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A large language model framework to uncover underreporting in traffic crashes 发现交通事故漏报的大型语言模型框架
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-11-13 DOI: 10.1016/j.jsr.2024.11.009
Cristian Arteaga, JeeWoong Park
Introduction: Crash reports support the development of traffic safety countermeasures, but these reports often suffer from underreporting of crucial crash factors due to miscoded entries during data collection. To rectify these issues, the current practice relies on manual information rectification, which is time consuming and error prone, especially with large data volumes. To address these hurdles, we develop a framework to analyze traffic crash narratives and uncover underreported crash factors by capitalizing on the capabilities of Large Language Models (LLM). Method: The framework integrates procedures for prompt definition, selection of LLM generation parameters, output parsing, and underreporting determination. For evaluation, we present a case study on identification of underreported alcohol involvement in traffic crashes. We investigate the framework’s identification accuracy in relation to different underlying LLMs (i.e., ChatGPT, Flan-UL2, and Llama-2), prompt framings (i.e., explicit vs. implicit matching), and generation parameters (i.e., sampling temperature and nucleus probability). Our validation dataset consists of 500 crash reports from the State of Massachusetts. Results: Analysis results demonstrate that the developed framework achieves a recall and precision of up to 1.0 and 0.93, respectively, indicating a successful retrieval of underreported instances. These findings indicate that the developed framework addresses a critical gap in the existing traffic safety analysis workflow by enabling safety analysts to uncover underreporting in crash data efficiently and accurately, without the need for extensive expertise in natural language processing. Practical Applications: Thus, the developed approach offers unprecedented opportunities to maximize the quality and comprehensiveness of traffic crash records, paving the way for more effective countermeasure development.
导言:碰撞事故报告为交通安全对策的制定提供了支持,但由于数据收集过程中的误码输入,这些报告经常会出现关键碰撞因素报告不足的问题。为了纠正这些问题,目前的做法是依靠人工纠正信息,这既耗时又容易出错,尤其是在数据量较大的情况下。为了解决这些问题,我们开发了一个框架,利用大型语言模型 (LLM) 的功能来分析交通事故叙述,并揭示未充分报告的事故因素。方法:该框架整合了提示定义、LLM 生成参数选择、输出解析和漏报确定等程序。为了进行评估,我们提出了一个案例研究,用于识别交通事故中少报的酒精参与情况。我们根据不同的底层 LLM(即 ChatGPT、Flan-UL2 和 Llama-2)、提示框架(即显式匹配与隐式匹配)和生成参数(即采样温度和核概率)来研究该框架的识别准确性。我们的验证数据集包括来自马萨诸塞州的 500 份碰撞报告。分析结果分析结果表明,所开发框架的召回率和精确率分别高达 1.0 和 0.93,表明成功检索了未充分报告的实例。这些结果表明,所开发的框架解决了现有交通安全分析工作流程中的一个关键缺口,使安全分析人员能够高效、准确地发现碰撞数据中的漏报情况,而无需具备丰富的自然语言处理专业知识。实际应用:因此,所开发的方法为最大限度地提高交通事故记录的质量和全面性提供了前所未有的机会,为更有效地制定对策铺平了道路。
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引用次数: 0
Drivers’ long-term crash risks associated with being ticketed for speeding 驾驶员因超速被开罚单而面临的长期撞车风险
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-11-07 DOI: 10.1016/j.jsr.2024.10.009
Darren Walton , Ross Hendy
Introduction: This research analyzes the relationship between police-issued tickets for speeding and the crash risk of those drivers, in New Zealand, between 2015–2019. Method: The main data are constructed through data-matching license details of crash outcomes with all officer-issued tickets for speeding between 2015–2016 (N = 534,935). The sub-group of drivers that accumulate tickets is compared to a coarsened exact matched set of drivers of the same age. Results: There is a strong relationship between the number of tickets a person has in a two-year period (2015–16) and the likelihood of a crash outcome (2017–2019). However, the accumulation of tickets is not the best predictor of crash likelihood. A combination of the excess in speed and the accumulation of tickets increases the relative odds of a subsequent crash. These results are discussed considering the threshold at which New Zealand criminalizes alcohol-relating offending (notionally 4.2 times the base rate crash risk). The same rate of elevated crash risk exists when a driver has one ticket for being 10 km/h over the speed limit and has another speeding ticket within two years.
