Each year, road traffic accidents continue to increase, with a majority attributed to unsafe driver behavior. Actions such as texting, speeding, or other forms of distraction place lives at risk whenever they occur. Although previous studies have examined the relationship between driver behavior and accident occurrence, limited research has focused specifically on developing cities such as Kigali, Rwanda. Historically, Kigali relied on diesel-powered buses that lacked advanced monitoring capabilities. The recent transition to electric buses now enables access to driver cabin CCTV footage, dashboard camera recordings, and telemetry data, allowing for improved monitoring of driver behavior. By analyzing these data sources, this study aims to identify associations between driver behavior and accident occurrences.

Driver footage provided by an electric bus company was analyzed to identify common behavioral distractions. The State Farm Distracted Driver Detection Kaggle dataset was used as a reference to guide activity labeling by identifying similarities between the recorded footage and known distracted driving behaviors. To support behavioral detection in video data, the deep learning-based Vision-Language Model AnomalyCLIP was applied to classify driver activities. Using global context optimization during training, AnomalyCLIP analyzes extracted video frames to categorize behaviors such as texting, drinking, eating, and self-grooming as abnormal behaviors.

Early findings indicate that driver behaviors can be classified into categories such as safe driving, texting, talking on the phone, drinking, eating, turning, self-grooming, improper seatbelt use, handling personal items, and interacting with passengers. This research seeks to develop accurate behavioral classifiers to support interventions aimed at reducing road accidents in Kigali.