Analytic Methods in Accident Research最新文献

Bayesian forecasting of short-term crash risk with conditional extreme value models: A comparison between one-stage and two-stage approaches 条件极值模型的短期崩溃风险贝叶斯预测：一阶段和两阶段方法的比较

IF 12.6 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-12-01 DOI: 10.1016/j.amar.2025.100409

Depeng Niu, Tarek Sayed

Extreme Value Theory (EVT) has become a widely used approach for quantifying crash risk from traffic conflict data. Most existing applications, however, rely on unconditional models, which fail to adequately capture dependence in extreme traffic conflicts and do not reliably predict future crash risk. To demonstrate the potential of conditional EVT models for advancing short-term crash risk forecasting, this study compares two conditional EVT approaches within a Bayesian framework that address extremal dependence from distinct perspectives. The first approach is the two-stage GARCH-EVT framework, where conditional mean and variance are modeled using GARCH-type specifications before EVT is applied to the standardized residuals. Both traditional and covariate-augmented variants are examined. The second approach uses a one-stage conditional peak-over-threshold (POT) model, represented by the score-driven POT model, which directly captures dynamics in the conditional exceedance probability and the distribution of exceedance sizes. Crash risk is quantified using two conditional tail risk measures, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), with forecasting performance evaluated through traditional and comparative backtesting. An empirical study examines rear-end conflicts collected at two signalized intersections over four observation days to generate one-cycle-ahead crash risk forecasts during the out-of-sample period. Traditional backtesting indicates that both the covariate-augmented GARCH-EVT and the score-driven POT approaches produce valid and comparable forecasts, with the two-stage method yielding estimates with lower uncertainty. Comparative backtesting, however, shows that the score-driven POT model achieves slightly superior forecasting accuracy. The weaker performance of the two-stage framework can be attributed to partial removal of extremal dependence, sensitivity to substitute values in cycles without conflicts, and the limitations inherent in its two-stage structure.

极值理论（Extreme Value Theory， EVT）已成为一种广泛应用于交通冲突数据中碰撞风险量化的方法。然而，大多数现有的应用程序依赖于无条件模型，这些模型不能充分捕捉极端交通冲突中的依赖性，也不能可靠地预测未来的碰撞风险。为了证明条件EVT模型在推进短期崩溃风险预测方面的潜力，本研究比较了贝叶斯框架内的两种条件EVT方法，这些方法从不同的角度解决了极端依赖性。第一种方法是两阶段GARCH-EVT框架，在EVT应用于标准化残差之前，使用garch类型规范对条件均值和方差进行建模。传统的和协变量增广的变体进行了检查。第二种方法采用单阶段条件峰值超过阈值（POT）模型，由分数驱动的POT模型表示，该模型直接捕获条件超越概率和超越大小分布中的动态。采用风险价值（VaR）和条件风险价值（CVaR）两个条件尾部风险度量来量化崩溃风险，并通过传统回溯测试和比较回溯测试来评估预测绩效。一项实证研究考察了在四个观察日内收集的两个信号交叉口的追尾冲突，以在样本外期间生成一个周期前的碰撞风险预测。传统的回溯检验表明，协变量增强的GARCH-EVT和分数驱动的POT方法都能产生有效的、可比较的预测，两阶段方法产生的估计具有较低的不确定性。然而，对比回测表明，分数驱动的POT模型的预测精度略高。两阶段框架较弱的表现可归因于部分去除极值依赖性，对无冲突循环中的替代值敏感，以及其两阶段结构固有的局限性。

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引用次数: 0

A note on observed injury bias in police-reported pre-crash travel speed estimates 关于警方报告的碰撞前行驶速度估计中观察到的伤害偏见的说明

IF 12.6 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-10-12 DOI: 10.1016/j.amar.2025.100407

