# gtfs2vec -- Learning GTFS Embeddings for comparing Public Transport Offer in Microregions

arXiv: https://arxiv.org/abs/2111.00960 · HTML: https://mlsanity.com/paper/2111.00960/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BusTr: Predicting Bus Travel Times from Real-Time Traffic](https://mlsanity.com/paper/2007.00882/) (2020)

## Built on by (most cited first, 3 of 3)

- [SRAI: Towards Standardization of Geospatial AI](https://mlsanity.com/paper/2310.13098/) (2023)
- [Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments](https://mlsanity.com/paper/2409.06730/) (2024)
- [Scalable Transit Delay Prediction at City Scale: A Systematic Approach with Multi-Resolution Feature Engineering and Deep Learning](https://mlsanity.com/paper/2601.18521/) (2026)

## Abstract (arXiv)

We selected 48 European cities and gathered their public transport timetables in the GTFS format. We utilized Uber's H3 spatial index to divide each city into hexagonal micro-regions. Based on the timetables data we created certain features describing the quantity and variety of public transport availability in each region. Next, we trained an auto-associative deep neural network to embed each of the regions. Having such prepared representations, we then used a hierarchical clustering approach to identify similar regions. To do so, we utilized an agglomerative clustering algorithm with a euclidean distance between regions and Ward's method to minimize in-cluster variance. Finally, we analyzed the obtained clusters at different levels to identify some number of clusters that qualitatively describe public transport availability. We showed that our typology matches the characteristics of analyzed cities and allows succesful searching for areas with similar public transport schedule characteristics.