Indoor evacuation planning using a limited number of sensors (original) (raw)

electronics A Survey of Algorithms and Systems for Evacuating People in Confined Spaces

The frequency, destruction and costs of natural and human-made disasters in modern highly-populated societies have resulted in research on emergency evacuation and wayfinding, which has drawn considerable attention. The subject is now a multidisciplinary area of research where information and communication technologies (ICT), and in particular the Internet of Things (IoT), have a significant impact on sensing and computing dynamic reactions that mitigate or prevent the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to share ongoing research results, identify the research gaps and address the need for future research. We present a comprehensive review of research on emergency evacuation and wayfinding, focusing on the algorithmic and system design aspects. Starting from the history of emergency management research, we identify the emerging challenges concerning system optimisation, evacuee behaviour optimisation and data analysis, and the additional energy consumption by ICT equipment that operates the emergency management infrastructure.

Experiences with evacuation route planning algorithms

2012

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in the event of natural disasters. Hurricane Rita and the recent tsunami revealed limitations of traditional approaches to provide emergency preparedness for evacuees and to predict the effects of evacuation route planning (ERP). Challenges arise during evacuations due to the spread of people over space and time and the multiple paths that can be taken to reach them; key assumptions such as stationary ranking of alternative routes and optimal substructure are violated in such situations. Algorithms for ERP were first developed by researchers in operations research and transportation science. However, these proved to have high computational complexity and did not scale well to large problems. Over the last decade, we developed a different approach, namely the Capacity Constrained Route Planner (CCRP), which generalizes shortest path algorithms by honoring capacity constraints and the spread of people over space and time. The CCRP uses time-aggregated graphs to reduce storage overhead and increase computational efficiency. Experimental evaluation and field use in Twin Cities Homeland Security scenarios demonstrated that CCRP is faster, more scalable, and easier to use than previous techniques. We also propose a novel scalable algorithm that exploits the spatial structure of transportation networks to accelerate routing algorithms for large network datasets. We evaluated our new approach for large-scale networks around downtown Minneapolis and riverside areas. This article summarizes experiences and lessons learned during the last decade in ERP and relates these to Professor Goodchild's contributions.

A Simulation-based Approach for Large-scale Evacuation Planning

2020 IEEE International Conference on Big Data (Big Data), 2020

Evacuation planning methods aim to design routes and schedules to relocate people to safety in the event of natural or man-made disasters. The primary goal is to minimize casualties which often requires the evacuation process to be completed as soon as possible. In this paper, we present QueST, an agent-based discrete event queuing network simulation system, and STEERS, an iterative routing algorithm that uses QueST for designing and evaluating large scale evacuation plans in terms of total egress time and congestion/bottlenecks occurring during evacuation. We use the Houston Metropolitan Area, which consists of nine US counties and spans an area of 9,444 square miles as a case study, and compare the performance of STEERS with two existing route planning methods. We find that STEERS is either better or comparable to these methods in terms of total evacuation time and congestion faced by the evacuees. We also analyze the large volume of data generated by the simulation process to gain insights about the scenarios arising from following the evacuation routes prescribed by these methods.

Capacity Based Evacuation with Dynamic Exit Signs

arXiv (Cornell University), 2013

Exit paths in buildings are designed to minimise evacuation time when the building is at full capacity. We present an evacuation support system which does this regardless of the number of evacuees. The core concept is to even-out congestion in the building by diverting evacuees to less-congested paths in order to make maximal usage of all accessible routes throughout the entire evacuation process. The system issues a set of flow-optimal routes using a capacity-constrained routing algorithm which anticipates evolutions in path metrics using the concept of "future capacity reservation". In order to direct evacuees in an intuitive manner whilst implementing the routing algorithm's scheme, we use dynamic exit signs, i.e. whose pointing direction can be controlled. To make this system practical and minimise reliance on sensors during the evacuation, we use an evacuee mobility model and make several assumptions on the characteristics of the evacuee flow. We validate this concept using simulations, and show how the underpinning assumptions may limit the system's performance, especially in low-headcount evacuations.

Convergent Plans for Large-Scale Evacuations

Evacuation planning is a critical aspect of disaster preparedness and response to minimize the number of people exposed to a threat. Controlled evacuations aim at managing the flow of evacuees as efficiently as possible and have been shown to produce significant benefits compared to self-evacuations. However, existing approaches do not capture the delays introduced by diverging and crossing evacuation routes, although evidence from actual evacuations highlights that these can lead to significant congestion. This paper introduces the concept of convergent evacuation plans to tackle this issue. It presents a MIP model to obtain optimal convergent evacuation plans which, unfortunately, does not scale to realistic instances. The paper then proposes a two-stage approach that separates the route design and the evacuation scheduling. Experimental results on a real case study show that the two-stage approach produces better primal bounds than the MIP model and is two orders of magnitude faster; It also produces dual bounds stronger than the linear relaxation of the MIP model. Finally, simulations of the evacuation demonstrate that convergent evacuation plans outperform existing approaches for realistic driver behaviors.

