Prediction of fatal and major injuries of drivers, cyclists, and pedestrians in collisions

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

• Record URL:
  http://dx.doi.org/10.7307/PTT.V32I1.3134
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03535320
• Supplemental Notes:
  • © 2020 Dalia Shanshal et al.
• Authors:
  • Shanshal, Dalia
  • Babaoglu, Ceni
  • Başar, Ayşe
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 39-53
• Serial:
  • PROMET-Traffic & Transportation
  • Volume: 32
  • Issue Number: 1
  • Publisher: University of Zagreb
  • ISSN: 0353-5320
  • EISSN: 1848-4069
  • Serial URL: https://traffic2.fpz.hr/index.php/PROMTT
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Behavior; Crash causes; Crash data; Crash injuries; Cyclists; Data analysis; Drivers; Fatalities; Injury severity; Machine learning; Mathematical prediction; Pedestrians; Traffic crashes
• Geographic Terms: Toronto (Canada)
• Subject Areas: Highways; Pedestrians and Bicyclists; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01748421
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 24 2020 9:15AM