Accident; analysis and prevention最新文献_第5页

Injury severity analysis of e-bike crashes: An age-stratified study of riders aged 40 and above 电动自行车碰撞损伤严重程度分析：40岁及以上骑自行车者的年龄分层研究

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-09 DOI: 10.1016/j.aap.2026.108392

Jingchun Jia , Hao Yue , Shanglin Yang , Xiaolu Jia , Yushuang Qiu

As electric bikes (e-bikes) gain popularity, traffic safety concerns have intensified, particularly for riders aged 40 and above, who face heightened risks due to declining physiological capabilities. However, research analyzing crash injury severity factors for this demographic remains limited. This study examined 2452 e-bike crashes involving riders aged 40 and above in Jiaozhou, China, divided into three groups: 40–50 years, 50–60 years, and 60 years and above. A hybrid methodological framework combining the eXtreme Gradient Boosting (XGBoost) algorithm with Shapley Additive exPlanations (SHAP) and a Random Parameters Binary Logit model with Heterogeneity in Means (RPBL-HM) was constructed. Results showed that rural areas, primary/secondary roads, and holidays increase severe injury likelihood across all riders aged 40 and above. Each age group exhibited distinct risk patterns. The 40–50 age group showed higher severe injury probability with sub-zero temperatures and truck-involved crashes. The 50–60 age group faced elevated risks during nighttime, dawn, rainy or snowy weather, sub-zero temperatures, unhealthy air quality, and weekday nights. The 60 and above age group demonstrated higher risks when riders were farmers, unhealthy air quality, off-peak hours, motorcycle/truck involvement, rural autumn, and autumn crashes involving trucks. These findings provide evidence for developing age-targeted traffic safety interventions, offering significant implications for improving e-bike safety among elderly riders in an increasingly aging society.

随着电动自行车的普及，人们对交通安全的担忧也在加剧，尤其是对于40岁及以上的骑行者来说，由于生理能力的下降，他们面临着更大的风险。然而，针对这一人群的碰撞损伤严重程度因素分析研究仍然有限。本研究调查了中国胶州地区2452起涉及40岁及以上骑行者的电动自行车事故，将其分为40 - 50岁、50-60岁和60岁及以上三组。构建了结合Shapley加性解释（SHAP）的极限梯度增强（XGBoost）算法和均值异质性随机参数二元Logit模型（RPBL-HM）的混合方法框架。结果显示，农村地区、主要/次要道路和假期增加了所有40岁及以上骑手严重受伤的可能性。每个年龄组表现出不同的风险模式。40-50岁年龄组在零下温度和卡车事故中显示出更高的严重伤害概率。50-60岁的人群在夜间、黎明、雨雪天气、零度以下的温度、不健康的空气质量和工作日的夜晚面临着更高的风险。60岁及以上的年龄组在以下情况下的风险更高：农民、不健康的空气质量、非高峰时间、摩托车/卡车参与、农村秋季以及秋季涉及卡车的撞车事故。这些发现为制定针对年龄的交通安全干预措施提供了证据，为在日益老龄化的社会中提高老年人骑电动自行车的安全性提供了重要启示。

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引用次数: 0

Exploring novel surrogate safety indicators measuring conflict riskiness and severity: a case study in Sacramento, United States 探索衡量冲突风险和严重程度的新型替代安全指标：以美国萨克拉门托为例

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-09 DOI: 10.1016/j.aap.2026.108400

Yikai Chen , Yujie Bian , Quan Yuan , Mark King , Jie He , Xudong Cao , Xiaobo Ruan , Yubing Zheng

Determining appropriate traffic conflict indicators is essential for accurately conducting road safety evaluations. However, existing studies often fail to comprehensively address key factors such as the presence or absence of collision courses, the riskiness before and after the conflict point, and the mobility of the conflict point when selecting conflict riskiness indicators. Moreover, current conflict severity indicators are based on fully inelastic collision theory, which lacks sufficient modeling accuracy. This study delves into the characterization of collision and crossing courses in both angle and straight-line conflicts, providing a comprehensive analysis of the risks faced by road users both before reaching a conflict point and after one has passed it. Based on this analysis, a new combination scheme of conflict riskiness indicators is proposed. A two-dimensional, six-degrees-of-freedom potential collision model is then developed based on the theory of partially elastic collisions, and a new indicator, Extended Delta-E, is introduced. Finally, correlation analysis based on crash and conflict data is performed to compare the proposed indicators with traditional riskiness and severity indicators, evaluating their accuracy in road safety evaluation. The results demonstrate that the conflict rates and severity rates derived from the proposed indicators exhibit the strongest correlation with actual crash rates and crash severity rates, respectively, compared to other indicators. The conflict riskiness and severity indicators proposed in this study offer a more nuanced characterization of conflict events, and can serve as highly accurate alternative measures for crash-based road safety evaluations.