导言:本研究分析了 2015-2019 年间新西兰警方开出的超速罚单与这些司机的撞车风险之间的关系。研究方法:主要数据是通过将碰撞结果的驾驶执照详细信息与 2015-2016 年间所有警官开出的超速罚单(N = 534,935 )进行数据匹配而构建的。累积罚单的驾驶员子群与经过粗略精确匹配的同龄驾驶员子群进行比较。结果显示一个人在两年内(2015-2016 年)的罚单数量与发生碰撞结果的可能性(2017-2019 年)之间存在密切关系。然而,罚单的累积并不是预测车祸可能性的最佳指标。超速和罚单累积的结合会增加随后发生碰撞的相对几率。在讨论这些结果时,我们考虑了新西兰将与酒精有关的违法行为定为刑事犯罪的临界值(名义上是基准碰撞风险的 4.2 倍)。当一名驾驶员因超速 10 公里/小时而被开罚单,并在两年内再次被开超速罚单时,发生车祸的风险率也会升高。
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引用次数: 0
Factors influencing behavioral intentions to use conditionally automated vehicles 影响使用有条件自动驾驶汽车行为意向的因素
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-28 DOI: 10.1016/j.jsr.2024.10.006
Sjaan Koppel , David B. Logan , Xin Zou , Fareed Kaviani , Hayley McDonald , Joseph F. Hair Jr , Renée M. St. Louis , Lisa J. Molnar , Judith L. Charlton
Background: This study explored factors influencing the acceptance of conditionally automated vehicles among Australian drivers by extending the Technology Acceptance Model with the Technology Readiness Index. Method: Data from an online survey of 844 participants were analyzed using partial least squares structural equation modeling (PLS-SEM). Results: Perceived usefulness had the strongest direct effect on behavioral intention (0.469, p < 0.001), followed by attitude (0.318, p < 0.001). Innovativeness positively influenced behavioral intention (0.183, p < 0.001), while insecurity had a negative impact (−0.071, p < 0.01). Optimism and discomfort were not significant. Perceived usefulness also had significant indirect effects through attitude (0.156, p < 0.001) and trust (0.072, p < 0.001). Perceived ease of use indirectly influenced behavioral intention through perceived usefulness (0.306, p < 0.001), attitude (0.102, p < 0.001), trust (0.047, p < 0.001), and their combinations. Trust indirectly affected behavioral intention via attitude (0.130, p < 0.001). Perceived security and privacy risks had indirect negative effects through trust and attitude (−0.035, p < 0.001; −0.005, p < 0.05). Conclusion: These results suggest that fostering acceptance among less tech-savvy individuals may help promote positive attitudes, increase conditionally automated vehicle adoption, and potentially enhance road safety. Practical implications: These findings suggest a need for targeted programs to enhance perceived usefulness and trust while addressing security and privacy concerns, ultimately contributing to safer road systems through the adoption of conditionally automated vehicles.
研究背景本研究通过扩展技术接受模型与技术准备指数,探讨影响澳大利亚驾驶员接受有条件自动驾驶汽车的因素。研究方法采用偏最小二乘法结构方程模型(PLS-SEM)对 844 名参与者的在线调查数据进行分析。结果显示感知有用性对行为意向的直接影响最大(0.469,p <0.001),其次是态度(0.318,p <0.001)。创新性对行为意向有积极影响(0.183,p <0.001),而不安全感则有消极影响(-0.071,p <0.01)。乐观和不适感没有显著影响。感知有用性还通过态度(0.156,p < 0.001)和信任(0.072,p < 0.001)产生了显著的间接影响。感知易用性通过感知有用性(0.306,p < 0.001)、态度(0.102,p < 0.001)、信任(0.047,p < 0.001)及其组合间接影响行为意向。信任通过态度间接影响行为意向(0.130,p < 0.001)。感知到的安全和隐私风险通过信任和态度间接产生负面影响(-0.035,p < 0.001;-0.005,p < 0.05)。结论这些结果表明,促进对技术不太了解的人接受自动驾驶汽车可能有助于促进积极的态度,提高有条件自动驾驶汽车的采用率,并有可能加强道路安全。实际意义:这些研究结果表明,在解决安全和隐私问题的同时,有必要制定有针对性的计划来提高人们的实用性和信任度,最终通过有条件自动驾驶汽车的采用来提高道路系统的安全性。
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引用次数: 0
Recent Latino immigrants to Miami-Dade County, Florida: Impaired driving behaviors during the initial years after immigration and the pandemic lockdown 佛罗里达州迈阿密-戴德县的新近拉丁裔移民:移民和大流行病封锁后最初几年的受损驾驶行为
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-24 DOI: 10.1016/j.jsr.2024.09.009
Eduardo Romano , Mariana Sanchez
Introduction: Typically, recent Latino immigrants (RLIs) experience a decline in driving while impaired (DWI) rates soon after immigration, largely due to limited access to vehicles. Such a transitional period offers a window of opportunity for intervention for RLIs at risk of engaging in DWI and riding with an impaired driver (RWID). This manuscript examines the rates of DWI, RWID, and driving while impaired by drugs (DWID) among RLIs upon arrival to Miami/Dade County (MDC), Florida. Methods: Collected between 2018 and 2021, data originates from a longitudinal study examining self-reported drinking and driving trajectories among 540 RLIs to MDC. At baseline retrospective pre-immigration data were obtained simultaneously with first-year post-immigration data. Two follow-up surveys conducted one year apart (N=531 and N=522), collect data on RLIs initial 3 years in the United States. Results: Pre- to post-immigration trajectories for mean number of drinks per month (d/m) revealed a “U-shaped” curve: 18.3 d/m, 13.9 d/m, 10.4 d/m, 12.9 d/m, and 16.4 d/m, from pre-immigration (T0), first year (T1), second year before COVID (T2-BC) and during the pandemic lockdown (T2-DC), and third year in the United States (T3). The use of illicit drugs showed a constant decline, from 14.6% at T0 to 2.1% at T3. The prevalence of DWI at T1 was significantly lower compared to rates in the country of origin (T0) and continued declining through T3. DWID rates remained low across the assessment period. RWID was significantly more prevalent than DWI across all study time points. Conclusions: Although the relatively low prevalence of DWI, drug use, and DWID among the RLIs during their initial years in the United States is encouraging, the surge in alcohol use at T3 warns about the need for interventions to prevent increases in DWI. Practical applications: Findings from the present study point to an opportunity to develop early interventions to prevent the escalation of impaired driving among RLIs to MDC.
