Random forest models for motorcycle accident prediction using naturalistic driving based big data
Source of Publication
International Journal of Injury Control and Safety Promotion
Motorcycle accident studies usually rely upon data collected from road accidents collected through questionnaire surveys/police reports including characteristics of motorcycle riders and contextual data such as road environment. The present study utilizes big data, in the form of vehicle trajectory patterns collected through GPS, coupled with self-reported road accident information along with motorcycle rider characteristics to predict the likelihood of involvement of a motorcyclist in an accident. Random Forest-based machine learning algorithm is employed by taking inputs based on a variety of features derived from trajectory data. These features are mobility-based features, acceleration event-based features, aggressive overtaking event-based features and motorcyclists socio-economic features. Additionally, the relative importance of features is also determined which shows that aggressive overtaking event-based features have more impact on motorcycle accidents as compared to other categories of features. The developed model is useful in identifying risky motorcyclists and implementing safety measures focused towards them.
Informa UK Limited
Karachi, machine learning, motorcycle accident prediction, Naturalistic driving based big data, random forest
Outay, Fatma; Adnan, Muhammad; Gazder, Uneb; Baqueri, Syed Fazal Abbas; and Awan, Hammad Hussain, "Random forest models for motorcycle accident prediction using naturalistic driving based big data" (2022). All Works. 5571.
Indexed in Scopus