A safety field-based surrogate measure for quantifying bikelane conflict risk considering physical and behavioral dynamics
提出一种基于安全场的自行车道冲突风险度量方法（BSFM），整合物理与行为动力学，利用无人机轨迹数据验证，相比传统指标（TTC、PTTC）能更准确识别冲突（召回率81.3%），并有效追踪实时风险，适用于行人、自行车和电动自行车。
While offering separation between vulnerable road users and motorized vehicles, the presence of non-motorized traffic facilities, such as bikelanes, introduces complex interaction dynamics among pedestrians, conventional bicycles, and e-bikes. Existing surrogate measures inadequately quantify conflict risks in these environments due to oversimplified assumptions and limited consideration of contextual factors. This study proposes a novel safety field-based surrogate measure (Bikelane Safety Field-based Measure, BSFM) that integrates physical dynamics (i.e., road environmental characteristics, kinematic interactions) and behavioral dynamics (i.e., psychological comfort, risk perception, evasion behaviors) to quantify bikelane conflict risk. Utilizing drone-collected trajectory data (203 conflict groups, 37,652 s) from three Tianjin intersections, a bikelane-specific safety field model was developed. Subsequently, the BSFM was proposed, and the threshold of the surrogate measure was determined using extreme value theory. Validation of the BSFM yielded the following key findings: (i) The BSFM demonstrated superior conflict identification recall (81.3%) compared to Time-to-Collision (TTC) (34.5%) and Projected Time-to-Collision (PTTC) (41.4%). (ii) Significant sensitivity to evasive actions was observed, with Kolmogorov-Smirnov and Mann-Whitney U tests confirming statistically significant changes in BSFM values during swerving and deceleration maneuvers (p < 0.001). (iii) Real-time risk tracking was effectively achieved through dynamic visualizations of the safety performance envelope. (iv) The model exhibited robust applicability across diverse conflict participants, including pedestrians, bicycles, and e-bikes. The BSFM provides a validated framework for real-time safety assessment in shared micro-mobility environments, advancing proactive traffic management strategies.