Heavy commercial vehicles (HCVs) face elevated crash risks in mountainous terrains due to the challenging topography and intricate geometry, posing a significant challenge for transportation agencies in mitigating these risks. While safety studies in such terrains traditionally rely on historical crash data, the inherent issues associated with crash data have led to a shift towards proactive safety studies using surrogate safety measures (SSM) in recent years. However, the scarcity of accurate microscopic data related to HCV drivers has limited the application of proactive safety studies in mountainous terrains. This study addresses this gap by employing an SSM known as anticipated collision time (ACT) to explore the impact of horizontal curves on the crash risk of HCVs in mountainous terrain. To perform the crash risk analysis, a collection of videos was gathered from horizontal curves in the mountainous terrain along the Guwahati-Shillong bypass in the Northeastern region of India. Subsequently, trajectories were extracted from these videos using semi-automated image processing software. Traffic conflicts were identified using ACT, and the crash risk was estimated through the Peak-Over Threshold (POT) approach of the Extreme Value Theory (EVT). The findings indicate that Run-Off-Road (ROR) traffic events happen more frequently on or near the horizontal curves falling in mountainous terrain. However, the frequency of severe ROR traffic events is lower, indicating the lower propensity for such collisions on the selected curves. The threshold for the safety margin of ROR traffic events involving HCVs was 2 s. The study revealed that stationary models exhibit an overestimation of crash frequency (0, 6) compared to the observed crash frequency (0, 2). Consequently, non-stationary crash risk models were developed, incorporating road geometry and the braking and yaw rates of HCVs as covariates. The results demonstrate that the estimated confidence bounds (1, 2) align with the observed crash frequency (0, 2), emphasizing the applicability of POT models for safety analysis in mountainous terrains in India. The study identified curve radius, length of the approach tangent, and the distance between the center points of horizontal and vertical curves as influential factors affecting the Run-Off-Road (ROR) crash risk of HCVs. Notably, sharp curves with radii less than 200 m or more are associated with a significantly higher crash risk. Additionally, an increased distance between the midpoints of horizontal and vertical curves beyond 1 m was found to escalate the ROR crash risk of HCVs. To mitigate these risks, it is recommended to reduce the length of the approach tangent to prevent high-speed travel on sharp curves. Furthermore, proper signage should be strategically placed to warn drivers and avert potential hazards.

中文翻译：

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使用主动安全方法评估重型商用车在山区水平曲线上的碰撞风险

由于具有挑战性的地形和复杂的几何形状，重型商用车 (HCV) 在山区面临着较高的碰撞风险，这对运输机构减轻这些风险构成了重大挑战。虽然此类地形的安全研究传统上依赖于历史碰撞数据，但与碰撞数据相关的固有问题导致近年来转向使用替代安全措施 (SSM) 的主动安全研究。然而，与 HCV 驾驶员相关的准确微观数据的缺乏限制了主动安全研究在山区的应用。本研究通过采用称为预期碰撞时间 (ACT) 的 SSM 来探讨水平曲线对山区 HCV 碰撞风险的影响，从而弥补了这一差距。为了进行碰撞风险分析，从印度东北地区古瓦哈提-西隆绕道沿线山区的水平曲线收集了一组视频。随后，使用半自动图像处理软件从这些视频中提取轨迹。使用 ACT 识别交通冲突，并通过极值理论 (EVT) 的峰值超过阈值 (POT) 方法估计碰撞风险。研究结果表明，越野（ROR）交通事件更频繁地发生在山区的水平曲线上或附近。然而，严重 ROR 交通事件的频率较低，表明所选曲线上发生此类碰撞的可能性较低。涉及 HCV 的 ROR 交通事件的安全裕度阈值为 2 秒。研究表明，与观察到的碰撞频率 (0, 2) 相比，静态模型对碰撞频率 (0, 6) 的估计过高。因此，开发了非平稳碰撞风险模型，将道路几何形状以及 HCV 的制动和偏航率作为协变量。结果表明，估计的置信区间 (1, 2) 与观察到的碰撞频率 (0, 2) 一致，强调了 POT 模型在印度山区安全分析中的适用性。该研究将曲线半径、引道切线长度以及水平曲线和垂直曲线中心点之间的距离确定为影响HCV越野（ROR）碰撞风险的影响因素。值得注意的是，半径小于 200 m 或以上的急弯与显着较高的碰撞风险相关。此外，水平曲线和垂直曲线中点之间的距离增加超过 1 m 会增加 HCV 的 ROR 碰撞风险。为了减轻这些风险，建议减少接近切线的长度，以防止在急弯上高速行驶。此外，应策略性地放置适当的标牌，以警告驾驶员并避免潜在危险。