This study explores the relationship between age, gender, and road accident rates, using census data to predict accident frequencies. It finds that over 62% of accidents occur within 11 km of a driver's home, highlighting the "close to home" effect. Using regression-based machine learning models, the study analyzes data from England, UK, and finds that census data explains over 28% of the variance in road accident rates per capita. The research suggests that demographic factors like age and gender significantly influence road accident rates, with younger male drivers and those aged 35–49 being at higher risk. The study also identifies that areas with a higher proportion of males aged 20–34 and a higher percentage of people aged 35–49 have higher accident rates. The findings support the use of census data for road safety research, as it provides a large-scale, accessible method to estimate accident risks and explore indirect relationships between behaviors and road safety. The study's results align with previous literature, demonstrating that demographic data can effectively predict accident trends, even when more detailed driver behavior data is not available. The research contributes to the understanding of road safety by highlighting the importance of demographic factors and the potential of census data in informing policy and planning decisions.This study explores the relationship between age, gender, and road accident rates, using census data to predict accident frequencies. It finds that over 62% of accidents occur within 11 km of a driver's home, highlighting the "close to home" effect. Using regression-based machine learning models, the study analyzes data from England, UK, and finds that census data explains over 28% of the variance in road accident rates per capita. The research suggests that demographic factors like age and gender significantly influence road accident rates, with younger male drivers and those aged 35–49 being at higher risk. The study also identifies that areas with a higher proportion of males aged 20–34 and a higher percentage of people aged 35–49 have higher accident rates. The findings support the use of census data for road safety research, as it provides a large-scale, accessible method to estimate accident risks and explore indirect relationships between behaviors and road safety. The study's results align with previous literature, demonstrating that demographic data can effectively predict accident trends, even when more detailed driver behavior data is not available. The research contributes to the understanding of road safety by highlighting the importance of demographic factors and the potential of census data in informing policy and planning decisions.