Accident; analysis and prevention最新文献_第9页

Crash analysis for urban road networks: Semi-parametric spatial negative binomial models with monotonic constraints 城市道路网络碰撞分析：具有单调约束的半参数空间负二项式模型

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-29 DOI: 10.1016/j.aap.2025.108329

Miaojie Xia , Li Guan , Jiang Du , Yang Wang

Spatial heterogeneity constitutes an essential element in advanced crash frequency modeling frameworks. However, current spatial methodologies exhibit two primary limitations: (1) inflexible parametric formulations of exposure variables, and (2) non-conjugate model structures that compromise Gibbs sampler convergence. To address these issues, we develop a semi-parametric spatial count model that introduces flexible exposure specifications, requiring only monotonic functional relationships while explicitly incorporating spatial dependence. This framework employs data-augmented Gibbs sampling to achieve computationally efficient Bayesian estimation through conjugate forms. Simulation results verified that the estimation works as intended. Empirical evaluations across Houston and Dallas urban road networks demonstrated significant performance advantages relative to conventional approaches. Integration of estimates with roadway characteristics and exposure metrics yielded substantive insights into crash patterns within adjacent roadway segments. Road crash analysis in both cities showed three key patterns. First, segment length consistently serves as an offset variable, while annual average daily traffic (AADT) displays complex, segment-varying relationships due to unobserved heterogeneity. In Houston, the effect of AADT on crashes weakens under low traffic volumes but becomes nearly proportional (offset-like) at medium-to-high volumes. Conversely, Dallas exhibits more linear patterns, suggesting city-specific heterogeneity. Second, compared to one-way highway design, other designs generally pose higher risks, with lane number, width, and roadbed width increasing risk to different degrees, while better median designs reduce risk. Finally, both cities exhibit strong spatial heterogeneity in crash count. This framework enables transportation agencies to prioritize road safety interventions through precise quantification of crash risk factors and spatial heterogeneity, while avoiding restrictive parametric assumptions.

空间异质性是先进碰撞频率建模框架的重要组成部分。然而，目前的空间方法表现出两个主要的局限性：(1)暴露变量的参数公式不灵活；(2)非共轭模型结构损害吉布斯采样器收敛性。为了解决这些问题，我们开发了一种半参数空间计数模型，该模型引入了灵活的暴露规范，只需要单调函数关系，同时明确地结合了空间依赖性。该框架采用数据增强吉布斯抽样，通过共轭形式实现计算效率高的贝叶斯估计。仿真结果验证了该估计按预期工作。对休斯顿和达拉斯城市道路网络的实证评估表明，与传统方法相比，它们具有显著的性能优势。将估计与道路特征和暴露度量相结合，可以对相邻路段的碰撞模式产生实质性的见解。这两个城市的道路交通事故分析显示了三个关键模式。首先，区段长度始终作为一个抵消变量，而年平均日流量（AADT）由于未观察到的异质性而表现出复杂的区段变化关系。在休斯顿，AADT对交通事故的影响在低交通量时减弱，但在中高交通量时几乎成正比（类似于抵消）。相反，达拉斯呈现出更多的线性模式，表明城市特有的异质性。其次，与单向路设计相比，其他设计的风险普遍较高，车道数、宽度和路基宽度不同程度地增加了风险，而较好的中位数设计降低了风险。最后，两个城市在碰撞数量上都表现出很强的空间异质性。该框架使交通运输机构能够通过精确量化碰撞风险因素和空间异质性来优先考虑道路安全干预措施，同时避免限制性参数假设。

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引用次数: 0

A national randomized field experiment testing the impact of simulated usage-based insurance programs on driver safety 一项全国性随机现场实验，测试模拟基于使用的保险计划对驾驶员安全的影响

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-28 DOI: 10.1016/j.aap.2025.108331

Jeffrey P. Ebert , Catherine C. McDonald , Ruiying A. Xiong , Dina Abdel-Rahman , Neda Khan , Maya Irving , Athena Lee , Arjun Patel , Sadie Friday , Brian Tefft , William J. Horrey , Subhash Aryal , Michael O. Harhay , M.Kit Delgado

