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

arXiv: https://arxiv.org/abs/2404.02740 · HTML: https://mlsanity.com/paper/2404.02740/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [Large Language Models are Zero-Shot Next Location Predictors](https://mlsanity.com/paper/2405.20962/) (2024)
- [ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction](https://mlsanity.com/paper/2410.14099/) (2024)
- [Enhancing stop location detection for incomplete urban mobility datasets](https://mlsanity.com/paper/2407.11579/) (2024)
- [SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction](https://mlsanity.com/paper/2510.01245/) (2025)
- [Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction](https://mlsanity.com/paper/2501.06561/) (2025)
- [Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model](https://mlsanity.com/paper/2511.01929/) (2025)

## Abstract (arXiv)

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.