Accident; analysis and prevention最新文献_第4页

A graph-based spatio-temporal framework for predicting safety-critical pedestrian–vehicle interactions at unsignalized crosswalks 一个基于图的时空框架，用于预测无信号人行横道上安全关键的行人-车辆相互作用

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-16 DOI: 10.1016/j.aap.2026.108409

Kaliprasana Muduli , Indrajit Ghosh , Satish V. Ukkusuri

Pedestrian safety remains a critical global concern, especially in countries like India, where unsignalized crossings with limited traffic control contribute to high pedestrian fatality rates. This study proposes a novel graph-based framework for analyzing pedestrian–vehicle interactions, advancing beyond traditional indicator-based, trajectory prediction, and pairwise modeling approaches. Indicator-based methods (e.g., PET, TTC) are retrospective and fail to capture evolving dynamics. Trajectory prediction models suffer from error accumulation, while pairwise models like LSTM or Transformer architectures are limited to two-agent interactions, restricting scalability and scene comprehension. In contrast, the proposed framework constructs holistic, scene-level multi-relational graphs, representing pedestrians and vehicles as interconnected nodes, with explicit modeling of pedestrian–pedestrian, vehicle–vehicle, and pedestrian–vehicle interactions. Unlike existing approaches that treat each pedestrian–vehicle pair in isolation, the proposed method represents all road users present in the scene as nodes in a dynamic spatio-temporal graph, enabling the model to learn not only direct pedestrian–vehicle relationships but also indirect influences mediated by surrounding pedestrians and vehicles. This design eliminates the need to pre-select interaction pairs, simplifying real-world deployment and improving scalability in dense traffic scenarios. Disentangled Multi-Scale Aggregation (DMSA) captures group behavior by focusing on contextually relevant agents, while a temporal CNN backbone models both short- and long-range dependencies efficiently. Empirical evaluations demonstrate the superior performance of the proposed model, which achieved a test accuracy of 90.6%, F1-score of 0.906, precision of 0.927, recall of 0.886, specificity of 0.928, and an AUC of 0.950, outperforming widely used baselines from the literature, such as GRU, LSTM, and Transformer-MLP, that have been applied in pedestrian interaction modeling tasks. Ablation studies confirmed the importance of Multi-Relational Adjacency Matrices (MRAM) and DMSA in improving accuracy and reducing false positives. By modeling scene-level dynamics, the framework enables context-aware prediction of critical events, supporting proactive conflict warning systems.

行人安全仍然是一个重要的全球问题，特别是在印度等国家，在这些国家，没有信号的交叉路口和有限的交通控制导致了高行人死亡率。本研究提出了一种新的基于图的框架来分析行人与车辆的相互作用，超越了传统的基于指标、轨迹预测和两两建模方法。基于指标的方法（如PET、TTC）是回顾性的，无法捕捉不断变化的动态。轨迹预测模型受到误差积累的影响，而LSTM或Transformer架构等两两模型则仅限于双智能体交互，限制了可扩展性和场景理解。相比之下，该框架构建了整体的、场景级的多关系图，将行人和车辆表示为相互连接的节点，并对行人与行人、车辆与车辆以及行人与车辆的交互进行了明确的建模。与现有的隔离处理每个行人-车辆对的方法不同，该方法将场景中的所有道路使用者表示为动态时空图中的节点，使模型不仅可以学习直接的行人-车辆关系，还可以学习由周围行人和车辆中介的间接影响。这种设计消除了预先选择交互对的需要，简化了实际部署，并提高了密集流量场景中的可扩展性。解纠缠多尺度聚合（DMSA）通过关注上下文相关的代理来捕获群体行为，而时间CNN主干有效地模拟了短期和长期依赖关系。实证评价表明，该模型的测试准确率为90.6%，f1得分为0.906，精度为0.927，召回率为0.886，特异性为0.928，AUC为0.950，优于文献中广泛使用的基线，如GRU、LSTM和Transformer-MLP，这些基线已经应用于行人交互建模任务。消融研究证实了多关系邻接矩阵（MRAM）和DMSA在提高准确性和减少假阳性方面的重要性。通过对场景级动态建模，该框架可以实现关键事件的上下文感知预测，支持主动冲突预警系统。

