mlsanityData to 7 Oct 2026

MobilityDL: A Review of Deep Learning From Trajectory Data

arXiv 2402.00732 ↗First posted 1 Feb 2024cs.LGcited by 6

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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).

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 72 references that are in the map. First posted on arXiv 1 Feb 2024.

Builds on

21 of 72 references.

References · 21

  1. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  2. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  3. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  4. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
  5. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  6. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  7. 2020Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic informationcited by 58
  8. 2020LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protectioncited by 57
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  12. 2019GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detectioncited by 16
9 more
  1. 2022How do you go where? Improving next location prediction by learning travel mode information using transformerscited by 12
  2. 2021Learning to Transfer for Traffic Forecasting via Multi-task Learningcited by 7
  3. 2022Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformerscited by 6
  4. 2022Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with Uncertainty Estimationcited by 3
  5. 2022Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twincited by 3
  6. 2022Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classificationcited by 2
  7. 2021Online Co-movement Pattern Prediction in Mobility Datacited by 1
  8. 2021Periodic Residual Learning for Crowd Flow Forecastingcited by 1
  9. 2022Meta-Learning over Time for Destination Prediction Taskscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 3.

6 papers.

  1. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  2. 2024UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddingscited by 4
  3. 2025Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Predictioncited by 0
  4. 2026Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Modelscited by 0
  5. 2026Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linkingcited by 0
  6. 2026Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systemscited by 0