Characterization of crash-prone drivers in Saudi Arabia - A multivariate analysis

- A survey was conducted on traffic accidents and different related features.
- The factor analysis revealed that the drivers at fault and not at fault may have distinguishable profile.
- Principal component analysis found out suitable variables to characterize chauffeurs.
- Logit models were developed to quantify the effects of certain variables.
- This study may assist in profiling the chauffeurs involved in accidents.

This study conducted a survey of traffic crashes with the data collected from police stations in the three cities of Saudi Arabia involving different features related to crashes, drivers, vehicles, and understanding of traffic signs. Among the chauffeurs, drivers at fault and not at fault were separated and investigated through factor analysis for 19 parameters related to their background and knowledge of traffic signs. The data show that a particular age group and time of the day may provide more insights to characterize the overall crashes in these cities. The factor analysis shows that the drivers at fault and not at fault may have distinguishable profile. Logit models were developed to quantify the effects of these variables. The models show that driver's experience and knowledge of traffic signs for chauffeurs has positive impact on reducing faulty behavior of drivers. Approximately, 68%-74% of the original variables are required to characterize chauffeurs, indicating the possibility of data reduction in traffic safety monitoring program. This study may assist in profiling the chauffeurs involved in crashes and reducing the parameters to be monitored for traffic safety program. The recommendations of this study may be considered beneficial in making policy for licensing and hiring of chauffeurs.