Analytic Methods in Accident Research最新文献

Modeling economic loss associated with fishing vessel accidents: A Bayesian random-parameter generalized beta of the second kind model with heterogeneity in means

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

Analytic Methods in Accident Research

Pub Date : 2025-04-12 DOI: 10.1016/j.amar.2025.100384

Yun Ye , Pengjun Zheng , Qianfang Wang , S.C. Wong , Pengpeng Xu

The distribution of economic loss associated with vessel accidents typically exhibits non-negative, continuous, positively skewed, and heavy-tailed characteristics. Another challenge in analyzing fishing vessel accidents is the absence of relevant factors. Ignoring such heterogeneity caused by unobserved factors potentially leads to inaccurate inferences. In the present study, a novel Bayesian random-parameter generalized beta of the second kind (GB2) model with possible heterogeneity in means and variances was developed. The flexible GB2 distribution was harnessed to model the skewed and heavy-tailed response variable, while the random parameters were specified to capture the unobserved heterogeneity. The proposed method was validated using an insurance claim dataset with 3448 fishing vessel accidents within Ningbo waters during 2018–2022. The proposed model successfully identified significant influential factors, including fixed parameters, random parameters, and covariates influencing the means of the random parameters. Specifically, offshore and inevitable accidents, fishing transport vessels, double-trawl vessels with mechanical failures, wide-hulled vessels, and favorable sea conditions were associated with greater economic loss. Special attention should also be paid to nighttime accidents involving steel-hulled fishing transport vessels, as this accident type emerged to result in greater loss during the pandemic lockdown period. Our approach can accommodate the abnormality, skewness, and heavy-tail of vessel accident loss data, adjust for the bias introduced by unobserved factors, and uncover the interactive relationship among covariates. Targeted countermeasures were proposed to mitigate economic loss resulting from fishing vessel accidents.

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引用次数: 0

Assessment of vehicle age as a contributor to temporal shifts in single-vehicle driver injury severities

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

Analytic Methods in Accident Research

Pub Date : 2025-03-28 DOI: 10.1016/j.amar.2025.100383

Emmanuel Kofi Adanu , Richard Dzinyela , Dustin Wood , Steven Jones

Vehicle age plays a crucial role in crash occurrence and occupant injury severity, with older vehicles historically associated with more severe injury outcomes compared to newer models. This study investigates the temporal instability of specific injury-contributing factors for single-vehicle, single-occupant crashes involving vehicles equal or less than 3 years old at the time of the crash, using data from Alabama’s Critical Analysis Reporting Environment (CARE) system. The analysis spans four time points: 2010, 2014, 2018, and 2022. Preliminary data analysis indicates a reduction in new vehicle severe injury crashes from 7.25% in 2010 to 4.05% in 2022. Random parameters multinomial logit models with heterogeneity in means were developed to identify crash factors significantly related to injury outcomes. Key findings highlight the consistent trend of higher severity crashes in which drivers fail to use a seatbelt and airbags are deployed. However, there was a notable decrease in severe injuries for 3-year-old vehicles involved in crashes in 2022 compared to previous years. Model results revealed that this benefit is particularly evident in the reduced likelihood of severe injury among drivers older than 65 years where airbags were deployed over the years, except for 2010. The study indicates the importance of advancements in vehicle technology in enhancing occupant safety. It also emphasizes the need for ongoing research into driver behavior, road conditions, and the evolution of safety standards to fully leverage these technological improvements. The findings suggest that continuous updates to driver education and awareness programs are essential to reflect new technologies and changing driving environments, ensuring drivers can effectively utilize advanced safety features.