Mouyid Islam , Fred Mannering

Vehicle pre-crash travel speed is one of the most important determinants of driver injury severity. However, pre-crash travel speed estimates made by police officers, especially those in crashes with less severe injuries (where there is less of a need for high levels of accuracy due to potential litigation), can be susceptible to biases because of the tendency to associate less severe driver injuries with lower pre-crash travel speeds. This potential bias makes the use of pre-crash travel speeds in injury-severity modeling highly problematic due to its endogeneity with injury severity. To detect the presence and extent of this problem, a bias correction term for pre-crash travel speed estimation equations is applied by treating injury-severity level (discrete) and pre-crash travel speed (continuous) as a discrete/continuous econometric model. The findings show that for severe injury crashes, the bias correction is statistically insignificant, reflecting the increased accuracy required of police officers in severe crashes. However, for crashes resulting in less severe occupant injuries, there is a significant bias resulting from observed injury levels, which distorts the effects of explanatory variables on pre-crash travel speed estimates. The results of this paper not only provide empirical evidence of potential endogeneity problems in models of crash injury severity but also underscore the need to more fully consider potential endogeneity issues and their associated consequences in statistical models and machine learning models.

车辆碰撞前行驶速度是驾驶员损伤严重程度的重要决定因素之一。然而，警察在碰撞前的行驶速度估计，特别是那些受伤不太严重的事故（由于潜在的诉讼，不太需要高水平的准确性），可能容易受到偏见的影响，因为倾向于将较轻的驾驶员伤害与较低的碰撞前行驶速度联系起来。由于碰撞前行驶速度与损伤严重程度内生性一致，这种潜在的偏差使得在损伤严重程度建模中使用碰撞前行驶速度非常成问题。为了检测该问题的存在和程度，通过将伤害严重程度（离散）和碰撞前行驶速度（连续）作为离散/连续计量模型，应用碰撞前行驶速度估计方程的偏差校正项。研究结果表明，对于严重伤害事故，偏差校正在统计上不显著，反映了在严重事故中对警察的准确性要求的提高。然而，对于导致乘员伤害较轻的碰撞，观察到的伤害水平会产生显著的偏差，这扭曲了解释变量对碰撞前行驶速度估计的影响。本文的结果不仅提供了碰撞损伤严重程度模型中潜在内生性问题的经验证据，而且强调了在统计模型和机器学习模型中更充分考虑潜在内生性问题及其相关后果的必要性。

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引用次数: 0

A note on random parameters models of crash injury severities with k-means clustering for data preprocessing 基于k-均值聚类的碰撞损伤严重性随机参数模型预处理研究

IF 12.6 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-10-12 DOI: 10.1016/j.amar.2025.100408

Nawaf Alnawmasi , Fred Mannering

Many recent studies have shown that data segmentation (seeking to segment the data into potentially homogeneous groups by factors such as data-collection year, driver age, driver gender, driver behaviors, etc.) can significantly improve crash injury-severity model estimation results. However, the choice of the segmentation criterion is often speculative and based on a predetermined expectation of homogeneity by the analyst. In an effort to improve model estimation results, a potential alternative to analyst-specified data segmentation is to preprocess the data using multivariate machine learning techniques. This paper demonstrates the potential of data preprocessing using k-means clustering as a means to improve the estimation of statistical models. Empirical results show that the combination of k-means clustering, in addition to data segmentation by year to account for temporal shifts in parameters, result in an improved statistical fit (a hybrid of analyst-specified and machine learning data segmentation). Furthermore, a comparison of the marginal effects generated by the clustered and non-clustered models suggests that the preprocessing of data by clustering techniques can result in more precise marginal effect estimates to guide safety policies. The findings show considerable potential for using machine learning algorithms, such as k-means clustering, to improve the estimation results of statistical models.

最近的许多研究表明，数据分割（试图通过数据收集年份、驾驶员年龄、驾驶员性别、驾驶员行为等因素将数据分割成潜在的同质组）可以显著改善碰撞伤害严重程度模型的估计结果。然而，分割标准的选择通常是推测性的，并且基于分析人员对同质性的预定期望。为了改进模型估计结果，分析师指定的数据分割的潜在替代方案是使用多变量机器学习技术预处理数据。本文展示了使用k-means聚类作为改进统计模型估计的手段的数据预处理的潜力。经验结果表明，k-means聚类的结合，除了按年进行数据分割以考虑参数的时间变化外，还可以改善统计拟合（分析师指定和机器学习数据分割的混合）。此外，对聚类模型和非聚类模型产生的边际效应进行了比较，表明通过聚类技术对数据进行预处理可以得到更精确的边际效应估计，从而指导安全政策。这些发现显示了使用机器学习算法（如k-means聚类）来改进统计模型的估计结果的巨大潜力。