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引用次数: 0

Inferring the structure of pedestrian flows at a transportation hub 推断交通枢纽的行人流结构

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-16 DOI: 10.1016/j.aap.2025.108391

Xiaolu Jia , Claudio Feliciani , Hisashi Murakami , Sakurako Tanida , Liang Chen , Hao Yue , Daichi Yanagisawa , Katsuhiro Nishinari

In transportation hubs, pedestrian flows form complex network structures, leading to serious congestion at peak hours. Understanding their dynamics is crucial to managing safe and efficient transportation. Although many experimental and theoretical studies have investigated pedestrian interactions at the microscopic level, computational models that account for pedestrians’ macroscopic origin and destination (OD) demands and mesoscopic route choices in large walking facilities are rare and lack empirical validation. In other words, pedestrians’ decision-making at strategic (macroscopic) and tactical (mesoscopic) levels, other than the operational (microscopic) level, has remained largely unexplored. Here, we propose an integrated Strategic–Tactical–Operational model for transportation hub (STO-Hub model), and validate it using 0.87 million pedestrian trajectories collected over three days by means of 11 LiDAR sensors at JR Shinjuku station in Japan. Based on an abstracted graph of the main concourse with directed links between different platform entrances and gates, we employ the gravity model at the strategic layer to estimate time-varying OD demand, a logit route-choice model at the tactical layer to capture route choice behavior, and an agent-based model to reproduce interactions with the surrounding environment and pedestrians. The STO-Hub model accurately reconstructs OD demand and route-choice behavior, achieving high agreement with directed flow counts, and the simulation delineates local congested areas evident in the sensing data. By estimating OD demand and route splits and by reproducing local interactions at any selected section, the STO-Hub model captures pedestrian dynamics across all three levels, including at congested locations. We further propose a STO-Hub framework that integrates sensing, the STO-Hub model, and management plans, providing a practical 10-min-resolution basis for OD-informed pedestrian guidance and control in transportation hubs. The study fills a gap in strategic modeling and management for large transportation hubs and supports congestion prevention, improved safety, and higher operational efficiency.

在交通枢纽，行人流形成复杂的网络结构，导致高峰时段严重拥堵。了解它们的动态对于管理安全和高效的运输至关重要。尽管许多实验和理论研究从微观层面探讨了行人相互作用，但考虑大型步行设施中行人宏观始发和目的地（OD）需求和中观路径选择的计算模型很少，而且缺乏经验验证。换句话说，除了操作（微观）层面，行人在战略（宏观）和战术（中观）层面的决策在很大程度上仍未被探索。在此，我们提出了一个综合的交通枢纽战略-战术-运营模型（stohub模型），并使用日本JR新宿站的11个激光雷达传感器在三天内收集的87万行人轨迹进行验证。基于具有不同站台入口和大门之间直接连接的主要大厅的抽象图，我们在策略层使用重力模型来估计时变OD需求，在战术层使用logit路径选择模型来捕获路径选择行为，并使用基于代理的模型来再现与周围环境和行人的交互。STO-Hub模型准确地重建了OD需求和路线选择行为，与定向流计数高度一致，模拟描绘了感知数据中明显的局部拥堵区域。通过估算OD需求和路线分割，并在任何选定的路段再现当地的互动，STO-Hub模型捕捉了所有三个层面的行人动态，包括在拥挤的地方。我们进一步提出了一个集成传感、STO-Hub模型和管理计划的STO-Hub框架，为交通枢纽的od信息行人引导和控制提供了一个实用的10分钟分辨率基础。该研究填补了大型交通枢纽战略建模和管理方面的空白，并支持预防拥堵、改善安全性和提高运营效率。

