Integrating short-term behavioral patterns for drivers’ stop/go behavior prediction when approaching signalized intersections

A deep understanding of short-term behavioral patterns is critical for predicting stop/go behavior, especially in complex scenarios such as approaching signalized intersections demanding stop/go decisions. Although driving behavior has been studied from static and dynamic perspectives, short-term behavioral-pattern changes during signal phase transitions at intersections and their contribution to stop/go prediction remain underexplored. This study proposes a stop/go prediction model integrating short-term behavioral-pattern identification using UAV-collected trajectories from urban intersections. A multi-type feature space is first constructed from continuous trajectories to represent short-term behavior. Based on this representation, the trajectories are segmented into driving behavior primitives using Bayesian Model-based Agglomerative Sequence Segmentation (BMASS). The resulting primitive segments are then grouped into discrete primitive types using a Gaussian Mixture Model (GMM), from which primitive-label sequences are obtained. Based on these sequences, Latent Dirichlet Allocation (LDA) extracts short-term behavioral-pattern proportions, while a first-order Markov chain captures behavioral-pattern transition statistics. Finally, these behavioral-pattern features are fused with trajectory-based statistical features and fed into an XGBoost model for stop/go prediction. Compared with the trajectory-only baseline, the full framework improves accuracy from 0.913 to 0.971. The results indicate that short-term behavioral-pattern features offer incremental predictive value beyond trajectory- and primitive-level information.