A visualization-supported, hierarchical, action-learning model for driving behavior in a V2X environment

Understanding human driving decisions is crucial for intelligent transportation research. Most existing studies focus on individual vehicles in limited contexts, which restricts broader applicability of results. Leveraging Vehicle-to-Everything (V2X) infrastructure, this study introduces a machine learning framework to model driving actions and detect outliers across diverse environments. This approach features a semantically enabled clustering method that groups similar driving behaviors based on speed and actions. It also adds a time-series learning model to identify typical driving behaviors across various contexts, thereby enabling detection of abnormal driving actions. A suite of visual tools has been developed to help interpret driving patterns, and a case study using six months of data from a V2X pilot project in Tampa, Florida, demonstrates the framework's effectiveness in modeling human driving decisions. It also highlights discrepancies between context-appropriate driving behaviors and actual human actions to improve safety and efficiency for transportation planners and individual drivers.