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

Abstract

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.