Many motor vehicle crashes are due to risky driving behaviors measurable by smartphone telematics applications. These apps are deployed by usage-based insurance (UBI) programs to estimate crash risk and price policies, incentivizing customers to drive safer. A preregistered randomized controlled trial (NCT06101251) tested whether a simulated UBI program improved driver safety, and whether being assigned or choosing focused goals yielded further improvement. Adult drivers (n = 1,449) were recruited nationally with social media advertisements. After a 6-week baseline period, participants were randomly assigned to one of four groups for a 12-week intervention: 1) Observation (control); 2) Standard Feedback (weekly texts about four risky behaviors, $100 incentive); 3) Assigned Goal (weekly texts about algorithmically assigned focus behavior and goal, $100 incentive); 4) Chosen Goal (weekly texts about self-chosen focus behavior and goal, $100 incentive). Post-intervention, participants were monitored for 6 more weeks. The primary outcome was a 0–100 score for overall safety (100 = safest). Standard Feedback (covariate-adjusted difference, 2.0 [95 % CI, 1.2–2.7]; P < 0.001), Assigned Goal (2.7 [95 % CI, 2.0–3.5]; P < 0.001), and Chosen Goal (2.2 [95 % CI, 1.4–3.0]; P < 0.001) had higher safety scores than control during the intervention period and at follow-up. Differences between treatment groups were not significant after adjusting for number of comparisons, Ps > 0.10. Treatment groups had significant relative reductions in speeding (11 %–13 %, depending on group), hard braking (16 %–21 %), and rapid acceleration (16 %–25 %), Ps < 0.01. The kind of feedback and incentives offered by UBI programs can cause sustained improvements in driver safety.

许多机动车事故是由智能手机远程信息处理应用程序可测量的危险驾驶行为造成的。这些应用程序由基于使用情况的保险（UBI）计划部署，用于评估撞车风险和价格政策，激励客户更安全驾驶。一项预先注册的随机对照试验（NCT06101251）测试了模拟UBI计划是否提高了驾驶员的安全性，以及分配或选择重点目标是否会进一步提高安全性。通过社交媒体广告在全国招募成年司机（n = 1449）。在6周的基线期后，参与者被随机分配到四组中的一组，进行为期12周的干预：1)观察（对照组）；2)标准反馈（每周短信四项风险行为，奖励100美元）；3)指定目标（每周文本关于算法分配的焦点行为和目标，奖励100美元）；4)选择目标（每周发送关于自我选择的专注行为和目标的短信，奖励100美元）。干预后，参与者被监测6周以上。主要结果为总体安全性评分0-100分（100 =最安全）。标准反馈组（协变量调整差，2.0 [95% CI, 1.2-2.7]; P < 0.001）、指定目标组（2.7 [95% CI, 2.0 - 3.5]; P < 0.001）和选择目标组（2.2 [95% CI, 1.4-3.0]; P < 0.001）在干预期和随访时的安全性评分高于对照组。调整比较数后，各组间差异不显著，p > 0.10。治疗组在超速（11% - 13%，取决于组）、急刹车（16% - 21%）和快速加速（16% - 25%）方面有显著的相对降低，p < 0.01。UBI计划提供的反馈和激励措施可以持续改善驾驶员的安全。

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引用次数: 0

A vine copula-based analysis of spatial dependence of traffic conflict risk at highway ramp areas 公路匝道区域交通冲突风险空间依赖性分析

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-26 DOI: 10.1016/j.aap.2025.108330

