Analyzing transitions, safety and influencing factors of driving patterns as multivariate short-term behaviors in freeway diverging areas.

Freeway diverging areas require drivers to decelerate, change lanes, and observe traffic simultaneously, resulting in elevated crash risk compared to basic segments. Previous studies have mainly focused on isolated maneuvers which cannot fully capture the integrated behavioral process during freeway diverging. A multivariate pattern perspective is therefore needed to better understand driver behavior in these areas. Using naturalistic driving data collected in Shanghai, this study investigates short-term driving patterns and their influencing factors in freeway diverging areas, alongside the microscopic interactive environments and longitudinal safety disparities among various driving patterns. A Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM) is used to segment multivariate driving sequences, and the resulting segments are classified into driving patterns using a PCA-based K-means clustering approach. Machine-learning models combined with SHapley Additive exPlanations (SHAP) are then applied to quantify the effects of driver characteristics and environmental factors on the duration ratios of different driving patterns. Three driving patterns are identified: high-speed deceleration (Pattern #0), visual-checking slight lane change (Pattern #1), and low-speed deceleration lane change (Pattern #2). Their spatial evolution shows a consistent trend: Pattern #0 dominates near the entrance of the diverging area and Pattern #2 becomes dominant near the taper section. Driving styles, traffic density, weather conditions, and the number of taper lanes have the strongest effects on pattern durations. Pattern #0 and Pattern #2 prevail in free-flow and car-following conditions respectively, and Pattern #1 shows the highest longitudinal conflict risk, whereas the 3.25-km scenario shows the lowest.