Motorbike Accident Severity Analysis Dataset

The dataset used in this study focuses on road traffic accidents involving motorcyclists in Bangladesh. It comprises various attributes related to the biker, road, environmental conditions, and accident outcomes. The dataset contains both categorical and numerical features, making it suitable for both classification and regression analysis.

Key features include:

Biker Attributes: Biker_Age, Biker_Occupation, Biker_Education_Level, Riding_Experience, Daily_Travel_Distance, Talk_While_Riding, Smoke_While_Riding, Wearing_Helmet, Motorcycle_Ownership, Valid_Driving_License.

Bike and Road Conditions: Bike_Condition, Road_Type, Road_condition, Weather, Time_of_Day, Traffic_Density, Speed_Limit, Bike_Speed, Number_of_Vehicles.

Behavioral Factor: Biker_Alcohol.

Target Variable: Accident_Severity (e.g., No Accident, Minor, Moderate, Severe).

The dataset was collected through field surveys and interviews with accident victims and bystanders at various accident sites across Bangladesh. It reflects real-world scenarios and human behavior patterns associated with motorbike accidents, making it valuable for training machine learning models aimed at predicting accident severity and identifying contributing risk factors.