确定适当的交通冲突指标对于准确进行道路安全评价至关重要。然而，现有研究在选择冲突风险指标时，往往未能综合考虑冲突过程是否存在、冲突前后的风险性、冲突点的流动性等关键因素。此外，目前的冲突严重程度指标基于完全非弹性碰撞理论，缺乏足够的建模精度。本研究深入探讨了角度冲突和直线冲突中碰撞和穿越路线的特征，为道路使用者在到达冲突点之前和经过冲突点之后所面临的风险提供了全面的分析。在此基础上，提出了一种新的冲突风险指标组合方案。然后，基于部分弹性碰撞理论建立了二维六自由度潜在碰撞模型，并引入了一个新的指标扩展Delta-E。最后，基于碰撞和冲突数据进行相关性分析，将提出的指标与传统的风险和严重程度指标进行比较，评价其在道路安全评价中的准确性。结果表明，与其他指标相比，由建议指标得出的冲突率和严重程度率分别与实际碰撞率和碰撞严重程度率表现出最强的相关性。本研究提出的冲突风险和严重程度指标提供了更细致的冲突事件特征，可以作为基于碰撞的道路安全评估的高度准确的替代措施。

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引用次数: 0

Evaluating underground space of rail transit hub evacuation under fire scenarios: Virtual reality meets agent-based simulation 火灾场景下轨道交通枢纽疏散地下空间评价：虚拟现实与智能体仿真

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-08 DOI: 10.1016/j.aap.2025.108389

Zhilu Yuan , Shenyao Lin , Zixuan Mao , Changyang Xie , Yunhe Tong , Yongxing Li , Hongfei Jia

The rapid growth of urban rail transit has improved transportation efficiency but presents significant safety challenges, particularly in high-density transit hubs where aging infrastructure, overcrowding, and extreme events converge. Fires in such hubs create critical evacuation bottlenecks due to enclosed spaces and complex rescue environments. Existing methods of evacuation research lack the realism and adaptability required to address these dynamic scenarios, particularly for optimizing Selective Door Opening (SDO) strategies. To address these gaps, we developed a high-fidelity virtual environment replicating emergency scenarios in underground transit tunnels. A VR experiment was conducted to collect participants’ evacuation trajectories and eye-tracking data under different SDO strategies. Behavioral mechanisms, movement dynamics, gaze patterns and evacuation speeds in different areas were analyzed, with bivariate Gaussian distributions fitted to describe visual perception regions of pedestrians. Features derived from the foregoing VR experiment informed agent-based simulations, enabling the quantitative evaluation of SDO performance across diverse emergency scenarios. The results of the VR experiment indicate that during evacuation, pedestrians exhibit significant differences in evacuation speed and gaze patterns across various spatial regions. In addition, pedestrians show notable variation in perception scale for different types of AOIs. Furthermore, the evaluation of SDO using agent-based simulation reveals that the effectiveness of SDO is influenced by the scenario issues, with passenger volume and the weighting of safety factors dynamically impacting the selection of the optimal SDO.

城市轨道交通的快速发展提高了运输效率，但也带来了重大的安全挑战，特别是在基础设施老化、过度拥挤和极端事件集中的高密度交通枢纽。由于封闭的空间和复杂的救援环境，这些中心的火灾造成了严重的疏散瓶颈。现有的疏散研究方法缺乏应对这些动态情景所需的现实性和适应性，特别是在优化选择性开门（SDO）策略方面。为了解决这些问题，我们开发了一个高保真的虚拟环境，模拟地下交通隧道中的紧急情况。通过VR实验，收集了参与者在不同SDO策略下的疏散轨迹和眼动追踪数据。分析了行人在不同区域的行为机制、运动动态、注视模式和疏散速度，拟合了描述行人视觉感知区域的二元高斯分布。从上述虚拟现实实验中得出的特征为基于代理的模拟提供了信息，从而能够在各种紧急情况下对SDO的性能进行定量评估。VR实验结果表明，在疏散过程中，不同空间区域的行人在疏散速度和注视模式上存在显著差异。此外，行人对不同类型aoi的感知量表存在显著差异。此外，利用基于agent的仿真方法对SDO进行了评价，结果表明SDO的有效性受到场景问题的影响，乘客数量和安全系数的权重动态影响最优SDO的选择。

