Enhancing real-time conflict identification using trajectory data: Exploring the impact of interactions among traffic flow state variables.

Objective: This study aims to address the limitations of using historical crash data and trajectory data for crash and conflict identification. Specifically, it focuses on enhancing real-time conflict identification by investigating the influence of traffic flow state variables and their interactions on conflicts.

Methods: Vehicle trajectory data from HighD were processed, allowing extraction of traffic flow state and corresponding conflict during a specific time interval (10 s). Logistic regression models were further used to verify the impact of variables, including interaction terms, on the conflicts for different lane categories (inner, middle, and outer lanes). Additionally, machine learning techniques were employed to compare conflict identification performance including or excluding variable interactions.

Results: The interaction terms of the traffic flow state variables have significant effects on the conflicts for different categories of lanes. It is therefore essential to consider both the individual effects of traffic variables and their interaction effects to analyze conflict risk. Considering variable interactions leads to improved conflict identification accuracy and reduced identification error rates in comparison to the condition where interaction items are not taken into account.

Conclusions: The interaction terms of traffic flow state variables significantly affect and enhance conflict identification, improving accuracy and reducing error rates. These findings contribute to advancing the high-precision identification of real-time conflict identification, with implications for improving road safety measures.