A model for traffic incident prediction using emergency braking data

TL;DR

This paper introduces a traffic incident prediction model trained on emergency braking data instead of accidents, addressing data scarcity and achieving high accuracy with machine learning techniques.

Contribution

The study proposes a novel approach using emergency braking events for traffic incident prediction and evaluates preprocessing and modeling strategies for improved accuracy.

Findings

Random Forest achieved 85% accuracy.

Artificial data balancing improved model performance.

Prototype developed for Germany using vehicle, weather, and traffic data.

Abstract

This article presents a model for traffic incident prediction. Specifically, we address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. Based on relevant risk factors for traffic accidents and corresponding data categories, we evaluate different options for preprocessing sparse data and different Machine Learning models. Furthermore, we present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles as well as weather, traffic and road data, respectively. After model evaluation and optimisation, we found that a Random Forest model trained on artificially balanced (under-sampled) data provided the highest classification accuracy of 85% on the original imbalanced data. Finally, we present our conclusions and discuss further work; from gathering more data over a longer period of time to build stronger classification systems, to addition of internal factors such as the driver's visual and cognitive attention.