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引用次数: 0

DrowsyDG-Phys: Generalizable driver drowsiness estimation in conditional automated vehicles using physiological signals 基于生理信号的有条件自动驾驶车辆驾驶员困倦估计

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-16 DOI: 10.1016/j.aap.2026.108407

Jiyao Wang , Wenbo Li , Zhenyu Wang , Suzan Ayas , Birsen Donmez , Dengbo He , Kaishun Wu

Driver drowsiness is one of the leading causes of crashes, injuries, and fatalities on the road. Traditional drowsiness detection models relied on manually extracted physiological features processed through machine learning algorithms. However, these methods lacked flexibility and robustness across diverse real-world conditions. Although recent advances in deep learning have improved detection accuracy through automated feature extraction based on larger learnable parameter space, the generalization of existing models is still limited due to domain shifts. In this study, we proposed DrowsyDG-Phys, a novel domain generalization (DG) framework for driver drowsiness detection using three physiological signals (i.e., electrocardiogram, electrodermal activity, and respiration signals) that can be measured by in-vehicle or wearable sensors. Our approach introduced a backbone network for explicit time and frequency domain feature learning. In addition, our approach integrated three novel loss functions: a prior knowledge-based contrastive regularization for robustness, a feature centralization loss to promote generalization in heterogeneities, and a novel loss function to align drowsiness assessment criteria. Finally, we established a multi-source DG benchmark and evaluated our model on three existing datasets and a self-collected dataset involving 60 participants in a simulated SAE Level-3 driving scenario. Our proposed DrowsyDG-Phys achieves 78.5% accuracy on the DG protocol, as well as 88.4% accuracy on the cross-subject protocol. Experimental results demonstrated that DrowsyDG-Phys outperformed baseline methods, and improved generalization and robustness of physiological signal-based drowsiness monitoring.

司机的困倦是道路上撞车、受伤和死亡的主要原因之一。传统的嗜睡检测模型依赖于人工提取的生理特征，并通过机器学习算法进行处理。然而，这些方法在不同的现实条件下缺乏灵活性和鲁棒性。尽管深度学习的最新进展通过基于更大可学习参数空间的自动特征提取提高了检测精度，但由于域移位，现有模型的泛化仍然受到限制。在这项研究中，我们提出了一种新的域泛化（DG）框架，用于驾驶员困倦检测，该框架使用三种生理信号（即心电图、皮肤电活动和呼吸信号），可以通过车载或可穿戴传感器测量。我们的方法引入了一个骨干网络，用于显式的时域和频域特征学习。此外，我们的方法集成了三种新的损失函数：一种基于先验知识的对比正则化鲁棒性，一种特征集中损失来促进异质性的泛化，以及一种新的损失函数来校准困倦评估标准。最后，我们建立了一个多源DG基准，并在三个现有数据集和一个包含60名参与者的模拟SAE 3级驾驶场景的自收集数据集上评估了我们的模型。我们提出的DrowsyDG-Phys在DG协议上达到78.5%的准确率，在交叉协议上达到88.4%的准确率。实验结果表明，DrowsyDG-Phys优于基线方法，提高了基于生理信号的嗜睡监测的泛化和鲁棒性。

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引用次数: 0

Cooperative or competitive? Resolving social dilemmas in autonomous vehicles through evolutionary game theory 合作还是竞争？利用进化博弈论解决自动驾驶汽车中的社会困境

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-16 DOI: 10.1016/j.aap.2026.108402

