# MobilityDL: A Review of Deep Learning From Trajectory Data

arXiv: https://arxiv.org/abs/2402.00732 · HTML: https://mlsanity.com/paper/2402.00732/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 72 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [AdaNet: Adaptive Structural Learning of Artificial Neural Networks](https://mlsanity.com/paper/1607.01097/) (2016)
- [A Review of Location Encoding for GeoAI: Methods and Applications](https://mlsanity.com/paper/2111.04006/) (2021)
- [Leveraging Language Foundation Models for Human Mobility Forecasting](https://mlsanity.com/paper/2209.05479/) (2022)
- [GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection](https://mlsanity.com/paper/1912.00682/) (2019)
- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [Learning to Transfer for Traffic Forecasting via Multi-task Learning](https://mlsanity.com/paper/2111.15542/) (2021)
- [Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers](https://mlsanity.com/paper/2204.13326/) (2022)
- [Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with Uncertainty Estimation](https://mlsanity.com/paper/2205.05404/) (2022)
- [Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin](https://mlsanity.com/paper/2207.03575/) (2022)
- [Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification](https://mlsanity.com/paper/2204.11855/) (2022)
- [Online Co-movement Pattern Prediction in Mobility Data](https://mlsanity.com/paper/2102.08870/) (2021)
- [Periodic Residual Learning for Crowd Flow Forecasting](https://mlsanity.com/paper/2112.06132/) (2021)
- [Meta-Learning over Time for Destination Prediction Tasks](https://mlsanity.com/paper/2206.14801/) (2022)

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

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings](https://mlsanity.com/paper/2407.12550/) (2024)
- [Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Prediction](https://mlsanity.com/paper/2510.26940/) (2025)
- [Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models](https://mlsanity.com/paper/2603.00340/) (2026)
- [Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking](https://mlsanity.com/paper/2608.08646/) (2026)
- [Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems](https://mlsanity.com/paper/2608.14570/) (2026)

## Abstract (arXiv)

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).