Routing algorithms as tools for integrating social distancing with emergency evacuation

• URL: http://arxiv.org/abs/2103.03413v1
• Date: Fri, 5 Mar 2021 01:12:31 GMT
• Title: Routing algorithms as tools for integrating social distancing with emergency evacuation
• Authors: Yi-Lin Tsai (1), Chetanya Rastogi (2), Peter K. Kitanidis (1, 3, and 4), Christopher B. Field (3, 5, and 6) ((1) Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA, (2) Department of Computer Science, Stanford University, Stanford, CA, USA, (3) Woods Institute for the Environment, Stanford University, Stanford, CA, USA, (4) Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA, (5) Department of Biology, Stanford University, Stanford, CA, USA, (6) Department of Earth System Science, Stanford University, Stanford, CA, USA)
• Abstract summary: We explore the implications of integrating social distancing with emergency evacuation when a hurricane approaches a major city during the COVID-19 pandemic. We compare DNN-based and non-DNN methods for generating evacuation strategies that minimize evacuation time while allowing for social distancing in rescue vehicles. Although DNN-based solution can save considerable time in evacuation routing, it does not come close to compensating for the extra time required for social distancing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we explore the implications of integrating social distancing with emergency evacuation when a hurricane approaches a major city during the COVID-19 pandemic. Specifically, we compare DNN (Deep Neural Network)-based and non-DNN methods for generating evacuation strategies that minimize evacuation time while allowing for social distancing in rescue vehicles. A central question is whether a DNN-based method provides sufficient extra efficiency to accommodate social distancing, in a time-constrained evacuation operation. We describe the problem as a Capacitated Vehicle Routing Problem and solve it using one non-DNN solution (Sweep Algorithm) and one DNN-based solution (Deep Reinforcement Learning). DNN-based solution can provide decision-makers with more efficient routing than non-DNN solution. Although DNN-based solution can save considerable time in evacuation routing, it does not come close to compensating for the extra time required for social distancing and its advantage disappears as the vehicle capacity approaches the number of people per household.

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