车龄对碰撞事故的发生和乘员受伤的严重程度起着至关重要的作用，与较新的车型相比，车龄较长的车辆历来会造成更严重的伤害后果。本研究利用阿拉巴马州关键分析报告环境（CARE）系统中的数据，对车祸发生时车龄等于或小于 3 年的单车单人车祸中特定伤害诱因的时间不稳定性进行了调查。分析跨越四个时间点：2010 年、2014 年、2018 年和 2022 年。初步数据分析显示，新车重伤车祸率从 2010 年的 7.25% 降至 2022 年的 4.05%。随机参数多叉 Logit 模型具有均值异质性，可识别与伤害结果显著相关的碰撞因素。主要研究结果表明，在驾驶员未使用安全带和安全气囊未展开的碰撞事故中，严重程度较高的趋势始终如一。不过，与前几年相比，2022 年发生碰撞事故的 3 年车龄车辆的严重受伤人数明显减少。模型结果显示，除 2010 年外，65 岁以上的驾驶员在安全气囊展开的情况下，严重受伤的可能性逐年降低，这一优势尤为明显。这项研究表明，汽车技术的进步对提高乘员安全非常重要。研究还强调，有必要对驾驶员行为、道路状况和安全标准的演变进行持续研究，以充分发挥这些技术改进的作用。研究结果表明，持续更新驾驶员教育和认知计划对于反映新技术和不断变化的驾驶环境至关重要，可确保驾驶员有效利用先进的安全功能。

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引用次数: 0

A physics-informed risk force theory for estimating pedestrian crash risk by severity using artificial intelligence-based video analytics

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

Analytic Methods in Accident Research

Pub Date : 2025-03-03 DOI: 10.1016/j.amar.2025.100382

Saransh Sahu , Yasir Ali , Sebastien Glaser , Md Mazharul Haque

Pedestrians are a vulnerable road user group, and assessing their crash risk at critical locations, such as signalized intersections, is crucial for developing targeted countermeasures. While conflict-based safety assessments using traffic conflict measures effectively estimate crash risk, they often overlook the heterogeneity of different motorized and non-motorized road users. Conversely, field-based theories account for road user heterogeneity, yet their application in crash risk assessment, specifically evaluating pedestrian crash risk, and particularly by severity level using real-world data, remains underexplored. This study introduces a novel application of physics-informed risk force theory for assessing pedestrian crash risk by injury severity, utilizing facility-based video data at signalized intersections. The study derives risk forces that encompass pedestrian and vehicle heterogeneity as a nearness-to-collision component and vehicle impact speed as a severity component. Stationary and non-stationary extreme value models, incorporating exogenous traffic parameters at the signal cycle level, were applied to 72 h of video data collected from three signalized intersections in Queensland, Australia. The non-stationary univariate extreme value model with risk force as a measure of nearness-to-collision reliably estimated total crash frequency compared to historical crash records. In addition, the bivariate extreme value model with risk force and impact speed reasonably predicted pedestrian crashes by severity levels. The results also indicate that an increased volume of interacting pedestrians and left-turning vehicles elevates the likelihood of total and severe crashes. The proposed pedestrian crash risk assessment framework offers a unified and efficient proactive approach that can enhance automated safety analysis of traffic facilities, thereby assisting road authorities in real-time safety management.

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引用次数: 0

Is there an emotional dimension to road safety? A spatial analysis for traffic crashes considering streetscape perception and built environment

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

Analytic Methods in Accident Research

Pub Date : 2025-02-13 DOI: 10.1016/j.amar.2025.100374

Yiping Liu , Tiantian Chen , Hyungchul Chung , Kitae Jang , Pengpeng Xu

Modern streetview image data provide two types of valuable information: the objective built environment and humans’ subjective perception of the streetscape. In the road safety domain, the built environment has been identified as playing a significant role while indicators of human perception are commonly used to evaluate street quality in urban planning. However, studies examining the association between humans’ perceptions of the streetscape and traffic crashes remain limited. This study aims to address this question and to inform safety considerations at the micro level in the planning process for the targeted streets. To answer the question, this study integrates databases on motor vehicle crashes, points of interest, street view images, and road networks for the urban area of Daejeon city in South Korea in 2019. A deep learning model was employed to calculate six perceptual indicators–wealthy, lively, boring, depressing, safety, and beautiful–based on a crowdsourcing dataset. Furthermore, a Bayesian multivariate Poisson-lognormal model with spatial-varying coefficients was introduced to simultaneously account for spatial random effect and the shared unobserved effect across crash severity levels. Results indicate that four of the six perceptual variables significantly affect the number of slight injury crashes, showing spatially heterogeneous effects. Based on the values of human perception indicators and their impacts on traffic crashes, we identified road segments which need special attention to objective safety performance when considering street renovation. Additionally, built environment factors such as the proportion of vegetation, the presence of sidewalks and fences, and points of interest (including educational, health service, and commercial establishments) were found to reduce the number of motor vehicle crashes. Overall, the findings are expected to facilitate the safety-enhanced street planning project, and contribute to the development of human-centric cities.

