A Bayesian Hierarchical Framework for Reliable Intersection Turning Control Extraction From Crowdsourced Trajectories

ABSTRACT

Accurate and dynamic Intersection Turning Control (ITC) information is a fundamental component for intelligent traffic management. However, widely used open‐source maps, such as OpenStreetMap, often suffer from incomplete attribute data and update lags. While crowdsourced trajectory data offers a promising solution, existing extraction methods typically rely on absolute frequency thresholds, lacking a systematic quantification of uncertainty. To address this, we propose an integrated framework for ITC extraction and confidence evaluation using mobile navigation trajectories. The framework first abstracts intersection geometry and refines trajectory segments to ensure spatial alignment. Building on this, a Balanced Random Forest model is utilized to robustly classify movement modes. Crucially, to quantify the reliability of these extracted rules, we introduce a Bayesian hierarchical confidence evaluation model that infers the posterior probability of movement legitimacy from the observed data. This probabilistic approach explicitly quantifies the reliability of each turning rule, effectively distinguishing between rare legal maneuvers and anomalous violations. Experimental results from a case study in Shanghai demonstrate that the proposed framework achieves an overall extraction accuracy of 93.75%, with an optimal confidence threshold identified between 0.60 and 0.65. The study validates that incorporating confidence evaluation significantly enhances the semantic correctness of map updates, providing a robust solution for maintaining real‐time, lane‐level urban road networks.