Modeling vehicle interaction strength in merge zones for merging risk evolution analysis

Driving risks during the merging process are complex and highly dynamic, with vehicle interactions serving as the fundamental driver of risk evolution. Existing vehicle interaction models predominantly rely on the discrete classification of interaction patterns, resulting in a lack of temporal analysis regarding the evolution of driving risk. To address this limitation, this study constructs a vehicle interaction model for merging zones employing kinematic approaches, characterizing the dynamic competition-cooperation relationship by quantifying interaction strength (IS). Subsequently, an explanatory framework employing locally weighted SHAP is established to investigate the mechanisms through which kinematic features drive IS. Finally, the Granger causality test is utilized to uncover the influence patterns of interaction strength on the evolution of merging risk. Validation results using trajectory data from merging zones demonstrate that: (1) the proposed vehicle interaction model successfully quantifies interaction strength, with its validity corroborated by statistical test results; (2) THW serves as the optimal parameter for regulating the competition-cooperation phase transition, and the adoption of a decisive and accelerated merging strategy by the merging vehicle facilitates the occurrence of cooperative merging; and (3) IS is statistically confirmed to Granger-cause merging risk. The merging risk generally lags behind variations in IS while maintaining a consistent directional trend, exhibiting a more rapid response under high-intensity interaction conditions. The proposed model provides a methodological reference for analyzing the evolution of merging risk, and the findings offer an empirical basis for optimizing the interactive merging decision-making of autonomous vehicles (AVs).