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引用次数: 0

A note on data segmentation, sample size, and model specification for crash injury severity modeling

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

Analytic Methods in Accident Research

Pub Date : 2025-02-12 DOI: 10.1016/j.amar.2025.100373

Qinzhong Hou , Jinglun Zhuang , Chenrui Zhai , Xiaoyan Huo , Fred Mannering

In recent years, the statistical assessment of crash injury severity data has increasingly begun to segment the available crash data into observational groups to explore the possibility that such groups may share the same estimated parameters. This method is commonly used to account for parameters that may shift over time, where the data is often segmented into groups based on observational year. Unfortunately, such data segmentation can lead to small samples within each group, which has caused some concern about decreasing sample size. However, concerns about diminishing sample size are often misplaced and not well understood. In this paper, the impact of data segmentation is assessed by estimating models that address the possibility of temporally shifting parameters. Starting with a large 80,000 observation sample, the process involves randomly segmenting the data into groups with sample sizes varying from 1000 to 40,000, and then assessing the difference between the estimated data-segmented models and the overall model (using all available data) using likelihood ratio tests. The results indicate that: 1) model specification is extremely important, regardless of sample size, 2) statistical tests should be used to determine the suitability of simple versus complex models, not sample size, and 3) the variance/covariance structure of the data being considered determines model specification and sample size effects, which means sample-size requirements are data-specific, and that general statements regarding minimum sample size requirements for specific model types cannot be made.

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引用次数: 0

How do drivers manage speed at tunnel entrances? Insights from uncorrelated grouped random parameters duration models for model invalidation and performance recovery times

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

Analytic Methods in Accident Research

Pub Date : 2025-01-23 DOI: 10.1016/j.amar.2025.100371

Yunjie Ju , Shi Ye , Tiantian Chen , Guanyang Xing , Feng Chen

Human drivers must quickly adjust to perturbations at tunnel entrances (i.e., the rapid switching of cross-sections, abrupt longitudinal changes in the driving environment, and changes in visual illumination, denoted “tunnel transition perturbations”) to regain control of their vehicles, especially when managing speed to prevent motor overshoot. Previous research has assessed drivers’ visual adaptation rather than variations in vehicle control under tunnel transition perturbations. In this study, a sample entropy method was used to measure the safety–critical duration of speed control events at tunnel entrances and thereby reveal the participants’ speed adaptation and recovery performance under tunnel transition perturbations. Two key metrics—model invalidation time and performance recovery time—were introduced, and an uncorrelated grouped random parameters hazard-based duration model was developed. Road grade, road curvature, income, and time having held a license were positively associated with model invalidation time, while a history of accidents in the past 12 months was negatively associated with model invalidation time. In addition, road grade, road curvature, and income had heterogeneous effects on model invalidation time. Moreover, a history of accidents in the past 12 months moderated the relationship between road grade and model invalidation time. Furthermore, road curvature, average annual mileage, and sleep deprivation significantly influenced performance recovery time, while road grade and non-fatigue condition had heterogeneous effects on performance recovery time. Overall, this study demonstrated that the participants’ personal characteristics and experiences significantly shaped the development of their internal models, and that their current status and perception had a substantial influence on their performance recovery under tunnel transition perturbations. These insights enhance understanding of the mechanisms of drivers’ motor control under tunnel transition perturbations and will therefore enable improvement of road traffic design and safety management at tunnel entrances.

