Predicting human mobility through the assimilation of social media traces into mobility models

TL;DR

This paper introduces a hybrid human mobility model combining social media data with classical models, significantly improving prediction accuracy across different spatial scales.

Contribution

It presents a novel hybrid model integrating social media traces with gravity models using stacked regression, enhancing mobility prediction accuracy.

Findings

Improved prediction accuracy at multiple spatial scales.

Effective integration of social media data into mobility models.

Validated across air travel and daily commuting datasets.

Abstract

Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.