The application of data mining techniques to model visual distraction of bicyclists

• A novel approach to modelling visual distraction of bicyclists is presented.
• A bicycle simulator for road traffic safety research is presented.
• We combine random forests for variable selection and support vector machine for modelling.
• A combination of qualitative and quantitative data provides the best results.
• The method may be used for automated vehicles to detect distracted bicyclists.

This paper presents a novel approach to modelling visual distraction of bicyclists. A unique bicycle simulator equipped with sensors capable of capturing the behaviour of the bicyclist is presented. While cycling two similar scenario routes, once while simultaneously interacting with an electronic device and once without any electronic device, statistics of the measured speed, head movements, steering angle and bicycle road position along with questionnaire data are captured. These variables are used to model the self-assessed distraction level of the bicyclist. Data mining techniques based on random forests, support vector machines and neural networks are evaluated for the modelling task. Out of the total 71 measured variables a variable selection procedure based on random forests is able to select a fraction of those and consequently improving the modelling performance. By combining the random forest-based variable selection and support vector machine-based modelling technique the best overall performance is achieved. The method shows that with a few observable variables it is possible to use machine learning to model, and thus predict, the distraction level of a bicyclist.