A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

Abstract

For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models. An analytical framework was proposed, further illustrated through a detailed case study. In the case study, real vehicle trajectory data from HighD were processed and applied, which can be aggregated to extract both traffic state variables and corresponding conflict data during a specific time interval. As for the conflict detection, Time-to-Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) indicators were utilized to identify risky conditions. Based on the proposed approach, the selection for the optimal prediction model was conducted, and the variable importance in conflict prediction within the optimal models derived from the SM-OPMS was also investigated. Finally, a comparative analysis with the enumeration-based optimal prediction model selection (E-OPMS) approach was conducted to validate the superiority of the proposed approach. Results indicate that SM-OPMS outperforms E-OPMS in optimal model selection, notably enhancing computational efficiency by up to 94.03%, while maintaining prediction precision within a maximum reduction of only 7.91%. The significance of the SM-OPMS approach is revealed by its comprehensive selection of the optimal prediction models for specific traffic scenarios, taking into account both prediction efficiency and precision simultaneously. The proposed approach is expected to contribute to the development of real-time conflict prediction in the future.