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引用次数: 0

Joint analysis on pedestrian injury severity across vehicle movements at intersections: Addressing temporal instability and spatial correlations 交叉路口车辆运动对行人伤害严重程度的联合分析：解决时间不稳定性和空间相关性

IF 12.6 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-10-07 DOI: 10.1016/j.amar.2025.100406

Chenzhu Wang, Mohamed Abdel-Aty, Natalia Barbour

Intersection-related vehicle–pedestrian collisions present a significant challenge in transportation safety due to the complexity and hazards of intersections within urban road networks. This study introduces a spatially aggregated ordered logit model with a joint multivariate normal structure, which offers distinct advantages over conventional models by effectively capturing correlations among vehicle movement types (left-turn, straight, and right-turn) and accounting for residual aggregation at both intersection and county levels. Using a dataset of 4280 pedestrian-vehicle crashes in Florida from 2019 to 2023, incorporating pedestrian, driver, vehicle, intersection, environmental, crash, and temporal characteristics, the proposed model demonstrates superior performance in capturing interdependencies among vehicle maneuvers. Four temporally consistently significant variables are identified including pedestrians aged under 18 years old, urban areas, major roadway speed limits below 30 mph and lighted roadways during nighttime. In contrast, several other variables demonstrate significance only in specific years, reflecting notable temporal variation in their impact on pedestrian injury severity. A series of statistical tests, including normality distribution tests, spatial autocorrelation tests, and assessments of independence and homoscedasticity, were conducted to validate the model. The results confirm the model’s ability to satisfy critical statistical assumptions—normality, independence, homoscedasticity, and spatial autocorrelation—and its robustness in achieving a high degree of spatial independence. The findings underscore the need for targeted safety measures and intersection design strategies to mitigate collision risks. By offering enhanced accuracy, temporal flexibility, and spatial insights, the proposed modeling approach provides a robust framework for developing evidence-based safety interventions and optimizing intersection designs to reduce pedestrian injury severity.

由于城市道路网络中交叉口的复杂性和危险性，与交叉口相关的车辆-行人碰撞对交通安全提出了重大挑战。本研究引入了一个具有联合多元正态结构的空间聚合有序logit模型，该模型通过有效捕获车辆运动类型（左转弯、直转弯和右转弯）之间的相关性，并考虑路口和县级的剩余聚合，具有明显优于传统模型的优势。利用2019年至2023年佛罗里达州4280起行人与车辆碰撞的数据集，结合行人、驾驶员、车辆、十字路口、环境、碰撞和时间特征，所提出的模型在捕捉车辆机动之间的相互依赖性方面表现出卓越的性能。确定了四个暂时一致的重要变量，包括18岁以下的行人，城市地区，主要道路限速低于30英里/小时以及夜间照明道路。相比之下，其他几个变量仅在特定年份表现出显著性，反映了它们对行人伤害严重程度的影响在时间上的显著变化。通过正态分布检验、空间自相关检验、独立性和均方差评估等一系列统计检验对模型进行验证。结果证实了该模型能够满足关键的统计假设——正态性、独立性、均方差和空间自相关——以及它在实现高度空间独立性方面的鲁棒性。研究结果强调了有针对性的安全措施和交叉口设计策略的必要性，以减轻碰撞风险。通过提供更高的准确性、时间灵活性和空间洞察力，所提出的建模方法为开发基于证据的安全干预措施和优化十字路口设计提供了一个强大的框架，以降低行人伤害的严重程度。

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引用次数: 0

Analyzing crash injury severities with deep learning and advanced statistical models: An assessment of methodological challenges 用深度学习和高级统计模型分析碰撞损伤严重程度：方法挑战的评估

IF 12.6 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-09-08 DOI: 10.1016/j.amar.2025.100405