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引用次数: 0

Safety-oriented passenger flow control at a congested metro hub: A microscopic approach 以安全为导向的拥挤地铁枢纽客流控制：微观方法

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-05 DOI: 10.1016/j.aap.2025.108390

Jun Zhang , Dongdong Shi , Wei Yang , Jian Ma

Massive passenger influxes at a metro hub pose significant risks. While previous studies have primarily focused on service-oriented passenger flow control from a macroscopic perspective, they have lacked detailed mechanisms to mitigate crowd-related risks from a microscopic viewpoint. To address this gap, this study develops a safety-oriented passenger flow control method based on a comprehensive microscopic pedestrian simulation model for metro hubs. The simulation model operates on two levels: the tactical level, which determines route choices for each pedestrian, and the operational level, which models pedestrian movement using the Social Force Model (SFM). We then introduce a dynamic entry ticket gate service time delay strategy to regulate pedestrian flow and mitigate crowding risks. The strategy leverages Model Predictive Control (MPC) integrated with a Kalman filter to enable real-time decision-making in a stochastic environment. The MPC’s predictive model is a linear representation derived from input–output data generated by our simulation. The control objective, defined as crowd danger, represents the state-of-the-art understanding of pedestrian dynamics. Computational experiments demonstrate the effectiveness of this approach in stabilizing the crowd danger metric around a predefined target level. Furthermore, the results reveal two important management insights: (1) Increasing the crowd danger reference value intensifies fluctuations in crowd behavior, as shown by our control performance analysis and correlation analysis, highlighting the need for a lower reference value to maintain stable control. (2) While this study focuses on crowd safety in the station hall, the proposed approach also improves platform service levels under various train headways and passenger densities in carriages. Thus, this safety-oriented strategy can be integrated into broader passenger flow management efforts to enhance both safety and service efficiency.

地铁枢纽的大量客流带来了巨大的风险。以往的研究主要从宏观角度关注服务型客流控制，缺乏从微观角度降低人群相关风险的详细机制。为了解决这一问题，本研究基于地铁枢纽综合微观行人仿真模型，开发了一种以安全为导向的客流控制方法。仿真模型在两个层面上运行：战术层面，决定每个行人的路线选择；操作层面，使用社会力量模型（Social Force model， SFM）模拟行人的运动。在此基础上，提出了一种动态入口检票口服务延迟策略，以调节行人流量，降低拥挤风险。该策略利用模型预测控制（MPC）与卡尔曼滤波器相结合，在随机环境中实现实时决策。MPC的预测模型是由模拟产生的输入输出数据导出的线性表示。控制目标，定义为人群危险，代表了对行人动态的最新理解。计算实验证明了该方法在将人群危险度量稳定在预定目标水平附近的有效性。此外，研究结果揭示了两个重要的管理启示：(1)增加人群危险参考值会加剧人群行为的波动，我们的控制绩效分析和相关性分析表明，需要较低的参考值来保持稳定的控制。(2)在本研究侧重于车站大厅人群安全的同时，提出的方法也提高了不同列车行距和车厢乘客密度下的站台服务水平。因此，这种以安全为导向的战略可以整合到更广泛的客流管理工作中，以提高安全性和服务效率。

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引用次数: 0

Uncertainty-aware spatiotemporal interaction learning for pre-conflict risk evolution with a risk-increase prior 具有风险增加先验的冲突前风险演化的不确定性感知时空交互学习

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-05 DOI: 10.1016/j.aap.2025.108379