Ruifeng Gu, N.N. Sze

Understanding spatial dependence on real-time safety risk is crucial for developing effective traffic management and control strategies at highway ramp areas. Prevalent statistical models for spatial analysis of safety risk are limited in their capability to capture complex, non-linear, and asymmetric dependence patterns across multiple locations. In this study, a novel vine copula approach is proposed for the spatial analysis of traffic conflict risk at highway ramp area, allowing for more flexible specification and control of heterogeneous spatial dependence patterns. Specifically, spatial correlations in traffic conflict risk, measured by time-integrated time-to-collision (TIT) across road segments and traffic lanes, are estimated using high-resolution vehicle trajectory data collected from unmanned aerial units. The results reveal significant variations in spatial dependence patterns of traffic conflict risk, particularly asymmetric tail dependencies in high-risk scenarios. These findings underscore the need for advanced statistical models to accommodate spatial dependence in traffic safety analysis, thereby supporting the development and implementation of more effective real-time traffic safety management systems.

了解实时安全风险的空间依赖性对于制定有效的公路匝道区域交通管理和控制策略至关重要。用于安全风险空间分析的流行统计模型在捕获多个位置的复杂、非线性和不对称依赖模式方面的能力有限。本文提出了一种新的藤联结方法，用于高速公路匝道区域交通冲突风险的空间分析，可以更灵活地规范和控制异质性空间依赖模式。具体而言，利用从无人机收集的高分辨率车辆轨迹数据，估算交通冲突风险的空间相关性，通过跨路段和交通车道的时间积分碰撞时间（TIT）来衡量。结果表明，交通冲突风险的空间依赖模式存在显著差异，特别是在高风险情景下的非对称尾部依赖。这些发现强调需要先进的统计模型来适应交通安全分析中的空间依赖性，从而支持开发和实施更有效的实时交通安全管理系统。

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引用次数: 0

The impact of N-back-induced mental workload and time budget on takeover performance n -back诱导的心理工作量和时间预算对接管绩效的影响

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-25 DOI: 10.1016/j.aap.2025.108327

Courtney M. Goodridge , Rafael C. Gonçalves , Ali Arabian , Anthony Horrobin , Albert Solernou , Yee Thung Lee , Audrey Bruneau , Yee Mun Lee , Natasha Merat

Mental Workload (MWL) refers to the specification of information processing capacity used for maintaining task performance. Some studies find no effects of high MWL on the timing and quality of takeovers, whilst others have found increases in crash risk and delayed response times. The effect of time budget – the time between event onset and an impending crash − is much clearer; drivers react faster when time budgets are smaller. However, no study has investigated whether the effects of a pure MWL interact with the effects of time budget during critical takeovers from a hands-off Level 2 (L2) driving system. A Bayesian multilevel modelling approach was used to quantify the direction, size, and uncertainty of the effects that MWL and time budget have on driver performance. Drivers (N = 37) used a hands-off L2 driving system: once while completing a pure MWL task (2-back) and another while monitoring the road. Rear-end conflicts were generated via lead vehicles decelerating with short (TTC = 3 s) or long (TTC = 5 s) time budgets. 2-back-induced MWL had no consistent or substantial impact on the timing or quality of takeovers. Conversely, drivers were faster to respond but more erratic in their post-takeover lateral control following events with smaller time budgets. We discuss the reasons for the absence of effects from the 2-back-induced MWL on takeover performance. One suggestion is that rear-end scenarios elicit automatised behaviours that do not rely upon cognitive control and thus remained unaffected by MWL. Conversely, scenarios that require cognitive control (e.g., lane change manoeuvres or hazard perception tasks) may be more susceptible to the detrimental effects of MWL during transitions of control.

