Applying machine learning to predict behavior of bus transport in Warsaw, Poland

TL;DR

This paper explores the use of machine learning to model and predict bus delays in Warsaw, Poland, utilizing detailed GPS data collected from all buses to understand their behavior.

Contribution

It presents initial results demonstrating the feasibility of applying machine learning to predict bus delays using real-time GPS data in Warsaw.

Findings

Preliminary models can predict bus delays with some accuracy.

GPS data provides valuable insights into bus movement patterns.

The approach shows potential for improving public transport reliability.

Abstract

Nowadays, it is possible to collect precise data describing movements of public transport. Specifically, for each bus (or tram) geoposition data can be regularly collected. This includes data for all buses in Warsaw, Poland. Moreover, this data can be downloaded and analyzed. In this context, one of the simplest questions is: can a model be build to represent behavior of busses, and predict their delays. This work provides initial results of our attempt to answer this question.