MohammadAli Seyfi , Amir Mohammad Karimi Mamaghan , Ali Behnood , Fred Mannering

In this research, statistical and deep learning models are applied to determine factors that affect motorcycle crash-injury severities. Four methodological challenges are considered: 1) imbalanced data (because fatal injuries are an exceedingly small portion of all resulting injury outcomes); 2) unobserved heterogeneity (because many unobserved factors will influence resulting injury severities); 3) quantification of variable effects; and 4) the possibility of temporally shifting relationships among variables. Convolutional neural networks and deep neural networks are the deep learning models considered, and random parameters logit models with heterogeneity in means and variances is the statistical model considered. Extensive experimentation indicated that data imbalance and unobserved heterogeneity could be best handled in deep learning models with a Bayesian deep neural network with a random generator and weighted loss function. With statistical modeling indicating significant shifts in model parameters over time, the data were segmented by year and both statistical and deep learning models were estimated. While techniques are available for deep learning to potentially handle data imbalance and unobserved heterogeneity, the quantification of variable effects and temporal shifts remains a challenge. For example, a comparison of variable effects show that the deep learning estimates of variable effects are generally inconsistent with the plausible values generated by the statistical models in terms of magnitudes and occasionally in terms of direction, indicating a need for improvements in deep-learning variable-effect extraction methods. The findings also show the need for future work to isolate the effect of complex temporal relationships which are currently imbedded in deep learning approaches, because the segmentation of data that has been used in statistical models to isolate temporal effects, and even the use of all data and defining new time-dependent variables, may not be a viable deep learning option due to the potential loss in predictive performance.

在本研究中，应用统计和深度学习模型来确定影响摩托车碰撞伤害严重程度的因素。研究考虑了四个方法学上的挑战：1)数据不平衡（因为致命伤害在所有导致的伤害结果中所占比例极小）；2)未观察到的异质性（因为许多未观察到的因素会影响导致的损伤严重程度）；3)变量效应的量化；(4)变量间关系发生时间转移的可能性。考虑的深度学习模型是卷积神经网络和深度神经网络，考虑的统计模型是均值和方差异质性的随机参数logit模型。大量的实验表明，使用随机生成器和加权损失函数的贝叶斯深度神经网络可以最好地处理数据不平衡和未观察到的异质性。统计建模表明模型参数随时间的显著变化，数据按年分割，并对统计和深度学习模型进行估计。虽然深度学习技术可以潜在地处理数据不平衡和未观察到的异质性，但变量效应和时间变化的量化仍然是一个挑战。例如，对变量效应的比较表明，深度学习对变量效应的估计通常与统计模型产生的合理值在量级上不一致，有时在方向上也不一致，这表明深度学习变量效应提取方法需要改进。研究结果还表明，未来的工作需要隔离目前嵌入深度学习方法中的复杂时间关系的影响，因为统计模型中使用的数据分割来隔离时间效应，甚至使用所有数据和定义新的时间相关变量，由于预测性能的潜在损失，可能不是一个可行的深度学习选择。

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引用次数: 0

A unified framework for modeling traffic crashes from hierarchical spatial resolutions 基于分层空间分辨率的交通事故建模统一框架

IF 12.5 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-07-13 DOI: 10.1016/j.amar.2025.100398

Shahrior Pervaz , Tanmoy Bhowmik , Naveen Eluru

Independent traffic crash modeling approaches do not account for the embedded relationships related to the multi-resolution data structure, leading to mis-specified estimations. The recently developed integrated frameworks demonstrate the capability of addressing this drawback. The current study proposes an integrated framework that accommodates information from multiple spatial units and observation resolutions. Specifically, the study develops an integrated model system that allows for the influence of independent variables from disaggregate crash record, micro-facility (segment and intersection) and macro (traffic analysis zone) level simultaneously within the macro level propensity estimation. The empirical analysis considers disaggregate crash records of 1818 segments and 4184 intersections from 300 traffic analysis zones in the City of Orlando, Florida. These crash records contain crash-specific factors, driver and vehicle factors, roadway, road environmental and weather information of each crash record. For micro-facility and macro levels, an exhaustive set of independent variables including roadway and traffic factors, land-use and built environment attributes, and sociodemographic characteristics are considered. The proposed model system can also accommodate for hierarchical correlations among the data across observation resolutions and parameter variability across the system. The empirical analysis is augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated model system relative to the non-integrated model. A validation exercise also highlights the superiority of the proposed framework. The application of the proposed integrated framework can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific solutions simultaneously.