Rui Li, Yiru Liu, Jian Sun, Ye Tian

With the large-scale deployment of autonomous vehicles (AVs), AV-human-driven vehicle (HV) interactions are increasingly common. AVs face a social dilemma: competitive behavior raises ethical and public acceptance concerns, whereas cooperative behavior can invite exploitation and degrade efficiency. We use Evolutionary Game Theory (EGT) to model long-run adaptation between AVs and HVs and quantify agent sociality via a data-calibrated Social Value Orientation (SVO) metric. After calibrating HV social preferences from unprotected left-turn trajectories, we incorporate HV heterogeneity into a two-population EGT with cooperative and competitive types. SVO-informed rewards are used to construct payoff matrices for replicator analyses to identify evolutionarily stable strategies (ESS). Experiments show that AV policies with moderate egoism mitigate the social dilemma and tend to achieve population-level dominance in both roles (left-turning and straight-going), whereas overly cooperative policies are evolutionarily unstable. Moreover, AVs benefit from opponent-aware, dynamically adjustable sociality to accommodate diverse HV preferences. To test the theory, we run agent-based imitation simulations. Sensitivity analyses indicate that AV advantages are hard to observe at low market penetration but become pronounced as penetration approaches about 50%, after which convergence accelerates. Overall, the framework clarifies when and why AV sociality preferences succeed over time, offering actionable guidance for designing adaptive, socially compatible AV decision policies in mixed traffic.

随着自动驾驶汽车（av）的大规模部署，自动驾驶汽车与人驾驶汽车（HV）之间的互动越来越普遍。自动驾驶汽车面临着一个社会困境：竞争行为会引起道德和公众接受的担忧，而合作行为会招致剥削并降低效率。我们利用进化博弈论（EGT）来模拟自动驾驶汽车和hv之间的长期适应，并通过数据校准的社会价值取向（SVO）度量来量化代理社会性。在从无保护的左转弯轨迹中校准HV社会偏好后，我们将HV异质性纳入了具有合作和竞争类型的两种群EGT中。利用SVO-informed奖励来构建收益矩阵，用于复制因子分析，以确定进化稳定策略。实验表明，适度利己主义的AV政策缓解了社会困境，并倾向于在两个角色（左转弯和直行）中实现种群水平的优势，而过度合作的AV政策在进化上是不稳定的。此外，自动驾驶汽车受益于对手意识，动态调整的社会性，以适应不同的HV偏好。为了验证这一理论，我们运行了基于代理的模仿仿真。敏感性分析表明，自动驾驶汽车的优势在低市场渗透率时很难观察到，但当市场渗透率接近50%时，优势就会变得明显。总体而言，该框架阐明了自动驾驶社会性偏好何时以及为什么会随着时间的推移而成功，为设计混合交通中自适应、社会兼容的自动驾驶决策政策提供了可操作的指导。

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引用次数: 0

ROAR: Robust accident recognition and anticipation for autonomous driving ROAR：为自动驾驶提供强大的事故识别和预测

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-16 DOI: 10.1016/j.aap.2026.108414

Xingcheng Liu , Yanchen Guan , Haicheng Liao , Zhengbing He , Zhenning Li

Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections, which can significantly degrade prediction accuracy. Additionally, previous models have not adequately addressed the considerable variability in driver behavior and accident rates across different vehicle types. To overcome these limitations, this study introduces ROAR, a novel approach for accident detection and prediction. ROAR combines Discrete Wavelet Transform (DWT), a self-adaptive object-aware module, and dynamic focal loss to tackle these challenges. The DWT effectively extracts features from noisy and incomplete data, while the object-aware module improves accident prediction by focusing on high-risk vehicles and modeling the spatial–temporal relationships among traffic agents. Moreover, dynamic focal loss mitigates the impact of class imbalance between positive and negative samples. Evaluated on three widely used datasets — Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D) — our model consistently outperforms existing baselines in key metrics such as Average Precision (AP) and mean Time-to-Accident (mTTA). These results demonstrate the model’s robustness in real-world conditions, particularly in handling sensor degradation, environmental noise, and imbalanced data distributions. This work offers a promising solution for reliable and accurate accident anticipation in complex traffic environments.

