# A Survey on Deep Learning for Human Mobility

arXiv: https://arxiv.org/abs/2012.02825 · HTML: https://mlsanity.com/paper/2012.02825/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 30 of 88)

- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2304.06798/) (2023)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model](https://mlsanity.com/paper/2304.11582/) (2023)
- [ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model](https://mlsanity.com/paper/2404.15380/) (2024)
- [Continuous Trajectory Generation Based on Two-Stage GAN](https://mlsanity.com/paper/2301.07103/) (2023)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Large Language Models are Zero-Shot Next Location Predictors](https://mlsanity.com/paper/2405.20962/) (2024)
- [Context-aware multi-head self-attentional neural network model for next location prediction](https://mlsanity.com/paper/2212.01953/) (2022)
- [Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs](https://mlsanity.com/paper/2402.09836/) (2024)
- [UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces](https://mlsanity.com/paper/2411.03859/) (2024)
- [Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks](https://mlsanity.com/paper/2405.02357/) (2024)
- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [MobilityGPT: Enhanced Human Mobility Modeling with a GPT model](https://mlsanity.com/paper/2402.03264/) (2024)
- [Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis](https://mlsanity.com/paper/2405.17468/) (2024)
- [Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors](https://mlsanity.com/paper/2412.05000/) (2024)
- [Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization](https://mlsanity.com/paper/2506.11109/) (2025)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs](https://mlsanity.com/paper/2409.17495/) (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)
- [Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis](https://mlsanity.com/paper/2210.10010/) (2022)
- [CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches](https://mlsanity.com/paper/2309.11587/) (2023)
- [Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion](https://mlsanity.com/paper/2503.15779/) (2025)
- [Generating Synthetic Mobility Networks with Generative Adversarial Networks](https://mlsanity.com/paper/2202.11028/) (2022)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [TravelAgent: Generative Agents in the Built Environment](https://mlsanity.com/paper/2412.18985/) (2024)
- [UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings](https://mlsanity.com/paper/2407.12550/) (2024)
- [Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places](https://mlsanity.com/paper/2506.14070/) (2025)
- [Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning](https://mlsanity.com/paper/2202.13867/) (2022)

## Abstract (arXiv)

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.