학술논문
Random forest models for motorcycle accident prediction using naturalistic driving based big data.
Document Type
Academic Journal
Author
Outay F; College of Technological Innovation (CTI), Zayed University, Dubai.; Adnan M; Transportation Research Institute (IMOB), Hasselt University, Belgium.; Gazder U; Department of Civil Engineering, University of Bahrain, Bahrain.; Baqueri SFA; Department of Civil Engineering, DHA Suffah University, Karachi, Pakistan.; Awan HH; The University of Lahore (Islamabad Campus), Islamabad, Pakistan.
Source
Publisher: Informa Healthcare Country of Publication: England NLM ID: 101247254 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1745-7319 (Electronic) Linking ISSN: 17457300 NLM ISO Abbreviation: Int J Inj Contr Saf Promot Subsets: MEDLINE
Subject
Language
English
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.