准确的事故预测对于提高自动驾驶汽车的安全性至关重要。然而，现有的方法通常假设理想条件，忽略了传感器故障、环境干扰和数据缺陷等挑战，这些挑战会显著降低预测精度。此外，以前的模型没有充分解决驾驶员行为和不同类型车辆事故率的巨大差异。为了克服这些限制，本研究引入了一种新的事故检测和预测方法——ROAR。ROAR结合了离散小波变换（DWT）、自适应对象感知模块和动态焦点损失来解决这些挑战。DWT有效地从噪声和不完整的数据中提取特征，而目标感知模块通过关注高风险车辆和建模交通代理之间的时空关系来改进事故预测。此外，动态焦点损失减轻了正负样本之间类别不平衡的影响。在三个广泛使用的数据集——行车记录仪事故数据集（DAD）、汽车碰撞数据集（CCD）和汽车事故检测（A3D）上进行评估后，我们的模型在平均精度（AP）和平均事故发生时间（mTTA）等关键指标上始终优于现有的基线。这些结果证明了该模型在现实条件下的鲁棒性，特别是在处理传感器退化、环境噪声和不平衡数据分布方面。该研究为复杂交通环境下可靠、准确的事故预测提供了一种有前景的解决方案。

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引用次数: 0

Enhancing vision-based traffic crash detection performance consistency across day-night scenes: A depth-aware and domain-adaptive network 增强基于视觉的交通碰撞检测性能一致性跨昼夜场景：深度感知和领域自适应网络

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-15 DOI: 10.1016/j.aap.2026.108405

Yang Yang , Xiantian Chen , Jianyu Wang , Yue Dong , Kun Qie , Zhenzhou Yuan

Closed-circuit television (CCTV)-based traffic video crash detection systems require stable and consistent cross-scene performance to support all-day crash response and rescue efficiency. However, due to the substantial domain discrepancies between daytime and nighttime scenes—particularly in illumination and imaging quality—traffic crash detection still suffers from significant performance degradation when transferred across heterogeneous lighting conditions. To address this issue, this research proposed a depth-aware and domain-adaptive network built upon the Visual State Space Model (VSSM) to achieve robust crash detection across heterogeneous lighting environments. The proposed model employed a two-stream architecture that integrated appearance, motion and 3D depth information, in which the depth enhancement module captured fine-grained spatial geometry to provide complementary structural constraints, while the domain adaptation constraint effectively mitigated domain shift, thereby improving the overall robustness and reliability of crash detection. Experimental results demonstrated that the proposed model achieved a recall of 96.043 %, a miss rate of only 2.507 %, and an F1-score of 97.003 %, significantly outperforming several widely used baseline models. Ablation experiments further confirmed the critical roles of optical flow representation, 3D depth features, and the dual-level domain adaptation mechanism in enhancing spatiotemporal consistency. Moreover, the model required only 0.623 GFLOPs and achieved a real-time inference speed of 118 frames per second (FPS), demonstrating high computational efficiency. The proposed framework effectively mitigates the performance discrepancy between daytime and nighttime crash detection, and its high inference speed can contribute to faster emergency response and reduced casualty risk, offering a practical foundation for developing stable and transferable intelligent traffic safety monitoring systems.

基于闭路电视（CCTV）的交通视频碰撞检测系统需要稳定一致的跨场景性能，以支持全天的碰撞响应和救援效率。然而，由于白天和夜间场景之间存在很大的域差异，特别是在照明和成像质量方面，交通碰撞检测在不同的照明条件下仍然存在显著的性能下降。为了解决这一问题，本研究提出了一种基于视觉状态空间模型（VSSM）的深度感知和领域自适应网络，以实现跨异构照明环境的鲁棒碰撞检测。该模型采用融合外观、运动和三维深度信息的两流架构，其中深度增强模块捕获细粒度空间几何提供互补的结构约束，而领域自适应约束有效缓解了领域漂移，从而提高了碰撞检测的整体鲁棒性和可靠性。实验结果表明，该模型的召回率为96.043%，漏检率仅为2.507%，f1得分为97.003%，显著优于几种广泛使用的基线模型。消融实验进一步证实了光流表征、三维深度特征和双能级域适应机制在增强时空一致性中的关键作用。此外，该模型只需要0.623 GFLOPs，实现了118帧/秒的实时推理速度，显示出很高的计算效率。该框架有效地缓解了白天和夜间碰撞检测的性能差异，其较高的推理速度有助于加快应急响应速度，降低人员伤亡风险，为开发稳定、可转移的智能交通安全监控系统提供了实践基础。

