Capacity Based Evacuation with Dynamic Exit Signs (original) (raw)
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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.
Transportation Research Part B: Methodological, 2010
This paper shows that evacuation routes, such as a building's stairwell or an urban freeway, may discharge inefficiently if left unmanaged, and that setting priority rules can speed up egress. Therefore, a simple control strategy is proposed. The strategy is decentralized and adaptive, based on readily available real-time data. The strategy is shown to be optimal in two senses: (i) it evacuates the maximum number of people at all times, and (ii) it finishes the evacuation in the least possible time. In both cases, it favors the people most at risk. The results shed light on other traffic problems.
Optimized evacuation route based on crowd simulation
Computational Visual Media
An evacuation plan helps people move away from an area or a building. To assist rapid evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and maximize the number of evacuees arriving at exits in each time span. Our system considers crowd distribution, exit locations, and corridor widths when determining optimal routes. It also simulates crowd movements during route optimization. As a basis, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits at nearly the same time. If this is not the case, our system updates the routes of the slower crowds. As crowd simulation is non-linear, the optimal route is computed in an iterative manner. The system repeats until an optimal state is achieved. In addition to directly computing optimal routes for a situation, our system allows the structure of the situation to be decomposed, and determines the routes in a hierarchical manner. This strategy not only reduces the computational cost but also enables crowds in different regions to evacuate with different priorities. Experimental results, with visualizations, demonstrate the feasibility of our evacuation route optimization method.
Proceedings of the 2009 13th IEEE/ACM …, 2009
To individuals evacuating, many multi-exit environments do not allow visibility of all the exits due to lineof-sight constraint. In addition, the environment can be dark or smoky, not allowing visibility to even a single exit. In such a situation, given that each individual in the crowd is accompanied with a helping device globally connected with a central server, a 'directional guidance' towards an optimal exit is a real possibility. In this context, the 'occupant density' around exits (within a static 'exit area') has been used in conjunction with the corresponding distances to devise a probabilistic strategy for optimal exit suggestion [1]. In this paper, we related the exit area with the level of visibility of the environment (the more the visibility, the more the exit area and vice versa). In this way, a more realistic human-behavioral model is implemented in which an individual viewing (seeing) an exit would always direct towards that exit, irrespective of the directional guidance provided. When an individual is not at any of the exit areas (not viewing even a single exit), a directional guidance is provided assuming that the individual is adhering to it. Additionally we used the measure of 'effective throughput' instead of occupant density, in conjunction with the corresponding distances. Through simulation results, we found a marked improvement in the evacuation time, when effective throughput was modeled instead of occupant density.
Help Me Evacuate! A Smart Adaptable Evacuation System With Congestion Prediction Capabilities
IEEE Access
Emergency evacuation planning is a vital problem that affects building occupants' safety. The commonly used static evacuation plans rely on static signs disregarding crowd density changes. Such static plans often lead to congestion at emergency exits; since many occupants tend to avoid following exit signs as they feel safer following the crowds exiting the building or following other paths familiar to them. This paper proposes a smart and adaptable evacuation system that predicts congestion and adapts accordingly to minimize evacuation time. We introduce a simulation model that mimics occupants' movement in different building layouts. The proposed system performs Monte Carlo simulations to forecast possible congestion locations and guide occupants away from them. Guiding directions are displayed and updated to consider dynamic environment changes. We evaluated our approach compared to a greedy evacuation method that relies on static exit signs, showing a significant evacuation time improvement of 21% achieved on average by our approach. INDEX TERMS Congestion forecasting, Monte Carlo simulations, smart evacuation.
Evaluating and Optimizing Evacuation Plans for Crowd Egress
IEEE computer graphics and applications, 2017
Evacuation planning is an important and difficult task in building design. The proposed framework can identify optimal evacuation plans using decision points, which control the ratio of agents that select a particular route at a specific spatial location. The authors optimize these ratios to achieve the best evacuation based on a quantitatively validated metric for evacuation performance. This metric captures many of the important aspects of an evacuation: total evacuation time, average evacuation time, agent speed, and local agent density. The proposed approach was validated using a night club model that incorporates real data from an actual evacuation.
A dynamic approach for evacuees' distribution and optimal routing in hazardous environments
Automation in Construction, 2018
In a complex built environment, the situation changes rapidly during an emergency event. Typically, available systems rely heavily on a static scenario in the calculation of safest routes. In addition, egress route calculation and evacuation simulations are performed separately from path-finding for rescue teams. In this paper, we propose a state-of-the-art dynamic approach, which deals not only with a 3D environment, shape of spaces and hazard location, but also with the dynamic distribution of occupants during evacuation. A database of densities and information about hazard influence are generated and used to calculate optimal paths for rescue teams. Three simulation scenarios are compared in this study-namely, static with constant density values determined for subsequent stages of evacuation, semi-dynamic with densities representing an actual people distribution in a building during evacuation simulation, and dynamic with temporal distribution of evacuees stored in a database, and dynamically used in optimal path calculations. The findings revealed that static simulation is significantly different from semi-dynamic and dynamic simulations, and each type of simulation is better suited for the decision task at hand.
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.
This paper deals with the optimal allocation of routes, destination, and departure times to members of a crowd, for instance in case of an evacuation or another hazardous situation in which the people need to leave the area as quickly as possible. The generic approach minimizes the evacuation times, considering the demand dependent waiting times at bottlenecks within the considered infrastructure. We present the mathematical optimization problem for both the optimal instructions, and the continuum model describing the pedestrian flow dynamics. The key contribution of the approach is that it solves the evacuation problem considering the entire solution space in a continuous manner (i.e. both the time dimension and the routing), implying that for each location and for each time instant the optimal path towards the most favorable exit is calculated, taking into consideration the traffic flow operations along the routes. The approach is generic in the sense that different network loading models can be used, and that a variety of components can be added to the optimization objective without loss of generality. Next to presenting the framework and the mathematical model, we propose an iterative numerical solver to compute the optimal instructions. We demonstrate the abilities and opportunities of this optimization framework with two case studies.
Evacuation Simulation under Different Conditions using a Safest Path Routing Algorithm
Proceedings of the 18th International Conference on Enterprise Information Systems, 2016
In this contribution we propose a safest path route algorithm for determination of the safest path directions of pedestrians in case of fire. The model and the algorithms are implemented in an open source framework (JuPedSim) which is a research platform to simulate pedestrian dynamics. We found that increasing the importance of the obstruction criteria (responsible for people's density) leads to a reduction of the total evacuation time. The proposed algorithm allows the even distribution of the evacuees to all available emergency exits, when there is an uneven distribution of people on the escape routes while avoiding a place with fire hazards. We simulate the evacuation of a shopping centre and showed that the application of the algorithm can reduce the total evacuation time up to 63% depending on the settings of the algorithm.