Chenhao Zhao , Min Li , Jiawei Liu , Zhishun Zhang , Shifeng Niu , Dongdong Song

Quantifying real-time conflict risk and revealing its evolution are of great importance for enhancing vehicle active safety. Recent studies estimate dynamic risk via conflict probability, yet annotation still relies on threshold based static views and uncertainty is only partially modeled, which limits the assessment of a model’s ability to learn conflict evolution. Addressing this gap, we posit a prior hypothesis of increasing pre-conflict risk and develop a risk quantification model that integrates driver control inputs and multi vehicle spatiotemporal interactions with explicit uncertainty outputs. The model is evaluated for accuracy and stability of risk perception, parameter sensitivity, and capacity for pattern learning. Experiments show that, relative to Time To Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), Proportion of Stopping Distance (PSD), Anticipated Collision Time (ACT),and Emergency Index (EI), the proposed model achieves stronger risk discrimination. On the test set of 15 conflict events used in this study, the proposed model detects elevated conflict risk on average 1.15 s before the conflict point. In four representative scenarios, including car following, ego lane change, unobstructed cut in and cut in under occluded view, the proposed model yields a lower false alarm rate than TTC and, on average, perceives rising conflict risk 1.44 s before the conflict point. Uncertainty analysis indicates lower uncertainty during the rising risk phase, enabling reliable capture of risk evolution. Sensitivity results support the expressiveness of the proposed hypothesis and reveal a common regularity across scenarios, where risk begins to increase approximately 4–6 s before conflict. The results establish a pre conflict risk modeling paradigm that jointly estimates risk and its confidence, supports calibration and transfer across scenarios, and provides an operational basis for proactive safety assessment.

实时量化冲突风险并揭示其演变过程对提高车辆主动安全性具有重要意义。最近的研究通过冲突概率来估计动态风险，但注释仍然依赖于基于阈值的静态视图，并且不确定性只是部分建模，这限制了对模型学习冲突演变能力的评估。为了解决这一问题，我们假设了冲突前风险增加的先验假设，并开发了一个风险量化模型，该模型将驾驶员控制输入和多车时空交互与明确的不确定性输出集成在一起。对该模型进行了风险感知的准确性和稳定性、参数敏感性和模式学习能力的评估。实验表明，相对于碰撞时间（TTC）、避免碰撞减速率（DRAC）、停车距离比例（PSD）、预期碰撞时间（ACT）和紧急指数（EI），该模型具有较强的风险识别能力。在本研究使用的15个冲突事件的测试集上，该模型在冲突点前平均1.15 s检测到冲突风险升高。在汽车跟随、自我变道、无阻碍插队和遮挡视野插队四种典型场景下，该模型产生的误报警率低于TTC，平均在冲突点前1.44 s感知到冲突风险上升。不确定性分析表明风险上升阶段的不确定性较低，从而能够可靠地捕获风险演变。敏感性结果支持所提出的假设的表达性，并揭示了跨场景的共同规律，其中风险在冲突发生前大约4-6秒开始增加。研究结果建立了一种冲突前风险建模范式，可以联合评估风险及其置信度，支持跨场景的校准和转移，并为主动安全评估提供操作基础。

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引用次数: 0

Loudness of In-Vehicle auditory Warnings: Sustained counteraction of task-related driver fatigue and individual differences in alertness maintenance 车内听觉警告的响度：任务相关驾驶疲劳的持续对抗和警觉性维持的个体差异

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-02 DOI: 10.1016/j.aap.2025.108381

Mengjiao Wu , Xuesong Wang , Ashleigh Filtness , Lishengsa Yue , Ziyuan Huang , Yujun Jiao

In-vehicle fatigue warnings alert drivers when signs of fatigue are detected. These warnings can temporarily restore alertness, helping drivers respond before a crash occurs. They play a key role in reducing fatigue-related crash risk. However, the optimal loudness and effective duration of such warnings are not yet well understood. This study aims to analyze the effectiveness of auditory warnings from two perspectives: first, by employing the Kaplan-Meier method and the Random Survival Forest model to analyze alertness maintenance time across different warning loudness levels and to identify influencing factors. And second, by applying a Bayesian Cox proportional hazards model with random intercepts and slopes to examine individual differences in baseline alertness maintenance risk and sensitivity to 50 dB auditory warnings within the driver population. The results indicate that auditory warnings heighten drivers’ awareness of fatigue and extend the duration over which drivers maintain alertness following a warning. For mild task-related fatigue, one to three warnings produced an incremental benefit compared with no warning, as indicated by longer alertness maintenance. Compared to novice drivers, experienced drivers exhibited a higher baseline risk of reduced vigilance, greater sensitivity to the 50 dB warning, and a lower risk of post-warning fatigue. Overall, this study provides practical guidance for fatigue warning design and accounts for individual differences in alertness maintenance risk. It also introduces a new perspective for evaluating warning effectiveness based on alertness maintenance time.

