Bei Runzhao, Du Zhigang, Lyu Nengchao, Yu Liang, Yang Yongzheng
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, PR China; Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan 430063, PR China.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, PR China.
Accid Anal Prev. 2025 Mar;211:107914. doi: 10.1016/j.aap.2024.107914. Epub 2025 Jan 8.
Freeway tunnel approach zones, situated outside the tunnel, do not undergo the same sudden changes in luminous environment and visual references that entrance zones experience. Despite this, accident data indicates that approach zones present similar safety risks to entrance zones, both of which are significantly higher than other tunnel sections. The reasons for the heightened risks in approach zones remain unclear in existing research. To address this knowledge gap, this study conducted real vehicle tests and subjective perception experiments. The Task Analysis of Driving Scenarios (TADS) was employed to analyze driving behavior patterns and develop a set of evaluation metrics, including four key driving behavior nodes (P1_SGD, P2_EF, P3_FF, P4_SAD), safety and efficacy indices for active deceleration behaviors (I1_ADS, I2_ADE), and two indicators for understanding anomalous behaviors (SR, AOI_PFN). By skillfully selecting scenarios to control variables, this research examined how limited visibility in tunnel approach zones and spatial intervisibility tunnels contribute to safety risks in these zones. Additionally, the Predictive Processing Model (PPM) was used to elucidate the temporal and spatial evolution of driving predictions, tasks, and behaviors under normal conditions. The findings reveal that, although heavy driving tasks cannot be avoided, under normal conditions, predictions develop gradually with minimal prediction errors, enabling effective navigation. However, limited visibility in tunnel approach zones and spatially intervisible tunnels lead to inaccuracies and deviations in predictions, resulting in significant prediction errors as drivers approach the tunnel. This causes more aggressive driving behaviors, disrupting the delicate balance of predictions and tasks.
高速公路隧道引道区域位于隧道外部,不会经历与入口区域相同的光照环境和视觉参考的突然变化。尽管如此,事故数据表明,引道区域与入口区域存在相似的安全风险,两者均显著高于其他隧道路段。现有研究中,引道区域风险增加的原因仍不明确。为填补这一知识空白,本研究进行了实车测试和主观感知实验。采用驾驶场景任务分析(TADS)来分析驾驶行为模式,并制定了一套评估指标,包括四个关键驾驶行为节点(P1_SGD、P2_EF、P3_FF、P4_SAD)、主动减速行为的安全和效能指标(I1_ADS、I2_ADE)以及两个理解异常行为的指标(SR、AOI_PFN)。通过巧妙选择场景来控制变量,本研究考察了隧道引道区域有限的能见度和空间互见性隧道如何导致这些区域的安全风险。此外,使用预测处理模型(PPM)来阐明正常情况下驾驶预测、任务和行为的时空演变。研究结果表明,尽管繁重的驾驶任务无法避免,但在正常情况下,预测会逐渐发展,预测误差最小,从而实现有效导航。然而,隧道引道区域和空间互见性隧道有限的能见度会导致预测不准确和偏差,当驾驶员接近隧道时会导致显著的预测误差。这会导致更激进的驾驶行为,扰乱预测和任务的微妙平衡。