Mixing Individual and Collective Behaviours to Predict Out-of-Routine Mobility

TL;DR

This paper presents a novel model that combines individual and collective mobility behaviors to improve the prediction of out-of-routine human displacements, outperforming existing methods especially during disruptions like COVID-19.

Contribution

The study introduces a dynamic integration approach that leverages collective intelligence to enhance mobility prediction accuracy beyond traditional deep learning models.

Findings

Superior prediction accuracy for out-of-routine mobility.

Effective near urban points of interest.

Retains performance during disruptive events like COVID-19.

Abstract

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviours. Our study introduces an approach that dynamically integrates individual and collective mobility behaviours, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across three US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. Spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviours strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviours, our approach offers transparent and accurate predictions, crucial for addressing contemporary mobility challenges.