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引用次数: 0

The role of ADHD in aggressive driving behavior among young adult drivers: effects of traffic aggressiveness and roadway environments ADHD在年轻成年司机攻击性驾驶行为中的作用：交通攻击性和道路环境的影响

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-14 DOI: 10.1016/j.aap.2026.108403

John M. Duany, Mustapha Mouloua, P.A. Hancock

This study examined the effects of Attention-Deficit Hyperactivity Disorder (ADHD), traffic aggressiveness, and roadway environment on driving behavior. Fifty-seven participants (26 ADHD, 31 non-ADHD; M_age = 20.75, SD = 5.19; 33 males, 24 females) completed questionnaires related to driving behavior. Participants then completed a series of simulated aggressive and non-aggressive drives in both city and freeway environments. Prior to the experimental drives, all participants completed a baseline control drive. Driving performance metrics (i.e., steering angle, acceleration pressure, brake pressure, and speed) and mental workload were recorded across all simulated drives. It was hypothesized that ADHD diagnosis, traffic aggressiveness, and roadway environment would each affect driving performance. Results showed that drivers with ADHD exhibited higher driving speed, while traffic aggressiveness and roadway environment exerted significant effects on steering angle and braking. Notably, ADHD drivers exhibited lower HRV (RMSSD), and NASA-TLX scores tended to be higher under aggressive city driving. The implications of these results for driver assessment, traffic safety, and public health are discussed.

本研究考察了注意缺陷多动障碍（ADHD）、交通攻击性和道路环境对驾驶行为的影响。57名参与者（26名ADHD， 31名非ADHD； Mage = 20.75, SD = 5.19； 33名男性，24名女性）完成了与驾驶行为相关的问卷调查。然后，参与者在城市和高速公路环境中完成了一系列模拟的攻击性和非攻击性驾驶。在实验驱动之前，所有参与者都完成了基线控制驱动。在所有模拟驾驶中记录驾驶性能指标（即转向角度、加速压力、制动压力和速度）和心理工作量。假设ADHD诊断、交通攻击性和道路环境都会影响驾驶表现。结果表明，ADHD驾驶员表现出更高的驾驶速度，而交通攻击性和道路环境对转向角和制动有显著影响。值得注意的是，ADHD司机表现出较低的HRV (RMSSD)，并且在激进的城市驾驶下，NASA-TLX得分往往更高。这些结果对驾驶员评估、交通安全和公众健康的影响进行了讨论。

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引用次数: 0

A doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort overlap 一个双重稳健的估计框架，量化与多队列重叠相关的碰撞- ems -创伤数据的潜在偏差。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2026-01-10 DOI: 10.1016/j.aap.2025.108380

Sajjad Karimi, Robert Kluger

Reliable estimation of injury severity is essential for informing trauma care, evaluating crash interventions, and guiding EMS resource allocation; however, analyses based on linked administrative datasets are often compromised by incomplete linkage and selection bias. This study employs a doubly robust estimation framework to address potential bias in injury severity estimation when integrating multiple datasets. Using Augmented Inverse Probability Weighting (AIPW), we adjust for selection bias introduced by incomplete linkage while improving robustness to misspecification in either the selection or outcome model. Using data from a multi-source linkage of crash, EMS, and trauma records, we estimate the Injury Severity Score (ISS) under three approaches: naïve complete-case analysis, inverse probability weighting (IPW), and AIPW. The naïve approach yielded a mean ISS of 13.52, while both IPW (10.86) and AIPW (10.93) provided adjusted estimates accounting for selection. Subgroup analyses revealed substantial differences in effect size and direction between models. For instance, the impact of male gender on ISS was estimated at 3.98 in AIPW versus 2.22 in naïve analysis. Similarly, secondary collisions and frontage-road crashes showed ISS increases exceeding 10 points under AIPW, compared to considerably lower naïve estimates. Several protective factors, including airbag deployment and crash setting, also demonstrated stronger effects when adjusted for bias. Our results demonstrate that traditional analyses of linked data may underestimate or misstate key risk and protective associations. The proposed AIPW framework offers a practical, statistically rigorous solution for producing population-level inferences in injury severity research using linked administrative data.

