# Future Directions in Human Mobility Science

arXiv: https://arxiv.org/abs/2408.00702 · HTML: https://mlsanity.com/paper/2408.00702/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2024.

## Built on by (most cited first, 16 of 16)

- [Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places](https://mlsanity.com/paper/2506.14070/) (2025)
- [A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction](https://mlsanity.com/paper/2506.13678/) (2025)
- [MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering](https://mlsanity.com/paper/2508.11163/) (2025)
- [Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction](https://mlsanity.com/paper/2509.01613/) (2025)
- [SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction](https://mlsanity.com/paper/2510.01245/) (2025)
- [UrbanFM: Scaling Urban Spatio-Temporal Foundation Models](https://mlsanity.com/paper/2602.20677/) (2026)
- [The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation](https://mlsanity.com/paper/2504.07911/) (2025)
- [ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation](https://mlsanity.com/paper/2505.23048/) (2025)
- [Deep learning four decades of human migration](https://mlsanity.com/paper/2506.22821/) (2025)
- [Data-Driven Discrete Geofence Design Using Binary Quadratic Programming](https://mlsanity.com/paper/2509.24679/) (2025)
- [Deep Generative Model for Human Mobility Behavior](https://mlsanity.com/paper/2510.06473/) (2025)
- [Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models](https://mlsanity.com/paper/2603.00340/) (2026)
- [Learning Demographic-Conditioned Mobility Trajectories with Aggregate Supervision](https://mlsanity.com/paper/2603.03275/) (2026)
- [Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models](https://mlsanity.com/paper/2608.21567/) (2026)
- [Inferring Urban Mobility Interactions from Aggregated Dynamics](https://mlsanity.com/paper/2609.07349/) (2026)
- [LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling](https://mlsanity.com/paper/2609.22117/) (2026)

## Abstract (arXiv)

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behaviour and provide useful tools for modellers. Finally, we discuss how progress in these research directions may help us address some of the challenges our society faces today.