Unsupervised Learning Approach for Risky Driving Behavior Identification on Expressways in C-ITS Environments

Abstract

Understanding the intricate relationship between driving behaviors, traffic crashes, and human factors is paramount in enhancing road safety. Human error, often stemming from risky driving behaviors, contributes significantly to traffic crashes. Identifying and mitigating these behaviors through advanced technologies and data analysis has become an important concern in the field of traffic safety management. This study introduces an unsupervised learning algorithm for detecting risky driving behaviors on expressways within Cooperative Intelligent Transport Systems (C-ITS) environments, employing deep clustering techniques to analyze individual driving patterns from Probe Vehicle Data (PVD). Utilizing data from 116 vehicles, including buses and heavy trucks, a Convolutional Neural Network (CNN)-based autoencoder was employed to extract latent hierarchical features, facilitating the clustering of similar driving patterns. Elementary Driving Behaviors (EDBs) were identified for different vehicle types and driving statuses, serving as a foundation for detecting risky driving behaviors against the proposed criteria. The research revealed a clear positive correlation between detected risky driving behaviors and traffic crashes across the vehicle types. Furthermore, when comparing our model’s criteria with traditional safety indexes, our proposed model demonstrated stronger correlations with traffic crashes, indicating its effectiveness in expressway driving environments. This research not only introduces a novel method for identifying risky driving behaviors but also underscores the importance of tailored traffic safety interventions in enhancing C-ITS environments.