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引用次数: 0

Understanding the effects of underreporting on injury severity estimation of single-vehicle motorcycle crashes: A hybrid approach incorporating majority class oversampling and random parameters with heterogeneity-in-means

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

Analytic Methods in Accident Research

Pub Date : 2025-01-23 DOI: 10.1016/j.amar.2025.100372

Nawaf Alnawmasi , Apostolos Ziakopoulos , Athanasios Theofilatos , Yasir Ali

The underreporting of crash data is a well-documented issue in road safety literature, but few studies have focused on addressing this problem in the context of analyzing crash injury severities. This paper aims to provide an empirical assessment of the impact of underreporting issue using a hybrid approach in estimating injury severity for single-vehicle motorcycle crashes. Unlike traditional machine learning methods that oversample the minority class (the category with the fewer observations such as fatal and severe injuries), the present study oversamples the majority class (i.e. minor injuries), which are often underreported in crash datasets, thus providing a fresh perspective on this issue. Afterwards, random parameter models with heterogeneity in means and variances were applied. The results of this study, as supported by the likelihood ratio tests, indicate that the key variables influencing motorcyclists’ injury severities remain consistent across both original and oversampled data models. Specifically, crashes occurring during slowing down or stopping are associated with lower injury severity, whereas negotiating a right turn increases the probability of severe injuries. Interestingly, crashes that occur on dry pavements are associated with higher injury severity when compared to wet pavements, likely due to rider behavior adjustments in adverse weather conditions to compensate for the risk. Overall, the oversampled models have a significantly lower marginal effects values compared to the original model’s marginal effects. This study provides a foundation for further examination of underreporting issue in crash injury severity modelling and also highlights the need to capture the dynamics of crash injuries suggesting that alternative approaches could improve the understanding and hence road safety management. Future studies are encouraged to replicate this methodology to validate the findings as well as utilize other advanced machine learning algorithms, like tree-based models to assess underreporting mitigation.

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引用次数: 0

Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data

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

Analytic Methods in Accident Research

Pub Date : 2025-01-15 DOI: 10.1016/j.amar.2025.100370

Yuzhi Chen , Yuanchang Xie , Chen Wang , Liguo Yang , Nan Zheng , Lan Wu

This paper proposes a novel functional data analysis approach to investigate the time-dependent effect of advanced driver assistance systems (ADAS), specifically forward collision warnings, on driver speed reduction behavior. Existing aggregate measures compress temporal information within driver behavior profiles and fail to explicitly reveal the temporal dependency of such effect. With the proposed approach, the functional representation method is adopted to capture the underlying driver behavior in response to warning messages and address issues of irregularly spaced observations and measurement errors; the results of the functional principal component analysis with the bootstrap-enhanced Kaiser-Guttman method reveal important patterns in driver response behaviors; and a nonparametric functional varying coefficient regression model, considering vehicle initial motions and drivers’ acceleration styles, is established. This regression model utilizes coefficient functions to estimate the time-dependent effect of ADAS. The proposed approach is evaluated based on the New York City connected vehicle dataset using forward collision warning event records. The results suggest that the treatment effect of the warning messages is time-dependent, initially increasing before progressively decreasing over time. Driver responses can be decomposed into several phases at the 95 % confidence level, including reaction time (1.3 s), brake adjustment time (1.3 s), progressive braking duration (2.7 s), and effective treatment duration (4.0 s). The time-dependent bootstrap confidence interval confirms driver heterogeneity in these distinct phases. The proposed functional data analysis approach can serve as a paradigm for quantifying the treatment effect of other ADAS applications. The findings can support the improvements of ADAS design and the development and calibration of driver behavior models accounting for ADAS.

