Unsupervised embedding and similarity detection of microregions using public transport schedules

TL;DR

This paper introduces a novel method to embed public transport timetable data into vector space, enabling the comparison and evaluation of city regions based on transport availability using spatial indexing and similarity detection.

Contribution

It develops a new approach for representing public transport data as vectors and identifies regions with similar transport characteristics across multiple cities.

Findings

The method successfully embeds transport data into vector space.

It enables comparison of regions based on transport characteristics.

The approach can evaluate public transport quality in cities.

Abstract

The role of spatial data in tackling city-related tasks has been growing in recent years. To use them in machine learning models, it is often necessary to transform them into a vector representation, which has led to the development in the field of spatial data representation learning. There is also a growing variety of spatial data types for which representation learning methods are proposed. Public transport timetables have so far not been used in the task of learning representations of regions in a city. In this work, a method is developed to embed public transport availability information into vector space. To conduct experiments on its application, public transport timetables were collected from 48 European cities. Using the H3 spatial indexing method, they were divided into micro-regions. A method was also proposed to identify regions with similar characteristics of public transport offers. On its basis, a multi-level typology of public transport offers in the regions was defined. This thesis shows that the proposed representation method makes it possible to identify micro-regions with similar public transport characteristics between the cities, and can be used to evaluate the quality of public transport available in a city.