Does gender affect the identification of road crash occurrences? An Overview and a comparative study
Keywords:
Road safety, Accident severity, Risk factors, Machine learningAbstract
Around the world, traffic accidents are regarded as a large and significant cause of injury and death. Nearly 3,700 people are killed and over 1.3 million individuals lose their lives in collisions involving trucks, vehicles, buses, motorcycles, or pedestrians. This article tries to identify the important causes of traffic accidents involving both men and women as well as the methods that have been suggested and put into practice based on the literature study. In order to determine how gender impacts the frequency of traffic accidents, a survey and a comparative study were conducted in this work. According to the findings, the factors that have been studied for accident causes in urban areas include speed, age, and gender. On rural roads, speed has been recognized as the primary cause of collisions, particularly among men, while age and lack of experience have been noted as influencing factors in women's traffic incidents. Because machine learning models are effective at predicting crashes, they have been utilized in the majority of research
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