Random forest models for motorcycle accident prediction using naturalistic driving based big data

Document Type

Article

Source of Publication

International Journal of Injury Control and Safety Promotion

Publication Date

1-1-2022

Abstract

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.

ISSN

1745-7300

Publisher

Informa UK Limited

Disciplines

Computer Sciences

Keywords

Karachi, machine learning, motorcycle accident prediction, Naturalistic driving based big data, random forest

Scopus ID

85145706368

Indexed in Scopus

yes

Open Access

no

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