Data-Driven Analysis for Road Safety: A Clustering Approach Using K-Means

Abstract

Ensuring road safety is a fundamental challenge in transportation systems, particularly on roads with high traffic volumes and varying geometric features. This study presents a general road safety analysis framework that exploits different types of data on traffic, geometry, and crashes to identify the portions of the network to be enforced with safety measures and potentially support drivers with an advanced onboard speed advisory system. The objective is to identify and classify homogeneous road elements to identify the portions of the road network that need additional safety measures and that have to be indicated to drivers according to their driving attitude and risk perception. Using Floating Car Data (FCD), road geometry data, and historical crash reports, multiple clustering tests were conducted to group road segments where factors such as curvature, speed variability, and risk indicators contribute to increased crash probability. The proposed framework is applied on Via Pontina, a critical and strongly congested rural road in Italy, and among several clustering tests performed, four were selected based on their interpretability. The results illustrate the effectiveness of a data-driven approach for road safety assessment, offering valuable insights for transportation engineers and policymakers.