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 and Ali Cheshmehzangi ()
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Saeid Pourroostaei Ardakani: School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
Xiangning Liang: School of Computer Science, University of Nottingham, Ningbo 315100, China
Kal Tenna Mengistu: School of Computer Science, University of Nottingham, Ningbo 315100, China
Richard Sugianto So: School of Computer Science, University of Nottingham, Ningbo 315100, China
Xuhui Wei: School of Computer Science, University of Nottingham, Ningbo 315100, China
Baojie He: Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China
Ali Cheshmehzangi: Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Hiroshima 739-8530, Japan

Sustainability, 2023, vol. 15, issue 7, 1-15

Abstract: 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.

Keywords: machine learning; road car accident; prediction model; big data; sustainable community; data-driven approach; community-friendly (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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