Fatma Outay, Muhammad Adnan, Uneb Gazder, Syed Fazal Abbas Baqueri, Hammad Hussain Awan
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引用次数: 2
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.
期刊介绍:
International Journal of Injury Control and Safety Promotion (formerly Injury Control and Safety Promotion) publishes articles concerning all phases of injury control, including prevention, acute care and rehabilitation. Specifically, this journal will publish articles that for each type of injury: •describe the problem •analyse the causes and risk factors •discuss the design and evaluation of solutions •describe the implementation of effective programs and policies The journal encompasses all causes of fatal and non-fatal injury, including injuries related to: •transport •school and work •home and leisure activities •sport •violence and assault