Machine learning in road accident research: decision trees describing road accidents during cross-flow turns

Abstract

In-depth studies of behavioural factors in road accidents using conventional methods are often inconclusive and costly. In a series of studies exploring alternative approaches, 200 cross-flow junction road accidents were sampled from the files of Nottinghamshire Constabulary, UK, coded for computer analysis using a specially devised ‘Traffic Related Action Analysis Language’, and then examined using different computational and statistical techniques. The present study employed an AI machine-learning method based on Quinlan's ‘ID3’ algorithm to create decision trees distinguishing the characteristics of accidents that resulted in injury or in damage only; accidents of young male drivers; and those of the relatively more and less dangerous situations. For example the severity of accidents involving turning onto a main road could be determined with 79% accuracy from the nature of the other vehicle, season, junction type, and whether the Turner failed to notice another road user. Accidents involving young male drivers could be identified with 77% accuracy by knowing if the junction was complex, and whether the Turner waited or slowed before turning.

Keywords:

• Causation
• Road Junction
• Machine Learning
• Police Records.