Real-Time Pedestrian Evacuation Planning during Emergency

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.

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“…Hadzic et al [5] focused on evacuation in buildings on fire. Their model is an extension of a dynamic network flow.…”

Section: Evacuationmentioning

confidence: 99%

Optimized evacuation route based on crowd simulation

et al. 2017

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“…how many people can walk abreast without impeding each other. Network Flow Graphs are often used to model road traffic in emergencies [14,15] and as a basis for dynamic evacuation planning [10,11,12]. In this research, we use the flow capacity constraint to determine how long it takes for an entire group of occupants to completely traverse the edge, analogous to calculating the amount of time it takes to transfer a given amount of data across a network link of limited bandwidth…”

Section: Network Flow Graphmentioning

confidence: 99%

“…This evacuation planner accounts for the location of hazards and produces evacuation routes that minimize evacuation time and exposure to the hazard. Alternative route generation schemes could be used, such as dynamic flow-based planners [10][11][12] or exploiting day-to-day sensor data to discover common exit routes.…”

Section: Route Allocationmentioning

confidence: 99%

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Problem Decomposition for Evacuation Simulation Using Network Flow

Murphy

Brown

Sreenan

2012

2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications

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Abstract. Simulation of building evacuations can be a powerful tool for predicting evacuation outcomes, but for this prediction to be useful it must be produced in a timely manner. The building evacuation outcomes are dependent on the movement decisions of the occupants, but simulating all possible combinations of occupant decisions is infeasible. Our contribution is a novel technique using building structure knowledge in the form of a Network Flow Graph to determine where and when occupants might interact with one another. We decompose the problem into non-interacting groups, to be simulated separately, which leads to a significant simulation workload reduction.Keywords: evacuation monitoring, multi-agent simulation, distributed simulation, network flow graph, problem decomposition IntroductionSimulation of building evacuation during emergencies is a powerful tool for evaluating building safety and prediction of evacuation outcomes [1]. Simulating the movement of evacuating pedestrians can provide useful information to building designers and to fire safety officials, including evacuation time estimates and locations at risk of traffic congestion or evacuee injury [1][2][3][4][5][6][7]. We make use of a 2D vector-space pedestrian simulator to simulate the individual movements of pedestrians in the building. This simulator provides a realistic model of pedestrian movement but is computationally intensive compared to simpler models such as Cellular Automata [2,4,10] or Graph-based simulations [5]. The usefulness of any prediction information is contingent on both the timeliness and the accuracy of the simulation that generated it. Since a real evacuation involves autonomous human actors and uncertain hazard spread, to ensure that the simulations cover likely scenarios we must run many different scenarios. The more we can reduce the simulation time, the more scenarios we can consider, and the more useful the results will be. In this paper, we investigate the problem of how to cover a wide range of scenarios without an excessive increase in computation time.We assume that clusters of individuals in an evacuation will behave as a group, and once they have formed a group all members will follow the same route out of the building. We reduce the amount of simulation by limiting our attention to the most likely routes for each initial group. However, different groups may meet during the evacuation and cause congestion, and may form into larger groups to continue the evacuation. Since congestion slows down the evacuation, and puts the participants at risk, we must investigate the interaction of the different groups. Even for small numbers of likely routes, this group interaction quickly produces an excessively large simulation space. We reduce the complexity of the problem by quickly generating possible group interactions using Network-Flow Graph analysis, identifying groups and routes which do not interact, distributing the simulation of those local scenarios to different processors, and then combining the results to pro...

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“…A GT-based mobile object trapping system is introduced in which the mobile target is captured by the mobile sensors by forming coalitions and sharing significant information, thus minimizing the capturing time [7]. In [8], different types of approaches are introduced for evacuation of pedestrians during emergency operation. The problem is modeled as an extension of dynamic network flow and heuristic methods, whose deployment and performances are compared with existing techniques.…”

Section: Introductionmentioning

confidence: 99%

Strategies for Searching Targets Using Mobile Sensors in Defense Scenarios

Hazra¹,

Kumar²,

Nene³

2017

IJITCS

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Abstract-Target searching is one of the challenging research areas in defense. Different types of sensor networks are deployed for searching targets in critical zones. The selection of optimal strategies for the sensor nodes under certain constraints is the key issue in target searching problem. This paper addresses a number of target searching problems related to various defense scenarios and introduces new strategic approaches to facilitate the search operation for the mobile sensors in a two-dimensional bounded space. The paper classifies the target searching problems into two categories: preference-based and traversal distance based. In the preference based problems, the strategies for the mobile sensors are determined by Stable Marriage Problem, College Admission Problem, and voting system; they are analyzed with suitable examples. Alternatively, traversal distance based problems are solved by our proposed graph searching approaches and analyzed with randomly chosen examples. Results obtained from the examples signify that our proposed models can be applied in defense-related target searching problems.

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Real-Time Pedestrian Evacuation Planning during Emergency

Cited by 27 publications

References 7 publications

Optimized evacuation route based on crowd simulation

Optimized evacuation route based on crowd simulation

Problem Decomposition for Evacuation Simulation Using Network Flow

Strategies for Searching Targets Using Mobile Sensors in Defense Scenarios

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