team has written two articles in the Proceedings to the 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Amsterdam, The Netherlands this summer.
Ant Colony Optimisation for Planning Safe Escape Routes
Reference: Morten Goodwin, Ole-Christoffer Granmo, Jaziar Radianti, Parvaneh Sarshar, Sondre Glimsdal (2013). "Ant Colony Optimisation for Planning Safe Escape Routes"
, Recent Trends in Applied Artificial Intelligence. Lecture Notes in Computer Science Volume 7906, 2013, pp 53-62.
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 in Disasters for Evacuation Planning
Reference: Ole-Christoffer Granmo, Jaziar Radianti, Morten Goodwin, Julie Dugdale, Parvaneh Sarshar, Sondre Glimsdal, Jose J. Gonzalez (2013)."A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning"
, Recent Trends in Applied Artificial Intelligence, Lecture Notes in Computer Science Volume 7906, 2013, pp 63-72.
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-temporalprobabilistic 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.