Data-Driven Analysis of Sudden Braking Events in Helsinki’s Public Transport

Abstract

This thesis explores how real-time data from public transport services can help to identify and predict sudden braking events in Helsinki’s bus network. Sudden braking is often a sign of risky traffic situations, especially for pedestrians and cyclists. By studying these moments, we can better understand where and when safety issues are likely to happen, and what might be causing them.

The research uses open data from Helsinki Region Transport (HSL), focusing on vehicle movement and braking patterns over multiple days. By analysing the time, location, and vehicle behaviour during these events, this research aims to detect patterns that show risk areas. Bus location and movement data were combined with weather conditions to detect patterns behind sudden braking. The outcomes are supported with visualizations, such as heatmaps and cluster-based spatial analysis, to highlight risk zones for sudden braking. These insights could help city planners and traffic safety experts make streets safer, especially for those walking or biking. The goal was to predict when and where a sudden braking event might occur.

The finding emphasizes the importance of using open urban mobility data to improve traffic safety and protect vulnerable road users.