心智负荷（Mental Workload， MWL）是指用于维持任务绩效的信息处理能力。一些研究发现，高MWL对收购的时机和质量没有影响，而另一些研究则发现，破产风险和反应时间延迟会增加。时间预算（事件发生和即将发生的崩溃之间的时间）的影响要清晰得多；时间预算越少，司机反应越快。然而，目前还没有研究调查纯MWL的影响是否与时间预算的影响相互作用，在关键的接管过程中，从不干涉的L2驾驶系统。贝叶斯多级建模方法用于量化MWL和时间预算对驾驶员性能影响的方向、大小和不确定性。驾驶员（N = 37）使用不干涉L2驾驶系统：一次在完成纯MWL任务（2-back）时，另一次在监控道路时。追尾冲突是由前车在较短（TTC = 3秒）或较长（TTC = 5秒）的时间预算下减速而产生的。反向诱导的MWL对收购的时机或质量没有持续或实质性的影响。相反，在时间预算较小的事件发生后，司机的反应更快，但在收购后的横向控制方面更不稳定。我们讨论了两回诱导的MWL对接管性能没有影响的原因。一种说法是，追尾场景引发了不依赖于认知控制的自动化行为，因此不受MWL的影响。相反，需要认知控制的场景（例如，变道操作或危险感知任务）在控制过渡期间可能更容易受到MWL的有害影响。

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引用次数: 0

Collision risk identification and prediction considering heterogeneous braking patterns using large-scale pre-collision trajectories 基于大规模碰撞前轨迹的非均匀制动模式碰撞风险识别与预测。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-24 DOI: 10.1016/j.aap.2025.108312

Xudong Ren , Lu Bai , Pan Liu , Wai Wong , Jin Liu , Shuo Kong , Bicheng Xu

Rear-end collisions often occur in vehicles when successive braking events lead to insufficient deceleration by one or more vehicles. This paper proposed a novel method for collision risk identification and prediction by integrating the braking dynamics of both leading and following vehicles in pre-crash scenarios. Using a piecewise linear model of deceleration profiles, 45 collision risk moments were identified across 10 scenarios based on ten kinematic parameters. A novel critical time-to-collision metric was proposed to integrate both the timing and execution of braking behaviors into collision risk prediction. To account for driver heterogeneity in deceleration, deceleration rate, and reaction time, Gaussian mixture regression was used to perform conditional inference to estimate braking pattern parameters and generate interval-valued crash risk predictions. The performance and optimal threshold were validated using large-scale vehicle trajectories from collision and non-collision events. The results demonstrate that the collision risk moments vary with both the braking timing and execution of the leading and following vehicles. The proposed metric outperformed traditional surrogate safety measures in predicting collision risk, consistently yielding higher accuracy with less variability across pre-crash time intervals. These findings indicate that the estimated critical time-to-collision is a reliable and effective measure for collision avoidance systems and advanced driver assistance technologies.

当连续的制动事件导致一辆或多辆车的减速不足时，车辆经常发生追尾碰撞。提出了一种基于前车和跟车制动动力学的碰撞风险识别与预测方法。采用分段线性减速曲线模型，基于10个运动学参数，确定了10种情况下的45个碰撞风险时刻。提出了一种新的碰撞临界时间度量，将制动行为的时序和执行同时纳入碰撞风险预测。为了考虑驾驶员在减速、减速率和反应时间方面的异质性，使用高斯混合回归进行条件推断，以估计制动模式参数并生成区间值碰撞风险预测。使用碰撞和非碰撞事件的大规模车辆轨迹验证了性能和最优阈值。结果表明，碰撞风险矩随前车和跟车制动时机和执行情况的不同而变化。该指标在预测碰撞风险方面优于传统的替代安全措施，在碰撞前的时间间隔内始终保持更高的准确性和更小的可变性。这些研究结果表明，碰撞临界时间的估计是碰撞避免系统和先进驾驶辅助技术的可靠和有效的度量。

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引用次数: 0

Evaluating causes of animal-vehicle collisions through the lens of driver behavior 通过驾驶员行为来评估动物与车辆碰撞的原因

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-21 DOI: 10.1016/j.aap.2025.108314

