Reverse Engineering Human Mobility in Large-scale Natural Disasters

TL;DR

This paper develops and evaluates a new human mobility model for large-scale natural disasters, based on expert knowledge and historical data, to improve Delay/Disruption-Tolerant Network performance assessment.

Contribution

It introduces a novel reverse-engineered mobility model for natural disasters, validated through simulations and comparison with existing models.

Findings

The new model better predicts human movement during disasters.

Simulation results show improved DTN performance with the new model.

Public release of source code facilitates further research.

Abstract

Delay/Disruption-Tolerant Networks (DTNs) have been around for more than a decade and have especially been proposed to be used in scenarios where communication infrastructure is unavailable. In such scenarios, DTNs can offer a best-effort communication service by exploiting user mobility. Natural disasters are an important application scenario for DTNs when the cellular network is destroyed by natural forces. To assess the performance of such networks before deployment, we require appropriate knowledge of human mobility. In this paper, we address this problem by designing, implementing, and evaluating a novel mobility model for large-scale natural disasters. Due to the lack of GPS traces, we reverse-engineer human mobility of past natural disasters (focusing on 2010 Haiti earthquake and 2013 Typhoon Haiyan) by leveraging knowledge of 126 experts from 71 Disaster Response Organizations (DROs). By means of simulation-based experiments, we compare and contrast our mobility model to other well-known models, and evaluate their impact on DTN performance. Finally, we make our source code available to the public.