Understanding the dynamics of road traffic accidents requires models that can address both the frequency and severity of such events. Traditional count models often fail to capture the excess zeros present in accident datasets, where many crashes result in no injuries. To address this limitation, we apply a Zero-Inflated Negative Binomial (ZINB) regression to model the number of victims in road accidents. This approach distinguishes between two processes: the likelihood of a crash being non-injurious and the count of victims in injurious crashes. Using real-world traffic accident data from California, we test multiple hypotheses concerning driver characteristics (age, gender, alcohol use, race), vehicle attributes (age, insurance status), and environmental conditions (weather). Our findings reveal nuanced relationships: for example, older vehicles and poor weather increase the likelihood of injury crashes, while younger drivers are more frequently involved in non-injurious incidents. Interestingly, male drivers are more likely to be involved in crashes without injuries, while crashes involving female drivers tend to result in a higher number of victims. These insights highlight the value of dual-structure models in traffic safety research and support more targeted and evidence-based policymaking in road safety and driver education.

The objective of this paper is to apply Association Rules to analyse Bakery basket sales data.

The objective of this paper is to apply dimensionality reduction methods to analyze data related to the satisfaction of US airline passengers.

The objective of this paper is to analyse and work on the clustering of airline fleet data.