Carson J. Pakula , Olin E. Rhodes Jr. , Travis L. DeVault

Animal-vehicle collisions (AVCs) are ubiquitous in developed regions of the world and pose risks to both wildlife and humans. In the United States, collisions with deer (Odocoileus spp.) cause billions of dollars in economic losses and thousands of human injuries annually. The current AVC literature has largely focused on factors unrelated to driver behavior including AVC hotspots, wildlife movement, and damages caused by AVCs. However, despite being a component in every AVC, few studies have investigated driver behavior during animal-vehicle interactions. Here, we systematically reviewed literature databases to identify factors influencing driver behavior during these interactions and to highlight apparent gaps in the literature. We found that vehicle speed, road attributes, environmental conditions, and vehicle types show inconsistent associations with AVCs and the mechanisms by which they influence driver behavior is not well understood. Many studies focused on mitigation methods to influence driver behavior, including various warning signs; however, the effectiveness of these systems varies considerably. Other topics including wildlife attributes, roadway illumination, and inherent driver attributes directly influence driver behavior, but are understudied. Most studies relied on seemingly logical explanations for results or associations between variables to identify these influences, but few studies directly tested how specific variables influenced driver behavior and detection ability of wildlife. Given that driver behavior influences every potential AVC, future research should directly investigate the behavioral and perceptual mechanisms behind driver detection of wildlife and other factors influencing overall driver behavior during wildlife-vehicle interactions.

动物与车辆碰撞（AVCs）在世界发达地区普遍存在，对野生动物和人类都构成威胁。在美国，与鹿（Odocoileus spp.）的碰撞每年造成数十亿美元的经济损失和数千人受伤。目前的AVC文献主要集中在与驾驶员行为无关的因素上，包括AVC热点、野生动物运动和AVC造成的损害。然而，尽管这是每个AVC的组成部分，但很少有研究调查动物与车辆相互作用时驾驶员的行为。在这里，我们系统地回顾了文献数据库，以确定在这些相互作用中影响驾驶员行为的因素，并突出了文献中的明显空白。我们发现，车速、道路属性、环境条件和车辆类型与AVCs的关联并不一致，而且它们影响驾驶员行为的机制也不清楚。许多研究侧重于影响驾驶员行为的缓解方法，包括各种警告标志；然而，这些系统的有效性差别很大。其他主题包括野生动物属性、道路照明和固有驾驶员属性直接影响驾驶员行为，但尚未得到充分研究。大多数研究依赖于对结果的看似合乎逻辑的解释或变量之间的关联来确定这些影响，但很少有研究直接测试特定变量如何影响驾驶员行为和野生动物的检测能力。鉴于驾驶员行为影响每一个潜在的AVC，未来的研究应直接研究驾驶员检测野生动物背后的行为和感知机制，以及野生动物与车辆交互过程中影响驾驶员整体行为的其他因素。

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引用次数: 0

The hazards of skipping meals and losing sleep for ride-hailing drivers—evidence based on quasi-experimental and experience sampling methodology 不吃饭和失眠对网约车司机的危害——基于准实验和经验抽样方法的证据。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-19 DOI: 10.1016/j.aap.2025.108304

Jingming Feng, Jian Chen, Tao Su, Huijian Fu, Ling Tan

The rapid growth of ride-hailing services, driven by big data and platform algorithms, has intensified physiological stressors among drivers, particularly during peak mealtime periods, where hunger and sleep deprivation (“skipping meals and losing sleep”) have become prevalent occupational hazards. While prior research has separately documented the adverse effects of hunger and sleep loss on self-control, their synergistic impact on driving safety remains underexplored, especially in ecologically valid settings. Guided by the self-control strength model, this study investigated how hunger and sleep deprivation interact to influence dangerous driving behaviours among ride-hailing drivers, examining the mediating role of self-control resource depletion and the moderating effects of sleep deprivation and work identity perception (contractor vs. worker).

Using a mixed-methods approach, Study 1 (N = 70 students) employed a 2 × 2 mixed quasi-experiment to establish baseline effects, revealing that hunger directly depleted self-control resources (measured via Stroop task), with sleep deprivation exacerbating this relationship. Study 2 extended these findings to 153 active ride-hailing drivers using a 2 × 2 between-subjects quasi-experiment, confirming that hungry drivers exhibited greater self-control resource depletion than fed drivers, particularly under sleep deprivation. Study 3 utilized experience sampling with 76 drivers (651 data points) to test cross-level mechanisms, demonstrating that hunger indirectly increased dangerous driving behaviors through self-control depletion, with sleep deprivation and worker identity perception amplifying these effects.

