Profiling crash-associated factors and injury risk patterns among lost-in-thought (daydreaming) drivers: a combined cluster-sequence analysis approach
研究华盛顿州走神驾驶者的事故特征，通过聚类和序列分析识别出三种事故模式，并针对不同情境提出信号灯改进、弯道预警和新手培训等对策。
Cognitive distraction, particularly in the form of being lost in thought or daydreaming, is a significant yet underexamined contributor to traffic crashes. This study investigates crash patterns and risk profiles associated with drivers who experienced lost-in-thought distraction at the time of a crash in Washington State. Using cluster correspondence analysis (CCA), distinct associations between crash characteristics and driver attributes were uncovered. In addition, process mining was employed to identify typical sequences of crash events. Three meaningful clusters emerged. Cluster 1 involved crashes on road segments with speed limits exceeding 40 mph, lacking traffic control, and often involving male drivers in clear weather. Cluster 2 was marked by crashes at signalized intersections under partly cloudy or foggy conditions, with a higher likelihood of injury. Cluster 3 reflected rainy or low-light crashes involving young drivers on curved, divided, high-speed roads. Across all clusters, frequent crash sequences included collisions with vehicles in transport, parked cars, and fixed objects. Cluster 1 and Cluster 2 stood out for their distinct contextual characteristics. Cluster 1 crashes often involved crossing the center line, suggestive of deep cognitive distraction. In Cluster 2, crashes frequently occurred after drivers stopped at flashing red lights or stop signs, then proceeded, indicating momentary lapses in attention. The study also highlights the limitations of current crash data and emphasizes the need for standardized reporting of distraction-related incidents. Findings support context-specific countermeasures, such as signal enhancements, curve warnings, and distraction-focused training for novice drivers, to address the multifaceted risks associated with lost-in-thought crashes.