Coupling Machine Learning and Visualization Approaches to Individual- and Road-level Driving Behavior Analysis in a V2X Environment

Vehicle-to-Everything (V2X) infrastructure generates a vast amount of data from sensor-equipped vehicles and road infrastructure. The availability of such data provides new opportunities to explore and understand drivers' behaviors in diverse scenarios and how road characteristics affect driving actions. Effectively extracting and communicating driving behaviors from this huge volume of data in a timely manner presents a significant challenge. In this paper, we introduce a novel approach that combines machine learning and visualization techniques to support the exploration and analysis of driving behaviors using extensive vehicle status data from V2X infrastructure. Our approach consists of three core components: a spatiotemporal driving behavior feature learning model, a combined individual- and road-level driving behavior clustering method, and a multi-level visualization framework. The feature learning model for driving behavior detection employs a bi-directional spatiotemporal attentive long short-term memory network mechanism to extract essential individualized driving behaviors. Time series vehicle status and location data are then transformed and represented as feature vectors so that an unsupervised clustering method can be applied to identify representative types of driving behaviors and assign clustering labels to individual feature vectors. Furthermore, the model aggregates clustering labels by road, enabling the examination of driving patterns in the context of road conditions. To facilitate the interpretation of feature vectors and clustering results, our visualization framework offers a set of multi-level designs that support visual analytics on both trajectory and road levels. This approach contributes to driving analysis by utilizing massive real-world V2X data. It specifically addresses existing limitations in combining machine learning with visualization to support intelligent discovery of traffic patterns in an automatic manner. This not only enhances efficiency, but also ensures improved explainability and transparency for interpreting results.