Scalable Evacuation Routing in Dynamic Environments

In the face of a natural or man-made disaster, evacuation planning refers to the process of reallocating the endangered population to a set of safe places, often in a hurry. Such a task needs proper preparation, execution, and most definitely a post-disaster response. We have contributed a new taxonomy of the evacuation planning problem and categorized available solutions. Evacuation routing is part of the bigger problem that finds the best routes to relocate the endangered population to safety. Given circumstances, even the tiniest improvement in evacuation routing during execution can save many lives. Therefore, different research communities are looking at this particular problem from their own viewpoints hoping to design a better practical solution. We propose a new method to perform evacuation routing efficiently under capacity constraints. Traditionally, simulation software or shortest path routing combined with zonal scheduling have been used to solve routing problems. Our method utilizes a state-of-the-art algorithm to connect each source node to its nearest destination. It also intelligently takes into account transportation network capacity and traffic flow to minimize congestion and system-wide transportation times. We have compared our method with previous routing algorithms and a common simulation method in a static environment. We show that our algorithm generates reliable and realistic routes and decreases transportation time by at least an order of magnitude, without any loss of performance. We also define the dynamic evacuation routing problem and propose a solution. The dynamic solution is capable of updating routes if the network topology is changed during the evacuation process. Effectively, it can solve the evacuation problem for a xiii moving disaster. We argue that an ideal evacuation routing algorithm should be able to generate realistic and efficient routes in a dynamic environment because changes to the road network are likely to happen after natural disasters. For example if a main road is blocked during a flood, the evacuation routing algorithm updates the plan based on this change in the road network and pushes the changed routes to the corresponding evacuees. In this dissertation we discuss evacuation routing and how it is connected to different aspects of the evacuation planning problem. Major works in this field have been studied and a better algorithm has been developed. The new algorithm’s performance and running time is iteratively improved and reported along with a comparison with previous works. The algorithm is extended to also solve the problem in a dynamic environment. Together these new developments pave the path for future researchers to study the evacuation problem and to integrate it into urban transportation services. Hopefully one day we can save more lives than before when future disasters occur.

Improved Algorithms for the Evacuation Route Planning Problem

Lecture Notes in Computer Science, 2015

Emergency evacuation is the process of movement of people away from the threat or actual occurrence of hazards such as natural disasters, terrorist attacks, fires and bombs. In this paper, we focus on evacuation from a building, but the ideas can be applied to city and region evacuation. We define the problem and show how it can be modeled using graphs. The resulting optimization problem can be formulated as an integer linear program. Though this can be solved exactly, this approach does not scale well for graphs with thousands of nodes and several hundred thousands of edges. This is impractical for large graphs. We study a special case of this problem, where there is only a single source and a single sink. For this case, we give an improved algorithm Single Source Single Sink Evacuation Route Planner (SSEP), whose evacuation time is always at most that of a famous algorithm Capacity Constrained Route Planner (CCRP), and whose running time is strictly less than that of CCRP. We prove this mathematically and give supporting results by extensive experiments. We also study randomized behavior model of people and give some interesting results.

Intelligent Evacuation Management Systems

ACM Transactions on Intelligent Systems and Technology, 2016

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emerg...

Multiple exits evacuation algorithm for real-time evacuation guidance

Spatial Information Research, 2017

Most studies for minimizing total evacuation time do not take into account aspects of realistic evacuation guidance because they focus on minimizing evacuation time arithmetically. In the mentioned study, occupants in one space can be divided and move to different exits in order to minimize the evacuation time. However, in an emergency situation, it is practically difficult to guide occupants in a space to different directions, and may confuse them significantly. For this reason, this study proposed a multiple exits evacuation algorithm (MEEA) that guide the occupants in one space to the same exit. The MEEA is based on graph theory and computes a process of exits assignment of the nodes, leading to the division of the spaces based on the exits. Each exit competitively absorbs nodes, repeating until evacuation times of exits are balanced and the total evacuation time is minimized. In order to verify MEEA, this study used evacuation simulators based on cellular automata called EgresSIM to compare the evacuation results of well-known evacuation models EVACNET4 and MEEA.

Evacuation Route Optimization Architecture Considering Human Factor

AI Communications, 2017

Rapidly changing detrimental safety conditions on an evacuation route might cause casualties and provoke panic. This is why we need to take into account these conditions for real-time route optimization. The objective of this paper is to facilitate efficient people evacuation through dynamic optimization of evacuation routes based on the safety conditions on the route and stress-related evacuees' responses to the same. We propose a multi-agent based evacuation route optimization architecture for smart space networks that considers the influence of stress on human reactions to the recommended routes. This architecture is meant as a real-time decision support in route selection that recommends routes to evacuees, possibly through smart phone or smart building displays. The functioning of the architecture is shown on an example of a simple smart space network.