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

Understand

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.