Macro Impact Factors of Road Traffic Accidents and Prediction Analysis of Accident Fatalities
Runze Li, Baohua Guo · Advances in engineering research/Advances in Engineering Research · 2024
This study aims to analyze and predict the number of deaths in road traffic accidents through multiple linear regression models, with a focus on examining macro influencing factors.The article collects multiple macroeconomic and social variables that affect the mortality rate of traffic accidents, including Gross Domestic Product (GDP), motor vehicle ownership, road mileage, number of motor vehicle drivers, and year-end total population.Firstly, the data was preprocessed, including missing value processing and outlier detection.Subsequently, a multiple linear regression model was used to model the data, and the assumptions of the model were validated.Through stepwise regression analysis, significant influencing factors were screened and the final regression model was constructed.The goodness of fit and predictive performance of the model are evaluated through cross validation and independent test sets.The results show that the number of motor vehicles, the length of highways open to traffic, and the number of motor vehicle drivers are the main macro factors affecting the number of deaths in traffic accidents.Based on this model, we can effectively predict the number of deaths in traffic accidents in the future, providing scientific basis for traffic management departments to formulate corresponding preventive measures.Research has shown that multiple regression models have high application value and accuracy in analyzing traffic accidents at the macro level.