Predicting Highway Risk Event with Trajectory Data: A Joint Approach of Traffic Flow and Vehicle Kinematics

Real-time collision risk prediction is essential for improving highway safety and reducing traffic accidents. However, previous studies have mainly used crash data and associated spatially discrete and temporally continuous traffic data, overlooking the potential of vehicle trajectory data, which provides comprehensive spatio-temporal information to characterize traffic near a specific location. Moreover, researchers have typically focused on either traffic flow characteristics or inter-vehicle microscopic kinematic characteristics for real-time risk prediction, with a dearth of studies integrating these two aspects. Given that risk events transpire more frequently than accidents and exhibit a strong correlation with them, it is imperative to concentrate more on risk events to proactively diminish crash probabilities. This study introduces a novel approach that extracts traffic flow and inter-vehicle kinematic features from risk events. It also provides a comparative analysis of the effectiveness of five machine-learning methods (Logistic Regression, K-Nearest Neighbors, eXtreme Gradient Boosting, Random Forests, and Multilayer Perceptron) and two data-processing strategies (oversampling and undersampling) in addressing risk identification and prediction issues. The results showed that (1) the synergistic use of traffic flow and inter-vehicle kinematic features surpasses the use of a single feature in identifying and predicting risks; (2) The eXtreme Gradient Boosting model, trained on the undersampled dataset, emerges as the optimal model for risk identification, boasting an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.976 and an F1 score of 0.604; (3) The RF model exhibits commendable performance under both risk prediction conditions (5 s ahead prediction and 10 s prediction), demonstrating the highest performance with F1 scores of 0.377 and 0.374, respectively. Additionally, it was discovered that the resampling strategy does not always prove effective in developing risk analysis models and should be chosen based on the model’s characteristics and target metrics. This offers valuable insights into the selection of data-processing strategies when handling unbalanced data. Finally, the study’s limitations and potential enhancements are discussed.