Maji Abhijnan, Ghosh Indrajit
Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India.
Accid Anal Prev. 2025 Oct;221:108219. doi: 10.1016/j.aap.2025.108219. Epub 2025 Aug 27.
Roundabouts in low- and middle-income countries are not as safe as expected due to non-lane-based traffic behaviors and heterogeneity in traffic conditions. To address the limitations of crash-based analyses, this study developed a proactive, data-driven framework that integrates high-resolution drone-recorded video-based trajectory extraction, multivariate Extreme Value Theory (EVT)-Peak-Over-Threshold (POT) modeling, and probabilistic clustering to identify and classify conflict events at unsignalized roundabouts. Trajectories from videos collected at 22 roundabouts were extracted via advanced computer-vision algorithms and processed in the Surrogate Safety Assessment Model (SSAM) developed by the Federal Highway Administration to compute four surrogate safety measures (SSMs): Time-to-Collision (TTC), Post-Encroachment Time (PET), maximum deceleration (MaxD), and maximum post-collision (hypothetical) velocity change (MaxDeltaV). The quadrivariate EVT-POT model with Gumbel-Hougaard copula was developed to capture joint exceedances of the SSMs and determine context-specific thresholds, i.e., 1.5 s for TTC and PET, -3.0 m/s for MaxD, and 4.5 m/s for MaxDeltaV, via Mean Residual Life, Threshold Stability, and AIC plots. The copula captured tail dependencies among the SSMs efficiently, marked by its goodness-of-fit diagnostics. Conflicts were mapped spatially, revealing that lane-change interactions constituted ∼ 43 %, rear-end ∼ 38 %, and crossing ∼ 19 % of conflicts, with distinct clustering at approach legs, weaving zones, and pedestrian/bicyclists crossing points. Latent profile analysis using the Gaussian Mixture Model stratified conflicts into five severity levels, i.e., from minor (29.7 %) to critical (7.6 %), enabling prioritized intervention strategies. This framework offers a scalable tool for practitioners to pinpoint high-risk areas and deploy targeted safety countermeasures, enhancing proactive roundabout safety under mixed-traffic conditions.
由于非基于车道的交通行为和交通状况的异质性，低收入和中等收入国家的环形交叉路口并不像预期的那样安全。为了解决基于碰撞分析的局限性，本研究开发了一个主动的、数据驱动的框架，该框架集成了基于高分辨率无人机记录视频的轨迹提取、多元极值理论（EVT）-阈值峰值（POT）建模和概率聚类，以识别和分类无信号环形交叉路口的冲突事件。通过先进的计算机视觉算法提取了在22个环形交叉路口收集的视频中的轨迹，并在美国联邦公路管理局开发的替代安全评估模型（SSAM）中进行处理，以计算四种替代安全措施（SSM）：碰撞时间（TTC）、侵入后时间（PET）、最大减速度（MaxD）和最大碰撞后（假设）速度变化（MaxDeltaV）。开发了具有Gumbel-Hougaard连接函数的四变量EVT-POT模型，以捕获SSM的联合超限情况，并通过平均剩余寿命、阈值稳定性和AIC图确定特定于上下文的阈值，即TTC和PET为1.5秒，MaxD为-3.0米/秒，MaxDeltaV为4.5米/秒。连接函数有效地捕获了SSM之间的尾部依赖性，这通过其拟合优度诊断得以体现。冲突在空间上进行了映射，结果显示变道交互构成了约43%的冲突，追尾约38%，交叉约19%的冲突，在进口路段、交织区和行人/自行车穿越点有明显的聚类。使用高斯混合模型的潜在剖面分析将冲突分为五个严重程度级别，即从轻微（29.7%）到严重（7.6%），从而能够制定优先干预策略。该框架为从业人员提供了一个可扩展的工具，以确定高风险区域并部署有针对性的安全对策，在混合交通条件下加强环形交叉路口的主动安全。
