models and discuss the advantages and shortages from the angle of human-centered sensing. Their review reveals important research opportunities that may contribute to an improved and more robust emergency management.

A Study on the Usage of Smartphone Apps in Fire Scenarios

In this paper, we present a thorough overview of the two recently developed applications in the field of emergency management. The applications titled GDACSmobile and SmartRescue are using mobile app and smartphone sensors as the main functionality respectively. Furthermore, we argue the differences and similarities of both applications and highlight their strengths and weaknesses. Finally, a critical scenario for fire emergency in a music festival is designed and the applicability of the features of each application in supporting the emergency management procedure is discussed. It is also argued how the aforementioned applications can support each other during emergencies and what the potential collaboration between them can be.

Predicting Congestions in a Ship Fire Evacuation: A Dynamic Bayesian Networks Simulation

In this paper, some new simulation results achieved from our proposed simulation model for analyzing congestions in ship evacuation are presented. To guarantee a safe evacuation, this model considers the most important real-life factors including, but not limited to, the passengers’ panic, the age and sex of the passengers, the structure of the ship, and so on. The qualitative factors have been quantized in order to compute the probability of congestion during the entire evacuation. We then utilize the dynamic Bayesian network (DBN) to predict congestion and to handle the non-stationarity of the scenario with respect to the time. Considering the most important scenarios and running the simulation, we demonstrate the distinct effects of these factors on congestion. The role of decision supports (DS), i.e. smartphone evacuation applications and rescue team presence on congestion is also studied. In addition, the impact of congested escape routes on the evacuation time is also investigated. The presented model and results of this paper are possible decision support tools for maritime organizations, emergency management sectors, and rescuers onboard the ships, which try to alleviate the human or property losses.