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引用次数: 0

A unified probabilistic approach to traffic conflict detection

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

Analytic Methods in Accident Research

Pub Date : 2024-12-20 DOI: 10.1016/j.amar.2024.100369

Yiru Jiao , Simeon C. Calvert , Sander van Cranenburgh , Hans van Lint

Traffic conflict detection is essential for proactive road safety by identifying potential collisions before they occur. Existing methods rely on surrogate safety measures tailored to specific interactions (e.g., car-following, side-swiping, or path-crossing) and require varying thresholds in different traffic conditions. This variation leads to inconsistencies and limited adaptability of conflict detection in evolving traffic environments, particularly as the integration of autonomous driving systems adds complexity. Consequently, there is an increasing need for consistent detection of traffic conflicts across interaction contexts. To address this need, we propose a unified probabilistic approach in this study. The proposed approach establishes a unified framework of traffic conflict detection, where traffic conflicts are formulated as context-dependent extreme events of road user interactions. The detection of conflicts is then decomposed into a series of statistical learning tasks: representing interaction contexts, inferring proximity distributions, and assessing extreme collision risk. The unified formulation accommodates diverse hypotheses of traffic conflicts and the learning tasks enable data-driven analysis of factors such as motion states of road users, environment conditions, and participant characteristics. Jointly, this approach supports consistent and comprehensive evaluation of the collision risk emerging in road user interactions. We demonstrate the proposed approach by experiments using real-world trajectory data. A unified metric for indicating conflicts is first trained with lane-change interactions on German highways, and then compared with existing metrics using near-crash events from the U.S. 100-Car Naturalistic Driving Study. Our results show that the unified metric provides effective collision warnings, generalises across distinct datasets and traffic environments, covers a broad range of conflict types, and captures a long-tailed distribution of conflict intensity. In summary, this study provides an explainable and generalisable approach that enables traffic conflict detection across varying interaction contexts. The findings highlight its potential to enhance the safety assessment of traffic infrastructures and policies, improve collision warning systems for autonomous driving, and deepen the understanding of road user behaviour in safety–critical interactions.

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引用次数: 0

Econometric approaches to examine the onset and duration of temporal variations in pedestrian and bicyclist injury severity analysis 用计量经济学方法研究行人和骑自行车者受伤严重程度分析中时间变化的开始和持续时间

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

Analytic Methods in Accident Research

Pub Date : 2024-10-12 DOI: 10.1016/j.amar.2024.100362

Natakorn Phuksuksakul , Naveen Eluru , Md. Mazharul Haque , Shamsunnahar Yasmin

There is considerable evidence in existing safety literature that the exogenous variable effects are likely to be time-varying in the injury severity analysis. The majority of these earlier studies tested time-varying effects of exogenous variables by crash year. However, there might be variability in the variable effects within a year, while the same effect might carry over in some or all parts of the preceding years. Towards that end, in this study, we propose a flexible framework to identify when the time-varying effect is likely to occur (the onset of temporal variation) and how long such time-varying effect lasts (duration of temporal variation) in the model estimates. In the study design, we assume that the onset of temporal variation can be any quarter of a year under consideration, while the time-varying effect can continue over different quarters after the onset of temporal variation in a variable effect. The injury severity model is estimated by using Correlated Random Parameter Generalized Ordered Logit formulation with piecewise linear functions. The empirical analysis is demonstrated by employing active traveler (pedestrian and bicyclist) crash data from Queensland, Australia for the years 2015 through 2020. The estimation results are further augmented by computing elasticity effects. The results indicate that the time-varying effects are likely to be different across years for several variables, while for other variables, the onset of time-varying effects could be different than the start of a year. Such flexibility in model specification is likely to have significant implications for devising and implementing effective countermeasures since it allows us to understand how road traffic injuries are evolving over time and when a new road safety issue might be arising.

现有安全文献中有大量证据表明，在伤害严重程度分析中，外生变量的影响很可能是时变的。这些早期研究大多按碰撞年份测试了外生变量的时变效应。然而，变量效应在一年内可能会有变化，而相同的效应可能会在前几年的部分或全部时间内延续。为此，在本研究中，我们提出了一个灵活的框架，以确定模型估计中的时变效应何时可能出现（时变的起始时间）以及这种时变效应会持续多久（时变的持续时间）。在研究设计中，我们假定时间变化的起始点可以是一年中的任何一个季度，而时间变化效应可以在可变效应的时间变化起始点之后的不同季度中持续。伤害严重程度模型是利用相关随机参数广义有序 Logit 公式和片断线性函数进行估计的。实证分析采用了澳大利亚昆士兰州 2015 年至 2020 年的主动旅行者（行人和骑自行车者）碰撞数据。通过计算弹性效应，进一步扩充了估算结果。结果表明，对于几个变量来说，不同年份的时变效应可能不同，而对于其他变量来说，时变效应的开始时间可能不同于一年的开始时间。模型规格的这种灵活性可能会对制定和实施有效的对策产生重大影响，因为它使我们能够了解道路交通伤害是如何随时间演变的，以及何时可能出现新的道路安全问题。

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引用次数: 0