Lee Voon Hee, Norazlina Khamis, Rafidah Md Noor, Samsul Ariffin Abdul Karim, Poppy Puspitasari
Road traffic accidentsRoad traffic accidents (RTAs) (RTAs) claim approximately 1.3 million lives globally each year, prompting the United Nations to target a 50% reduction in fatalitiesFatalities and injuries by 2030 through the Sustainable Development Goal (SDG). RTA's often bring negative social and economic impacts to individuals, families and the society; worst being loss of human life. Factors influencing RTA mortalityMortality are numerous, multifaceted as well as ever changing falling into areas of human, road, vehicle, and environment related elements. Recognizing and comprehending the evolving dynamics of critical factorsCritical factors associated with fatal RTAs is crucial, which can empower relevant authorities, to develop targeted and current preventive measures. Machine learningMachine learning techniques are gaining popularity for identifying risk factors and predicting RTA fatalities due to their adaptability and fewer assumptions compared to statistical models. This chapter reviews various techniques for predicting fatality of RTA. A taxonomyTaxonomy and categorization of identified risk factors has been proposed; offers a structured framework for understanding and addressing RTA risks for the development of effective preventive and reactive measuresPreventive and reactive measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia; School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, Kedah Darul Aman, Sintok, 06010, Malaysia; LEAD Research Lab, Faculty of Computing and Informatics, Universiti Malaysia Sabah, UMS, Jalan, Sabah, Kota Kinabalu, 88400, Malaysia; Mechanical and Industrial Engineering Department, Universitas Negeri Malang, Semarang St. 5 Malang, East Java, 65144, Indonesia; Center of Advanced Materials for Renewable Energy, Universitas Negeri Malang, Semarang St. No, 5 Malang, East Java, 65144, Indonesia