独立的交通碰撞建模方法没有考虑到与多分辨率数据结构相关的嵌入式关系，导致错误的估计。最近开发的集成框架展示了解决这一缺陷的能力。目前的研究提出了一个集成框架，可以容纳来自多个空间单元和观测分辨率的信息。具体而言，该研究开发了一个综合模型系统，该模型系统允许在宏观倾向估计中同时考虑来自分解碰撞记录、微观设施（路段和交叉口）和宏观（交通分析区）层面的自变量的影响。实证分析考虑了佛罗里达州奥兰多市300个交通分析区1818个路段和4184个交叉路口的分类碰撞记录。这些碰撞记录包含每个碰撞记录的特定碰撞因素、驾驶员和车辆因素、道路、道路环境和天气信息。对于微观设施和宏观层面，考虑了一组详尽的自变量，包括道路和交通因素、土地利用和建筑环境属性以及社会人口特征。所提出的模型系统还可以适应不同观测分辨率的数据之间的层次相关性和整个系统的参数可变性。通过采用几个拟合优度和预测度量来增强实证分析。结果清楚地表明，与非集成模型相比，所提出的集成模型系统提供了更好的性能。验证练习也突出了所建议框架的优越性。应用拟议的综合框架可以使交通专业人员同时采用基于政策、特定地点和特定结果的解决方案。

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引用次数: 0

Assessing the impact of COVID-19 on driver injury severities in fixed-object passenger car crashes: Insights from temporal and partially constrained modeling analysis 评估COVID-19对固定物体乘用车碰撞中驾驶员损伤严重程度的影响：来自时间和部分约束建模分析的见解

IF 12.5 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-06-22 DOI: 10.1016/j.amar.2025.100397

Md. Moshiur Rahman , Salvador Hernandez , Rakan Mohammad Radwan Albatayneh

The COVID-19 pandemic reshaped the global transportation sector, including in the U.S., creating an unprecedented shift in traffic patterns. Despite a reduction in vehicle miles traveled (VMT), crash severity, particularly fatalities, increased significantly. Among all crash types, fixed-object collisions have consistently posed a critical safety concern due to their disproportionately high fatality rates, a trend further exacerbated during the pandemic. This study examines the impact of COVID-19 on driver injury severity in fixed-object passenger car crashes in Oregon. The authors estimated separate unconstrained models of driver injury severity in fixed-object passenger car crashes across three distinct time periods: before pandemic (March 2019–February 2020), during pandemic (March 2020–February 2021), and after pandemic (March 2021–February 2022), as well as a partially constrained model utilizing a random parameters multinomial logit model that incorporates heterogeneity in both means and variances of the random parameters. The analysis utilized 22,522 crash records for the state of Oregon obtained from the Oregon Department of Transportation. Likelihood ratio tests were performed to assess the temporal instability of model parameter estimates throughout the three time periods and to compare the partially constrained and unconstrained models. The findings indicated notable temporal variations in the determinants of injury severity, encompassing driver attributes, crash circumstances, roadway characteristics, and environmental elements. While alcohol consumption, improper driving, and collisions with trees consistently influenced injury severity across all periods, factors such as gender, airbag deployment, speeding, seasonal variations, and road surface conditions exhibited changing effects. Out-of-sample predictions indicate that severe injuries in fixed-object crashes were consistently underestimated, highlighting growing concerns about increasing crash severity, particularly in the post-pandemic period.

