  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.
