Vehicle trajectory-based prediction of traffic conflicts on sharp horizontal curves.

Abstract

Objective: The traffic conflict situations at sharp curve sections are evaluated by analyzing vehicle trajectory data during navigation through these hazardous road segments.

Methods: This study develops a methodology for quantifying traffic conflict probabilities in curve scenarios based on multi-source trajectory data acquisition. Vehicle movement trajectories through curves are captured via integrated UAV aerial photography systems and onboard vehicle recorders. High-precision spatiotemporal coordinates with dynamic parameters (instantaneous velocity and acceleration) are extracted using the professional trajectory analysis software. To address noise interference in raw trajectory data, a Kalman filtering algorithm is implemented for optimal motion state estimation and data smoothing. At the model architecture level, we propose a CNN-LSTM hybrid predictive model that synergistic-ally combines the spatial-temporal feature extraction capabilities of convolutional neural networks with the temporal dependency modeling advantages of long short-term memory networks, enabling end-to-end learning for quantitative trajectory conflict prediction. To validate model generalizability, this study concurrently constructed multiple benchmark models-including Support Vector Machine (SVM), Gradient Boosted Trees (XGBoost), GNN-LSTM, Vanilla LSTM, and Bi-LSTM-for comparative experiments. The evaluation framework employed a rigorous multi-dimensional validation protocol from machine learning, assessing all models not only by fundamental classification accuracy but also through fine-grained efficacy metrics (Precision, Recall, F1-score). Results demonstrated the superior performance of the hybrid CNN-LSTM model in predicting traffic conflicts at curves. Ultimately, curve-specific conflict probabilities were derived by applying the CNN-LSTM model to experimental data analysis. The generalization performance under class-imbalanced conditions was quantified using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), while prediction accuracy was validated through metrics including classification accuracy. This establishes a comprehensive multi-capability evaluation framework covering model stability, sensitivity, and generalization capability.

Results: Empirical results confirm the CNN-LSTM model's superior performance in sharp-curve conflict prediction, achieving a mean accuracy exceeding 85%, precision above 82.7%, recall over 89.9%, and F1-score surpassing 86.1%, complemented by a 93.5% or higher average AUC-ROC that demonstrates robust generalization in class-imbalanced scenarios. These metrics collectively substantiate its exceptional spatiotemporal feature extraction capability and precise risk evolution pattern fitting, enabling enhanced representation of interactive vehicle conflicts in complex environments.

Conclusions: The research outcomes provide intelligent decision support for geometric optimization design of sharp curve sections and establish a reliable theoretical foundation for developing real-time dynamic risk warning systems. This work holds significant practical value for advancing the transformation of intelligent transportation management systems toward data-driven paradigms.