新冠肺炎疫情重塑了包括美国在内的全球交通运输行业，造成了交通模式的前所未有的转变。尽管车辆行驶里程（VMT）减少了，但碰撞严重程度，尤其是死亡人数，却显著增加。在所有类型的碰撞中，固定物体碰撞由于其不成比例的高死亡率而一直构成严重的安全问题，这一趋势在大流行期间进一步加剧。本研究考察了COVID-19对俄勒冈州固定物体乘用车碰撞中驾驶员受伤严重程度的影响。作者估计了三个不同时间段内固定物体乘用车碰撞中驾驶员伤害严重程度的独立无约束模型：大流行之前（2019年3月- 2020年2月）、大流行期间（2020年3月- 2021年2月）和大流行之后（2021年3月- 2022年2月），以及利用随机参数多项logit模型的部分约束模型，该模型结合了随机参数均值和方差的异质性。该分析利用了从俄勒冈州交通部获得的22522起俄勒冈州的撞车记录。进行似然比检验以评估三个时间段内模型参数估计的时间不稳定性，并比较部分约束和无约束模型。研究结果表明，伤害严重程度的决定因素存在显著的时间差异，包括驾驶员属性、碰撞环境、道路特征和环境因素。虽然饮酒、不当驾驶和与树木的碰撞在所有时期都会影响伤害的严重程度，但性别、安全气囊部署、超速、季节变化和路面状况等因素的影响也在不断变化。样本外预测表明，固定物体碰撞造成的严重伤害一直被低估，这突显出人们对碰撞严重程度日益增加的担忧，特别是在大流行后时期。

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引用次数: 0

Autonomous vehicle sensor data and the estimation of network-wide spatiotemporal generalized extreme value models of rear-end injury-severity crash frequencies 自动驾驶汽车传感器数据及追尾伤害严重碰撞频率网络时空广义极值模型估计

IF 12.5 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-05-29 DOI: 10.1016/j.amar.2025.100390

Sunny Singh , Yasir Ali , Fred Mannering , Md Mazharul Haque

Existing traffic conflict-based extreme value modeling applications are primarily restricted to a few concentrated locations due to the scarcity of network-wide vehicular trajectory data and the constraints associated with traditional network-wide modeling techniques. As such, this study develops a network-wide bivariate spatiotemporal non-stationarity generalized extreme value model to estimate rear-end crash frequency by injury severity level using Argo AI autonomous vehicle sensor data. Fusing this dataset with road network data from the Florida Department of Transportation, this paper studies a road network of 57 intersections and mid-blocks in Miami, Florida. Modified time-to-collision and the expected post-collision velocity difference (Delta-V) are used to estimate severe and non-severe rear-end crashes. Road geometry, road classification, and traffic state variables are used as covariates to address spatiotemporal heterogeneity in the generalized extreme value model estimation. Results show the significant impact of spatiotemporal variables such as lane width, median width, dedicated street parking, dedicated bike lane, vehicle class, and road class on rear-end crash frequency by injury severity levels. It is found that the bivariate spatiotemporal generalized extreme value model outperforms the bivariate random intercept generalized extreme value model and the univariate generalized extreme value model with conditional severity probability when benchmarked against observed annual crash frequency using root mean square error and the coefficient of determination (R-squared). Additionally, the bivariate spatiotemporal generalized extreme value model provides the closest estimate of observed severe crashes by roadway segments in the study area. The findings of this study underscore the importance of proactive network-wide safety management using spatiotemporal heterogeneity and autonomous vehicle sensor data to estimate crash frequency by severity for real-time decision-making.

现有的基于交通冲突的极值建模应用主要局限于几个集中的地点，这主要是由于全网络车辆轨迹数据的稀缺性和传统全网络建模技术的约束。因此，本研究利用Argo AI自动驾驶汽车传感器数据，开发了一个全网络双变量时空非平稳性广义极值模型，根据损伤严重程度估计追尾碰撞频率。本文将该数据集与佛罗里达州交通部的道路网络数据融合，研究了佛罗里达州迈阿密市由57个十字路口和中间街区组成的道路网络。修正碰撞时间和预期碰撞后速度差（Delta-V）用于估计严重和非严重追尾碰撞。在广义极值模型估计中，使用道路几何形状、道路分类和交通状态变量作为协变量来解决时空异质性问题。结果表明，车道宽度、中位宽度、专用街道停车、专用自行车道、车辆类别和道路类别等时空变量对不同伤害严重程度的追尾碰撞频率有显著影响。研究发现，当使用均方根误差和决定系数（r平方）对观测到的年碰撞频率进行基准测试时，二元时空广义极值模型优于二元随机截距广义极值模型和单变量条件严重概率广义极值模型。此外，二元时空广义极值模型提供了研究区域内按路段观察到的最接近的严重碰撞估计。这项研究的结果强调了主动的全网络安全管理的重要性，利用时空异质性和自动驾驶汽车传感器数据，根据严重程度估计碰撞频率，以进行实时决策。