These findings highlight the compounding risks of physiological stressors in ride-hailing work, emphasizing the need for interventions targeting both hunger management and sleep hygiene. The study contributes to accident prevention research by integrating ecological validity with theoretical rigor, offering actionable insights for drivers and platform operators to mitigate fatigue-related safety risks.

在大数据和平台算法的推动下，网约车服务的快速增长加剧了司机的生理压力，尤其是在用餐高峰期，饥饿和睡眠不足（“不吃饭、失眠”）已成为普遍的职业危害。虽然先前的研究分别记录了饥饿和睡眠不足对自我控制的不利影响，但它们对驾驶安全的协同影响仍未得到充分探讨，特别是在生态有效的环境中。在自我控制强度模型的指导下，本研究探讨了饥饿和睡眠剥夺如何相互作用影响网约车司机的危险驾驶行为，考察了自我控制资源枯竭的中介作用以及睡眠剥夺和工作认同感知（承包商与工人）的调节作用。采用混合方法，研究1 （N = 70名学生）采用2 × 2混合准实验来建立基线效应，揭示饥饿直接消耗自我控制资源（通过Stroop任务测量），睡眠剥夺加剧了这种关系。研究2将这些发现扩展到153名活跃的网约车司机，使用2x2受试者之间的准实验，证实饥饿的司机比饱食的司机表现出更大的自我控制资源消耗，尤其是在睡眠不足的情况下。研究3利用76名司机（651个数点）的经验抽样来检验跨水平机制，表明饥饿通过自我控制消耗间接增加危险驾驶行为，而睡眠剥夺和工人身份感知放大了这些影响。这些发现强调了网约车工作中生理压力源的复合风险，强调了针对饥饿管理和睡眠卫生的干预措施的必要性。该研究通过将生态有效性与理论严谨性相结合，为事故预防研究做出了贡献，为司机和平台运营商提供了可操作的见解，以减轻疲劳相关的安全风险。

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引用次数: 0

Profiling crash-associated factors and injury risk patterns among lost-in-thought (daydreaming) drivers: a combined cluster-sequence analysis approach 分析心不在焉（白日做梦）司机的撞车相关因素和伤害风险模式：一种组合聚类-序列分析方法。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-19 DOI: 10.1016/j.aap.2025.108315

Panick Kalambay , Philip Balyagati , Angela Kitali , Emmanuel Kidando

Cognitive distraction, particularly in the form of being lost in thought or daydreaming, is a significant yet underexamined contributor to traffic crashes. This study investigates crash patterns and risk profiles associated with drivers who experienced lost-in-thought distraction at the time of a crash in Washington State. Using cluster correspondence analysis (CCA), distinct associations between crash characteristics and driver attributes were uncovered. In addition, process mining was employed to identify typical sequences of crash events. Three meaningful clusters emerged. Cluster 1 involved crashes on road segments with speed limits exceeding 40 mph, lacking traffic control, and often involving male drivers in clear weather. Cluster 2 was marked by crashes at signalized intersections under partly cloudy or foggy conditions, with a higher likelihood of injury. Cluster 3 reflected rainy or low-light crashes involving young drivers on curved, divided, high-speed roads. Across all clusters, frequent crash sequences included collisions with vehicles in transport, parked cars, and fixed objects. Cluster 1 and Cluster 2 stood out for their distinct contextual characteristics. Cluster 1 crashes often involved crossing the center line, suggestive of deep cognitive distraction. In Cluster 2, crashes frequently occurred after drivers stopped at flashing red lights or stop signs, then proceeded, indicating momentary lapses in attention. The study also highlights the limitations of current crash data and emphasizes the need for standardized reporting of distraction-related incidents. Findings support context-specific countermeasures, such as signal enhancements, curve warnings, and distraction-focused training for novice drivers, to address the multifaceted risks associated with lost-in-thought crashes.

