Routing algorithms as tools for integrating social distancing with emergency evacuation

TL;DR

This paper compares Deep Reinforcement Learning and traditional algorithms for routing in emergency evacuations with social distancing, finding that while DRL offers more efficient routes, it does not significantly reduce evacuation time when social distancing is enforced.

Contribution

It models evacuation with social distancing as a Capacitated Vehicle Routing Problem and evaluates DRL against traditional algorithms, highlighting their relative efficiencies.

Findings

DRL provides more efficient routing than Sweep Algorithm.

Evacuation time savings with DRL are limited by social distancing constraints.

DRL's advantage diminishes as vehicle capacity approaches household size.

Abstract

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.