当检测到疲劳迹象时，车载疲劳警报会向驾驶员发出警报。这些警告可以暂时恢复警觉性，帮助司机在车祸发生前做出反应。它们在减少疲劳相关的碰撞风险方面发挥着关键作用。然而，这种警告的最佳响度和有效持续时间尚未得到很好的理解。本研究旨在从两个角度分析听觉预警的有效性：首先，采用Kaplan-Meier方法和随机生存森林模型分析不同警报响度水平下的警觉性维持时间，并找出影响因素。其次，通过采用随机截距和斜率的贝叶斯-考克斯比例风险模型来检验驾驶员群体中基线警觉性维持风险和对50分贝听觉警告敏感性的个体差异。结果表明，听觉警告提高了驾驶员的疲劳意识，延长了驾驶员在警告后保持警觉性的时间。对于轻微的任务相关疲劳，一到三次警告比没有警告产生更多的好处，这表明警觉性维持时间更长。与新手司机相比，经验丰富的司机表现出更高的警惕性降低的基线风险，对50db警告更敏感，警告后疲劳的风险更低。总体而言，本研究为疲劳预警设计提供了实用指导，并解释了警觉性维持风险的个体差异。提出了一种基于警觉性维护时间评价警觉性有效性的新视角。

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引用次数: 0

Uncovering latent structures of crash typology in narcotic-involved fatal crashes for safe system interventions 在涉及麻醉的致命碰撞中揭示碰撞类型的潜在结构，用于安全系统干预

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-02 DOI: 10.1016/j.aap.2025.108382

Anannya Ghosh Tusti, Rohit Chakraborty, Tausif Islam Chowdhury, Md Monzurul Islam, Mahmuda Sultana Mimi, Subasish Das

Narcotic-impaired driving increases the risk of fatal crashes, yet existing studies rarely provide narcotic-specific crash typologies that link driver impairment to roadway, traffic, and environmental conditions. This gap limits the design of Safe System interventions that can proactively address the most common high-risk configurations. Using Fatality Analysis Reporting System data from 2018 to 2022, this study applies Cramér’s V statistic for variable selection and Cluster Correspondence Analysis (CCA) to explore unsupervised crash typologies and latent patterns of narcotics-involved fatal crashes. CCA biplot coordinates group crashes into four clusters: high-speed lane changes on uncontrolled arterials, run-off-road impacts with rollovers, nighttime pedestrian or cyclist strikes on unlit roads, and moderate-speed angle crashes at signalized intersections. Results show that speed and lateral control failures dominate the first two clusters, narcotic-induced sensory and cognitive deficits under low visibility drive the third, and decision-making errors during turn phases characterize the fourth. Key factors such as posted speed limit, lighting condition, and driver age exert cluster-specific influences on incapacitating and fatal injury outcomes. These findings underscore the inadequacy of appropriate countermeasures and point to Safe System-aligned interventions, including dynamic speed management, enhanced roadside clear zones, targeted lighting upgrades, and intersection control strategies.

麻醉状态下的驾驶增加了致命车祸的风险，然而现有的研究很少提供麻醉状态下特定的车祸类型，将驾驶员的损伤与道路、交通和环境条件联系起来。这种差距限制了安全系统干预措施的设计，无法主动解决最常见的高风险配置。利用2018年至2022年的死亡率分析报告系统数据，本研究应用cram s V统计变量选择和聚类对应分析（CCA）来探索涉及毒品的致命事故的无监督事故类型和潜在模式。CCA双坐标图将事故分为四类：在不受控制的主干道上的高速变道，侧翻的越野跑碰撞，夜间行人或骑自行车的人在没有照明的道路上的撞击，以及在有信号的十字路口的中速角度碰撞。结果表明，速度和横向控制失败主导了前两组，麻醉诱导的感觉和认知缺陷驱动了第三组，转弯阶段的决策错误是第四组的特征。限速、照明条件和驾驶员年龄等关键因素对致残和致死性伤害的结果有特定的影响。这些发现强调了适当对策的不足，并指出了与安全系统相一致的干预措施，包括动态速度管理、加强路边畅通区、有针对性的照明升级和交叉口控制策略。

