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 multi-objective route planning model based on genetic algorithm for cuboid surfaces

During a natural disaster, risk management for the evacuation of people in high-rise buildings is very important for saving lives. In the case of fire, all parameters such as detection, lighting, warning systems, etc. for safety must be used interactively. Determination of evacuation conditions and different ways out are important parameters during the fire. In this study, a system is proposed for evacuating people from building with the shortest/safest route, taking into account certain factors to evaluate the current situation of the fire. Travelling Salesman Problem (TSP) may be adapted to this real-life problem to protect people in the shortest time finding optimum route. In this study, the system based on Genetic Algorithm is performed using the online information about smoke, heat and safety level, the location of fire and the potential congestion of people in order to evacuate people from the building with safety route. The system contains two- and three-dimensional surface applications to ensure evacuation with optimum distance inside/outside of the building. Results are evaluated considering the evacuation distance. Compared to other methods in the literature, the solution to this problem is improved by adding the evacuation process for the elevator and inside of the building

Escape planning in realistic fire scenarios with Ant Colony Optimisation

Published version of an article from the journal:Applied Intelligence Also available on Springerlink: http://dx.doi.org/10.1007/s10489-014-0538-9An 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

High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm

Evacuation of high rise building has become an issue nowadays as the modern development has increased tremendously with a very complex structure and design. The complexity and height of the building can affect the successfulness of the evacuation process, especially towards unfamiliar occupants in the building. Generally, they only know the route taken while they enter the building. Moreover, the available evacuation map provided by the building is not showing the shortest and safest path. Subsequently, they are hard to find the optimal route to escape. Furthermore, the shortest path algorithm needed additional features to produce better result. This research aims to assist the evacuees to find the shortest path in a high rise building using a shortest path algorithm. The objective is to design and develop an evacuation route using shortest path algorithm based on the evacuation map of the building. The method involves in this research starts with abstracting the original floor plan of the high rise building into CAD format. The floor plan is an important data to be used in this study, which is to design the evacuation route of the building. However, the original floor plan is visualised into 2D layout to gather the information on nodes and weights. The information then is used to generate a directed graph in order to obtain the shortest path results through the implementation of shortest path algorithm. The main algorithms involve is Dijkstra’s algorithm and then an Ant Colony Optimization algorithm is used as hybrid versions of Dijkstra’s algorithm. As a result, the evacuation route model is able to gain the shortest path and safest path consistently between Dijkstra’s algorithms and hybrid version which is Dijkstra-Ant Colony Optimization (DACO). In conclusion, based on the results, the shortest path can be implemented into a computerized evacuation map of the high rise building which can assist evacuees in pre evacuation to find the shortest and safest path to evacuate

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

Intergrating the Fruin LOS into the Multi-Objective Ant Colony System

Building evacuation simulation provides the planners and designers an opportunity to analyse the designs and plan a precise, scenario specific instruction for disaster times. Nevertheless, when disaster strikes, the unexpected may happen and many egress paths may get blocked or the conditions of evacuees may not let the execution of emergency plans go smoothly. During disaster times, effective route-finding methods can help efficient evacuation process, in which the directors are able to react to the sudden changes in the environment. This research tries to integrate the highly accepted human dynamics methods proposed by Fruin into the Ant-Colony optimisation route-finding method. The proposed method is designed as a multi-objective ant colony system, which tries to minimize the congestions in the bottlenecks during evacuations, in addition to the egress time, and total traversed time by evacuees. This method embodies the standard crowd dynamics method in the literature, which are Fruin LOS and pedestrian speed. The proposed method will be tested against a baseline method, that is shortest path, in terms of the objective functions, which are evacuation time and congestion degree. The results of the experiment show that a multi-objective ant colony system performance is able to reduce both egress time and congestion degree in an effective manner, however, the method efficiency drops when the evacuee population is small. The integration of Fruin LOS also produces more meaningful results, as the load responds to the Level of Service, rather than the density of the crowd, and the Level of Service is specifically designed for the sake of measuring the ease of crowd movement

Groupwise evacuation with genetic algorithms

In a crisis situation on board a ship, it can be of the utmost importance to have the passengers safely evacuate to the lifeboats in an efficient manner. Existing methods such as marked escape routes, maps and so on are not optimal as pre-planned escape routes may become heavily congested by passengers. The closest lifeboat is not always feasible as lifeboat capacity can be exceeded. Considering that some evacuees are strongly affiliated and would like to evacuate together as a group, it all becomes a very difficult problem to solve. Sub-problems have been modelled, but no existing model combines all of these aspects into account. We proceed by modelling the area to be evacuated as a time-expanded graph, assuming that future development in hazard severity is known in the form of a survivability percentage for each node. Then we apply a multi-objective genetic algorithm with five different fitness functions that use heuristics to maximize overall survivability and reduce the total egress time if possible. A method has been developed to pick the best evacuation plan out of the pool of potential solutions returned by the genetic algorithm. The solution is compared with Dijkstra’s algorithm and randomly generated paths. Experiments are conducted using these algorithms for both predefined and randomly generated graphs using different parameters. In the tested random graph, the genetic algorithm gives on average 24% better survivability and 3 times better grouping Random algorithms. A fixed network with a known solution was solved 100%. This genetic algorithm can be used to generate better routing plans that utilizes multiple evacuation routes and lifeboats while taking into account groups, resulting in smoother evacuations which can save more lives