Deep learning from trajectory data

I’ve previously written about Movement data in GIS and the AI hype and today’s post is a follow-up in which I want to share with you a new review of the state of the art in deep learning from trajectory data.

Our review covers 8 use cases:

1. Location classification
2. Arrival time prediction
3. Traffic flow / activity prediction
4. Trajectory prediction
5. Trajectory classification
6. Next location prediction
7. Anomaly detection
8. Synthetic data generation

We particularly looked into the trajectory data preprocessing steps and the specific movement data representation used as input to train the neutral networks:

On a completely subjective note: the price for most surprising approach goes to natural language processing (NLP) Transfomers for traffic volume prediction.

The paper was presented at BMDA2023 and you can watch the full talk recording here:

References

Graser, A., Jalali, A., Lampert, J., Weißenfeld, A., & Janowicz, K. (2023). Deep Learning From Trajectory Data: a Review of Neural Networks and the Trajectory Data Representations to Train Them. Workshop on Big Mobility Data Analysis BMDA2023 in conjunction with EDBT/ICDT 2023.

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This post is part of a series. Read more about movement data in GIS.