Young novice driver and police officer interactions: A study of sociodemographics, reasons for traffic stops, and outcomes

Traffic stops are a common form of driver–police interactions, yet those involving young novice drivers remain understudied. This study examined the sociodemographic patterns in interactions between young novice drivers and police officers in Charlotte, North Carolina, analyzing the reasons for traffic stops and their outcomes. A binary logistic regression (BLR) model and association rule mining (ARM) were applied. The BLR model indicated that, compared to vehicle regulatory reasons, factors such as unsafe movements and speeding are associated with higher odds of stopped drivers being young novices rather than adults aged +18. Stopped non-White drivers are generally less likely to be young novices compared to White drivers, except for Hispanic drivers, who show higher odds. Additionally, stopped drivers who receive a written warning rather than a verbal one or interact with officers with 15–24 years of service, compared to junior officers, are more likely to be young novices. The ARM results revealed that young White novice drivers are frequently stopped for vehicle equipment issues. Interactions between White male officers and non-Hispanic young Black male novice drivers often result in arrests, especially when initiated by searches, involving officers with 1–3 or 4–14 years of service. Traffic stops involving young Asian female drivers mostly stem from stop light/sign violations, typically handled by White male officers. The findings can inform safety education for young novice drivers and raise awareness of traffic stop trends across police departments, fostering responsible driving behaviors and adherence to traffic laws and contributing to socially sustainable mobility.