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引用次数: 0

Time-to-event crash severity prediction at highway-rail grade crossings with monotonic neural networks 基于单调神经网络的公路铁路平交道口碰撞严重程度时间预测

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-02 DOI: 10.1016/j.aap.2025.108385

Amin Keramati , Pan Lu , Yi Hao Ren

Despite advances in highway-rail grade crossing (HRGC) safety, including widespread use of active control devices, crashes at these intersections still lead to severe outcomes. Conventional crash prediction models often fail to capture severity-level dependencies, rely on assumption-driven and computationally intensive methods, and overlook links between severity and time between events. This study introduces a predictive framework based on positive monotonic neural networks (MNNs) for modeling time-to-crash outcomes with severity at HRGCs. Considering the relative newness of the time-to-crash paradigm in HRGC safety, the Neural Fine–Gray model is adopted as a core MNN implementation to estimate severity-specific crash likelihoods. This approach eliminates the numerical integration required in traditional time-to-event models, substantially reducing computational burden and accelerating training for large datasets. The framework naturally handles imbalanced HRGC data by treating event-free records as right-censored, avoiding the resampling required in traditional machine-learning approaches. To examine severity-level dependencies—an aspect largely overlooked in the literature—four MNN architectures are developed and evaluated. Using a 29-year North Dakota HRGC dataset, results show trade-offs between predictive accuracy and computational efficiency. The cause-specific MNN performs best for medium- and long-term horizons, whereas the multi-head MNN converges faster and excels at short horizons. Moreover, benchmarking against traditional time-to-event models—cause-specific Cox and Fine–Gray—shows modest calibration gains and 2%–50% stronger discrimination, reflecting the alignment between MNNs and the nonlinear, high-dimensional HRGC data. The framework also enhances interpretability by revealing paradoxical effects, including the “adding flashing lights paradox” and the “adding stop signs paradox.”

尽管公路铁路平交道口（HRGC）的安全性有所提高，包括主动控制装置的广泛使用，但这些交叉路口的碰撞仍然会导致严重的后果。传统的崩溃预测模型往往无法捕获严重级别的依赖关系，依赖于假设驱动和计算密集型的方法，并且忽略了事件之间的严重程度和时间之间的联系。本研究引入了一个基于正单调神经网络（MNNs）的预测框架，用于模拟hrgc严重程度的崩溃时间结果。考虑到HRGC安全中碰撞时间范式的相对新颖性，采用神经细灰模型作为核心MNN实现来估计特定严重程度的碰撞可能性。这种方法消除了传统时间到事件模型所需的数值积分，大大减少了计算负担，加速了大型数据集的训练。该框架通过将无事件记录视为右审查来自然地处理不平衡的HRGC数据，避免了传统机器学习方法所需的重新采样。为了检查严重级别的依赖性——这是文献中很大程度上被忽视的一个方面——我们开发并评估了四种MNN架构。使用29年的北达科他州HRGC数据集，结果显示了预测精度和计算效率之间的权衡。特定原因的MNN在中长期视野中表现最好，而多头MNN收敛更快，在短期视野中表现出色。此外，针对传统的事件时间模型（原因特定的Cox和fine - gray）进行基准测试，显示出适度的校准增益和2%-50%的强辨别，反映了mnn与非线性高维HRGC数据之间的一致性。该框架还通过揭示悖论效应来增强可解释性，包括“添加闪光灯悖论”和“添加停止标志悖论”。

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引用次数: 0

Interpretable analysis of risk factors for intercity bus operation safety on plateau roads using the SHAP method 基于SHAP方法的高原道路城际客车运行安全风险因素可解释分析

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-02 DOI: 10.1016/j.aap.2025.108384