认知分心，尤其是陷入思考或做白日梦的形式，是导致交通事故的一个重要因素，但尚未得到充分研究。这项研究调查了在华盛顿州发生的撞车事故中，经历过分心的司机的撞车模式和风险状况。使用聚类对应分析（CCA），揭示了碰撞特征和驾驶员属性之间的明显关联。此外，还采用流程挖掘来识别典型的崩溃事件序列。三个有意义的集群出现了。第一类事故发生在限速超过每小时40英里的路段，缺乏交通控制，通常是晴天的男性司机。第二组的标志是在部分多云或有雾的天气下，在有信号的十字路口发生撞车事故，造成伤害的可能性更高。集群3反映了雨天或弱光下年轻司机在弯曲、分隔的高速公路上发生的撞车事故。在所有集群中，频繁的碰撞序列包括与运输中的车辆、停放的汽车和固定物体的碰撞。集群1和集群2因其独特的上下文特征而脱颖而出。第一类撞车事故通常涉及越过中线，暗示着严重的认知分心。在第二组中，车祸经常发生在司机在闪烁的红灯或停车标志前停车，然后继续行驶后，这表明司机的注意力有短暂的失误。该研究还强调了当前碰撞数据的局限性，并强调了对分心相关事件进行标准化报告的必要性。研究结果支持针对具体情况的对策，如信号增强、弯道警告和针对新手驾驶员的注意力分散培训，以解决与失神碰撞相关的多方面风险。

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引用次数: 0

Why do drivers brake later than AEB in rear-end collisions? An analysis based on drive recorder videos 在追尾事故中，为什么司机刹车晚于AEB ？基于硬盘录像机视频的分析。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-19 DOI: 10.1016/j.aap.2025.108275

Yuqing Zhao, Yifan Lin

Rear-end collisions are among the most common crash types in Japan. However, due to their relatively low fatality and injury severity, research on them remains limited. Although previous studies have shown the effectiveness of autonomous emergency braking (AEB), further studies is needed to improve understanding of driver-related factors and enhance system performance to reduce collision risk.

This study investigated the effectiveness of AEB and explored factors contributing to braking delays by reconstructing 52 real-world taxi collisions exceeding 10 km/h using PC-Crash. Simulations were conducted with time-to-collision (TTC) and TTC_2nd-based (considering relative deceleration) AEB to evaluate collision-avoidance performance. Additionally, a decision tree was used to examine environmental, vehicular, and human factors affecting the time difference between driver-initiated braking and AEB activation.

The result indicates that AEB installed in taxis effectively reduced rear-end collisions. TTC_2nd-based AEB could avoid more collisions involving decelerating lead vehicles. Reducing AEB delay times could further enhance prevention. However, the AEB simulation showed limited effectiveness in collisions involving high speeds, wet roads, or sudden deceleration of the lead vehicle. Moreover, the primary reason drivers braked later than the simulated AEB was their failure to maintain a forward gaze before collisions. Drivers were more likely to divert their gaze from forward driving-related areas in non-critical situations, such as low-speed driving or red traffic signals ahead.

This study provides quantitative insights into human factors and AEB technology, which may inform the optimization of AEB systems and the development of driver monitoring systems, contributing to collision prevention and traffic safety.

