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

TL;DR

This paper introduces gtfs2vec, a method that creates embeddings of city regions based on public transport data, enabling comparison and clustering of microregions across European cities.

Contribution

The paper presents a novel approach using deep neural network embeddings and hierarchical clustering to analyze and compare public transport availability in city microregions.

Findings

Embeddings effectively capture transport characteristics.

Clusters align with city-specific transport features.

Method enables identification of similar transport regions.

Abstract

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.