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引用次数: 0

A note from the new Editor-in-Chief of Analytic Methods in Accident Research 《事故研究中的分析方法》新主编的注释

IF 12.5 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-05-20 DOI: 10.1016/j.amar.2025.100389

Shimul (Md Mazharul) Haque

引用次数: 0

Exploring the dynamic determinants of general aviation accidents across flight phases and time: A random parameter bivariate probit approach with heterogeneity in means 探索跨飞行阶段和时间的通用航空事故的动态决定因素：一种随机参数双变量概率方法

IF 12.5 1区工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Analytic Methods in Accident Research

Pub Date : 2025-05-19 DOI: 10.1016/j.amar.2025.100386

Qingli Liu , Penglin Song , Fan Li

General aviation experiences significant variation in accident characteristics across flight phases. This study seeks to investigate the phase transferability and temporal stability of determinants influencing general aviation accidents, using the U.S. data (2008–2019) from the National Transportation Safety Board. To achieve this, a random parameter bivariate approach with heterogeneity in means was employed, focusing on two binary outcomes: injury severity (fatal/severe vs. minor/none) and aircraft damage (destroyed vs. non-destroyed). Four flight phases were analyzed: departure, enroute, maneuvering, and arrival. The data were divided into three time periods, 2008–2011, 2012–2015, and 2016–2019, to assess the determinants’ temporal stability. Likelihood ratio tests revealed that pilot injury and aircraft damage risks exhibit phase non-transferability and temporal instability. Out-of-sample predictions indicated a steady rise in fatal or severe injury risk, while aircraft damage risk initially increased before declining over time. A significant positive correlation between pilot injury and aircraft damage was observed through model estimation. Key factors, including pilot, aircraft, flight, and environmental conditions, significantly influenced both outcomes. Moreover, factors such as decision-making errors, adverse physiological conditions, fixed landing gear, and visual meteorological conditions showed both phase transferability and temporal stability. However, most factors were phase- and period-specific. Based on these findings, targeted measures, such as pilot escape and survival training, as well as phase-specific, scenario-based training, are proposed to mitigate general aviation risks.

通用航空在不同飞行阶段的事故特征有显著差异。本研究旨在利用美国国家运输安全委员会（National Transportation Safety Board） 2008-2019年的数据，调查影响通用航空事故的决定因素的相位可转移性和时间稳定性。为了实现这一目标，采用了随机参数双变量方法，方法具有异质性，重点关注两个二元结果：损伤严重程度（致命/严重vs.轻微/无）和飞机损伤（损坏vs.未损坏）。分析了四个飞行阶段：起飞、途中、机动和到达。数据分为2008-2011年、2012-2015年和2016-2019年三个时间段，以评估影响因素的时间稳定性。似然比测试显示，飞行员受伤和飞机损坏风险表现出阶段不可转移性和时间不稳定性。样本外的预测表明，致命或严重伤害的风险稳步上升，而飞机损坏的风险最初是上升的，然后随着时间的推移而下降。通过模型估计，发现飞行员损伤与飞机损伤之间存在显著的正相关关系。包括飞行员、飞机、飞行和环境条件在内的关键因素对两种结果都有显著影响。此外，决策失误、不利生理条件、固定起落架和目视气象条件等因素均表现出相转移性和时间稳定性。然而，大多数因素是特定阶段和特定时期的。基于这些发现，提出了有针对性的措施，如飞行员逃生和生存培训，以及分阶段、基于场景的培训，以降低通用航空风险。

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