追尾事故是日本最常见的事故类型之一。然而，由于其相对较低的致死率和损伤严重程度，对其的研究仍然有限。虽然之前的研究已经证明了自动紧急制动（AEB）的有效性，但需要进一步的研究来提高对驾驶员相关因素的理解，提高系统性能以降低碰撞风险。本研究通过PC-Crash软件重建52起超过10公里/小时的出租车碰撞事故，研究了AEB的有效性，并探讨了导致制动延迟的因素。采用碰撞时间（TTC）和基于ttc2（考虑相对减速）的AEB进行仿真，评估避碰性能。此外，还使用决策树来检查影响驾驶员主动制动和AEB启动时间差的环境、车辆和人为因素。结果表明，在出租车上安装AEB可以有效减少追尾事故。基于ttc2的AEB可以避免更多涉及减速车辆的碰撞。减少AEB延迟时间可以进一步加强预防。然而，AEB模拟显示，在涉及高速、潮湿道路或领先车辆突然减速的碰撞中，AEB的有效性有限。此外，司机刹车晚于模拟AEB的主要原因是他们在碰撞前未能保持前视。在非紧急情况下，比如低速行驶或前方有红色交通信号时，司机更有可能将目光从前方驾驶相关区域转移开。该研究为人为因素和AEB技术提供了定量的见解，可为AEB系统的优化和驾驶员监控系统的开发提供参考，有助于预防碰撞和交通安全。

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引用次数: 0

Have they taken effect as expected? Unpacking the black box of road safety countermeasure effects 它们是否如预期的那样生效？打开道路安全对策的黑箱效果。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-11-19 DOI: 10.1016/j.aap.2025.108309

Yingheng Zhang , Haojie Li

Countermeasure evaluation is an important subfield of road traffic safety research. Numerous works have been carried out to establish the cause–effect relationship between countermeasures and crash-related outcomes. However, a single estimate of the countermeasure effect may not always reflect their actual road safety benefits since the underlying causal mechanisms are neglected. To be specific, conventional evaluation frameworks cannot provide a comprehensive view of how a countermeasure influences defined outcomes of interest, e.g., via influencing a specific intermediate variable (mediator). This paper aims to unpack the black box by exploiting the causal inference framework for mediation analysis, which is able to decompose the total causal effect into the indirect effect that relays through the putative mediator and the direct effect. In particular, this framework builds on the potential outcome paradigm, which offers general definitions for the causal estimands of interest and clarifies the core assumptions for nonparametric identification. As a major superiority over traditional mediation analysis, evaluators are permitted to choose from a wide range of statistical methods to estimate the component models (e.g., the mediator model and the outcome model). Simulation experiments are conducted to show the applicability of the causal mediation method to count data analysis with negative binomial models. Also, the method is applied to a speed enforcement camera case study to examine the presence of crash migration phenomena by modelling traffic volume as mediator. Our empirical analysis has found a null indirect effect on crash frequency at enforced road sites, implying that crash migration due to drivers choosing alternative routes to avoid speed cameras is unlikely in this particular case. In summary, by properly applying the causal mediation method to countermeasure evaluation, we could gain a deeper insight into systematic causality.

对策评价是道路交通安全研究的一个重要分支。已经开展了大量的工作来建立对策与碰撞相关结果之间的因果关系。然而，由于忽视了潜在的因果机制，对对策效果的单一估计可能并不总是反映其实际的道路安全效益。具体而言，传统的评估框架无法全面了解对策如何影响已定义的利益结果，例如通过影响特定的中间变量（中介）。本文旨在通过中介分析的因果推理框架来打开黑箱，该框架能够将总因果效应分解为通过假定中介传递的间接效应和直接效应。特别是，该框架建立在潜在结果范式的基础上，该范式为感兴趣的因果估计提供了一般定义，并澄清了非参数识别的核心假设。作为传统中介分析的主要优势，评估者可以从广泛的统计方法中进行选择，以估计成分模型（例如，中介模型和结果模型）。通过仿真实验验证了因果中介方法在负二项模型计数数据分析中的适用性。此外，该方法被应用于一个超速执法摄像机的案例研究，通过模拟交通量作为中介来检查碰撞迁移现象的存在。我们的实证分析发现，在强制道路上，事故发生频率的间接影响为零，这意味着在这种特殊情况下，由于驾驶员选择替代路线以避开超速摄像头而导致的事故迁移不太可能发生。综上所述，适当地将因果中介方法应用于对策评价，可以更深入地了解系统因果关系。

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引用次数: 0