Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis

Saeid Pourroostaei Ardakani, Xiangning Liang, Kal Tenna Mengistu, Richard Sugianto So, Xuhui Wei, Baojie He, Ali Cheshmehzangi

Research output: Journal PublicationArticlepeer-review

14 Citations (Scopus)


Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.

Original languageEnglish
Article number5939
JournalSustainability (Switzerland)
Issue number7
Publication statusPublished - Apr 2023


  • big data
  • community-friendly
  • data-driven approach
  • machine learning
  • prediction model
  • road car accident
  • sustainable community

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


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