Integrating gender, vehicle type, and temporal dynamics in road traffic accident analysis: a multivariate statistical approach.

Abstract

Road traffic accident (RTA) is a global public health concern, particularly in developing regions where infrastructure, enforcement, and driver behavior vary widely. This study investigates the influence of gender, vehicle type, and temporal dynamics on road traffic accidents within Gombe State, Nigeria, in 2025. A quantitative research design and hierarchical Analysis of Variance (ANOVA) was used to examine how these factors interact and contribute to the frequency and severity of accidents. Data were obtained from official records, including police and hospital reports, and analyzed for patterns in accident trends across different months, vehicle types, and gender categories. Findings reveal significant differences in accident rates between males and females, with males recording a higher mean number of accidents (M = 34.03, SD = 25.57) compared to females (M = 10.33, SD = 9.57), with a p-value of 0.000, indicating statistical significance. Vehicle type also emerged as a critical factor, with motorcycle accidents having the highest mean (M = 35.46, SD = 25.97), followed by moving vehicles (M = 29.29, SD = 15.02), and pedestrian-related accidents (M = 1.79, SD = 1.82), all with a p-value of 0.000, showing significant differences between these groups. Temporal dynamics, such as monthly variations, showed no statistically significant effect on accident rates (p = 0.634), despite noticeable fluctuations in mean accident counts across months, with January (M = 25.00, SD = 26.71) and June (M = 28.00, SD = 29.93) having the highest frequencies, and September recording the lowest (M = 13.67, SD = 13.05). The study underscores the need for targeted interventions, such as enhanced motorcycle safety regulations and gender-specific awareness campaigns, to mitigate accident risks.