损伤严重程度的可靠估计对于创伤护理、评估碰撞干预措施和指导EMS资源分配至关重要；然而，基于关联管理数据集的分析常常受到不完全关联和选择偏差的影响。本研究采用双重稳健估计框架来解决在综合多个数据集时损伤严重程度估计的潜在偏差。使用增广逆概率加权（AIPW），我们调整了不完全链接引入的选择偏差，同时提高了对选择或结果模型中错误规范的鲁棒性。使用来自多源链接的碰撞、EMS和创伤记录的数据，我们用三种方法估计损伤严重程度评分（ISS）： naïve全病例分析、逆概率加权（IPW）和AIPW。naïve方法的平均ISS为13.52，而IPW（10.86）和AIPW（10.93）提供了考虑选择的调整估计。亚组分析揭示了模型之间效应大小和方向的实质性差异。例如，男性对ISS的影响在AIPW中估计为3.98，而在naïve分析中估计为2.22。同样，二次碰撞和正面道路碰撞显示，在AIPW下，ISS增加了10个点，而naïve的估计要低得多。几个保护因素，包括安全气囊打开和碰撞设置，在调整偏差后也显示出更强的效果。我们的研究结果表明，对关联数据的传统分析可能低估或错误地描述了关键风险和保护性关联。提出的AIPW框架提供了一个实用的、统计上严谨的解决方案，可以使用相关的行政数据在伤害严重程度研究中产生人口水平的推断。

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引用次数: 0

Street vitality and traffic risk: a multiscale analysis of Barcelona and Warsaw 街道活力和交通风险：巴塞罗那和华沙的多尺度分析

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.2026.108393

Anastasiia Galaktionova , Aura-Luciana Istrate , Tiago Tamagusko , Páraic Carroll

This study examines the relationship between street vitality and traffic crash risk in Barcelona and Warsaw using street-level spatial regression models and group-based temporal clustering. Street vitality, operationalised as functional density and computer-vision-derived streetscape safety scores, shows a positive association with crash frequency across the street network in both cities, supporting the hypothesis that lively streets increase exposure and possibilities for conflict. However, street vitality explains only part of this risk dynamic: street length remains the strongest predictor overall, while building density produces mixed effects. In hotspot models, street vitality effects weaken substantially; functional density becomes insignificant, and visual safety effects diminish, suggesting that once crash concentrations form, risk is shaped more by localised design and behavioural factors than by land-use intensity. These findings underscore the importance of combining system-wide and site-specific perspectives in street safety planning, highlighting the need for design interventions that reconcile lively public spaces with traffic safety.

本研究利用街道空间回归模型和基于群体的时间聚类分析了巴塞罗那和华沙街道活力与交通事故风险之间的关系。街道活力，以功能密度和计算机视觉衍生的街道景观安全得分来运作，显示出两个城市的街道网络与碰撞频率呈正相关，支持了热闹的街道增加暴露和冲突可能性的假设。然而，街道活力只解释了这种风险动态的一部分：街道长度仍然是总体上最强的预测因子，而建筑密度则产生了混合效应。在热点模式中，街道活力效应明显减弱；功能密度变得微不足道，视觉安全效应减弱，这表明，一旦形成碰撞集中，风险更多地取决于局部设计和行为因素，而不是土地使用强度。这些发现强调了在街道安全规划中结合系统范围和特定场地视角的重要性，强调了设计干预的必要性，以协调活跃的公共空间与交通安全。

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引用次数: 0

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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