Routing algorithms as tools for integrating social distancing with emergency evacuation
Stanford University（斯坦福大学）；Woods Institute for the Environment, Stanford University（斯坦福大学伍兹环境研究所）；Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；Department of Biology, Stanford University（斯坦福大学生物系）；Department of Earth System Science, Stanford University（斯坦福大学地球系统科学系）
Yi-Lin Tsai, Chetanya Rastogi, Peter K. Kitanidis, Christopher B. Field
One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.
Well-executed emergency evacuations can save lives and reduce suffering. However, decision makers struggle to determine optimal evacuation policies given the chaos, uncertainty, and value judgments inherent in emergency evacuations. We propose and analyze a decision support tool for pre-crisis training exercises for teams preparing for civilian evacuations and explore the tool in the case of the 2021 U.S.-led evacuation from Afghanistan. We use different classes of Markov decision processes (MDPs) to capture compounding levels of uncertainty in (1) the priority category of who appears next at …
University of Connecticut（康涅狄格大学）；Suzhou Zhito Technology Co., LTD（苏州智途科技有限公司）
A very successful model for simulating emergency evacuation is the social-force model. At the heart of the model is the self-driven force that is applied to an agent and is directed towards the exit. However, it is not clear if the application of this force results in optimal evacuation, especially in complex environments with obstacles. Here, we develop a deep reinforcement learning algorithm in association with the social force model to train agents to find the fastest evacuation path. During training, we penalize every step of an agent in the room and give zero reward at the exit. We adopt …
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
Centre for Artificial Intelligence Research（人工智能研究中心）；Department of Information and Communication Technology（信息与通信技术系）；University of Agder, Norway（阿格德大学，挪威）
Strategizing Equitable Transit Evacuations: A Data-Driven Reinforcement Learning Approach
School of Sustainable Engineering and the Built Environment, Arizona State University（亚利桑那州立大学可持续工程与建筑环境学院）；University of California, Berkeley（加州大学伯克利分校）
The Pennsylvania State University（宾夕法尼亚州立大学）；The University of California, Merced（加州大学默塞德分校）
This paper describes our recent effort to use virtual reality to simulate threatening emergency evacuation scenarios in which a robot guides a person to an exit. Our prior work has demonstrated that people will follow a robot's guidance, even when the robot is faulty, during an emergency evacuation. Yet, because physical in-person emergency evacuation experiments are difficult and costly to conduct and because we would like to evaluate many different factors, we are motivated to develop a system that immerses people in the simulation environment to encourage genuine subject reactions. We are w…
Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, earthquakes, etc. An ability to evaluate evacuation plans in advance for these rare events, including identifying traffic flow bottlenecks, improving traffic management policies, and understanding the robustness of the traffic management policy are critical for emergency management. Given the rareness of such events and the corresponding lack of real data, traffic simulation provides a flexible and versatile approach for such scenarios, and furthermore allows dynamic interac…
College of Science, University of Sulaimani（苏莱曼尼大学理学院）；Sulaimania Polytechnic University（苏莱曼尼亚理工大学）；University of Kurdistan Hewler（休勒库尔德斯坦大学）
This paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata(CA) was fully integrated with fuzzy logic, the kth nearest neighbors (KNN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to his/her physical, biological and emotional features. The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his or her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits. These elements al…
A decade on from the devastating Black Saturday bushfires in Victoria, Australia, we are at a point where computer simulations of community evacuations are starting to be used within the emergency services. While fire progression modelling is embedded in strategic and operational settings at all levels of government across Victoria, modelling of community response to such fires is only just starting to be evaluated in earnest. For community response models to become integral to bushfire planning and preparedness, the key question to be addressed is: when faced with a bushfire, what will a comm…
NICTA（澳大利亚国家信息与通信技术研究院）；University of Melbourne（墨尔本大学）；Australian National University（澳大利亚国立大学）
Large-scale controlled evacuations require emergency services to select evacuation routes, decide departure times, and mobilize resources to issue orders, all under strict time constraints. Existing algorithms almost always allow for preemptive evacuation schedules, which are less desirable in practice. This paper proposes, for the first time, a constraint-based scheduling model that optimizes the evacuation flow rate (number of vehicles sent at regular time intervals) and evacuation phasing of widely populated areas, while ensuring a nonpreemptive evacuation for each residential zone. Two opt…
New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multipronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information…
Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm
Security and Optimization for Networked Globe Laboratory (SONG Lab), Department of Information Systems, University of Maryland, Baltimore County（安全与网络化全球实验室（SONG实验室）、信息系统系、马里兰大学巴尔的摩县分校）；Department of Computer Science, University of Colorado（计算机科学系、科罗拉多大学）；Mathematics Department and College of Aviation, Embry-Riddle Aeronautical University（数学系和航空学院、埃姆布里-里德尔航空大学）；Department of Mechanical Aerospace Engineering, New Mexico State University（机械航空航天工程系、新墨西哥州立大学）