Meiping Yun, Hao Zheng, Peiyu Lu

Identifying and analyzing factors that influence traffic risk is crucial for reducing crash frequency and severity. This is particularly true for intercity buses operating on high-altitude plateaus, where unique environmental and topographical conditions heighten operational risks. However, quantitative research on how these factors influence traffic operation risk in such contexts, especially across freeways, general highways, and urban roads, remains insufficient. This study uses two months of GPS trajectory data from 10 intercity bus routes in Qinghai province, China to address this gap. We assessed traffic operation risks at discrete road segments using three indicators: Coefficient of Speed Variation (CSV), Severity of Rapid Acceleration (SRA), and Severity of Rapid Deceleration (SRD). A Random Forest regression model, interpreted by the SHAP method, was employed to explore the complex effects of influencing factors. The results quantify the unique risks of the plateau environment, showing that driving instability surges at high altitudes, and the impact is significantly amplified by high speeds. The analysis further highlights the operational risks of intercity buses, identifying a clear fatigue threshold where continuous driving leads to more frequent aggressive maneuvers. Critically, the research differentiates these risks by road type, demonstrating a shift in dominant safety factors, as road geometry poses a greater threat on general highways than on freeways. These findings provide quantitative evidence for developing targeted safety interventions that address the unique operational risks of plateau roadways.

识别和分析影响交通风险的因素对于降低碰撞频率和严重程度至关重要。对于在高海拔高原运行的城际巴士来说尤其如此，那里独特的环境和地形条件增加了运营风险。然而，定量研究这些因素如何影响这种情况下的交通运营风险，特别是在高速公路、普通公路和城市道路上，仍然不足。本研究利用中国青海省10条城际公交线路两个月的GPS轨迹数据来解决这一差距。我们使用三个指标来评估离散路段的交通运行风险：速度变化系数（CSV）、快速加速严重程度（SRA）和快速减速严重程度（SRD）。采用随机森林回归模型，采用SHAP方法进行解释，探讨影响因素的复杂效应。结果量化了高原环境的独特风险，表明在高海拔地区驱动不稳定性激增，并且高速的影响显着放大。该分析进一步强调了城际巴士的运营风险，确定了一个明确的疲劳阈值，即连续驾驶会导致更频繁的激进操作。至关重要的是，该研究根据道路类型区分了这些风险，显示了主要安全因素的转变，因为道路几何形状在普通公路上比在高速公路上构成更大的威胁。这些发现为制定有针对性的安全干预措施，解决高原道路独特的运营风险提供了定量证据。

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引用次数: 0

Enhancing collaborative perception through multi-scale contextual information integration 通过多尺度语境信息整合增强协同感知

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-31 DOI: 10.1016/j.aap.2025.108367

Mingyue Ma , Dongyang Hong , Hui Zhang , Zelin Miao , Weiqing Wang , Guangming Zhao

In autonomous driving, perception systems face challenges from dynamic environments such as occlusions, changing lighting, and unpredictable traffic. These conditions make it hard to capture fine local details and broad context, while real-time operation demands high efficiency and low communication cost. In this paper, we propose a multi-scale contextual information integration (MSCI) framework designed to enhance collaborative perception. The method employs multi-scale adaptive attention augmentation to focus on relevant features across spatial scales, capturing fine-grained details and wider contextual cues to improve perception accuracy. The context-aware perception enhancement module then combines local and global information. It refines features to keep perception robust and stable in changing or challenging environments. Finally, the GRU-based dynamic booster embeds a motion-aware mechanism into the recurrent unit. This strengthens temporal modeling for sequential data and improves real-time decision making. Experimental results demonstrate that the proposed method achieves notable improvements in both detection accuracy and communication efficiency.

在自动驾驶中，感知系统面临着来自动态环境的挑战，如闭塞、不断变化的照明和不可预测的交通。这些条件使得难以捕获精细的局部细节和广泛的上下文，而实时操作要求高效率和低通信成本。在本文中，我们提出了一个旨在增强协作感知的多尺度上下文信息集成（MSCI）框架。该方法采用多尺度自适应注意力增强，聚焦于空间尺度上的相关特征，捕获细粒度细节和更广泛的上下文线索，以提高感知准确性。然后，上下文感知感知增强模块将本地和全局信息结合起来。它改进功能，使感知在不断变化或具有挑战性的环境中保持健壮和稳定。最后，基于gru的动态助推器将运动感知机制嵌入到循环单元中。这加强了时序数据的时间建模，并改进了实时决策。实验结果表明，该方法在检测精度和通信效率方面均有显著提高。

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引用次数: 0