导言:通常情况下,新近的拉丁裔移民(RLIs)在移民后不久,其酒后驾驶(DWI)率就会下降,这主要是由于他们获得车辆的机会有限。这样的过渡期为有酒驾和与酒驾司机同乘(RWID)风险的拉丁裔移民提供了干预的机会。本手稿研究了抵达佛罗里达州迈阿密/戴德县(MDC)的内陆移民酒驾、醉驾和吸毒后驾驶(DWID)的比例。方法:数据收集于 2018 年至 2021 年,来自一项纵向研究,该研究对 540 名抵达 MDC 的 RLIs 的自我报告酒驾轨迹进行了调查。在基线上,我们同时获得了移民前的回顾性数据和移民后的第一年数据。相隔一年进行的两次跟踪调查(N=531 和 N=522)收集了 RLI 在美国最初三年的数据。结果:从移民前到移民后,每月平均饮酒次数(d/m)的轨迹呈 "U "形曲线:从移民前(T0)、第一年(T1)、COVID 前的第二年(T2-BC)和大流行病封锁期间(T2-DC)以及在美国的第三年(T3),饮酒次数分别为 18.3 次/月、13.9 次/月、10.4 次/月、12.9 次/月和 16.4 次/月。非法药物使用率持续下降,从 T0 的 14.6% 降至 T3 的 2.1%。与原籍国(T0)相比,T1 的酒后驾车(DWID)发生率明显较低,并在 T3 期间持续下降。在整个评估期间,DWID 的发生率仍然很低。在所有研究时间点,RWID 的发病率都明显高于 DWI。结论:尽管在美国的最初几年中,RLIs 的酒后驾驶、吸毒和酒后驾车的发生率相对较低,这一点令人鼓舞,但在 T3 阶段,酒精使用率激增,这提醒我们需要采取干预措施,以防止酒后驾驶的增加。实际应用:本研究的结果表明,有机会制定早期干预措施,以防止 RLIs 中的受损驾驶行为升级至 MDC。
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引用次数: 0
A vehicle occupant injury prediction algorithm based on road crash and emergency medical data 基于道路碰撞和紧急医疗数据的车辆乘员伤害预测算法
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-24 DOI: 10.1016/j.jsr.2024.09.015
Tetsuya Nishimoto , Kazuhiro Kubota , Giulio Ponte
Introduction: Advanced Automatic Collision Notification (AACN) systems are an automobile safety technology designed to reduce the number of fatalities in traffic accidents by optimizing early treatment methods. AACN systems rely on robust injury prediction algorithms, however, despite the importance of time to treatment, current injury prediction algorithms used in AACN systems do not take this critical time period time into consideration. Method: This study developed a vehicle occupant injury prediction algorithm by using emergency transport time in addition to mass crash data, to determine the risk of serious injury for vehicle occupants in a road crash. Two sources of de-identified data were used: The South Australian Traffic Accident Reporting System (TARS) database and the highly detailed South Australian Serious Injury Database (SID). Firstly, the TARS data, a large statistical crash dataset, was imputed into a logistic regression analysis to produce a base injury prediction algorithm. The important effect of emergency transport time on the risk of death and serious injury was then independently quantified as an odds ratio (OR) from the SID. The ORs were converted into regression coefficients and subsequently introduced into the base injury prediction algorithm to produce an enhanced injury prediction algorithm. Results: The ORs calculated from the SID showed that the risk of death and serious injury increased with increasing transport time: 61–90 min (OR = 1.6), 91–120 min (OR = 3.3), and > 120 min (OR = 4.9), compared to a transport time of 60 min or less. An assessment of the base algorithm compared to the enhanced injury prediction algorithm through Receiver Operating Characteristic (ROC) analysis, demonstrated a prediction accuracy improvement from AUC 0.70 to AUC 0.73 when evaluating the respective algorithms. The injury prediction calculations indicate that the impact of two risk factors, transport time and age-related decline in human injury tolerance, are significant, and both have a strong influence on the increased risk of serious injury. Conclusions: The impact of emergency transport time on the risk of fatal and serious injuries was determined from a relatively small, but data rich SID. Subsequently this was incorporated into an injury prediction algorithm constructed from the large (TARS) statistical crash data set to produce an enhanced injury prediction algorithm. Practical Application: By adding the effect of transport time to enhance the basic injury prediction algorithm, an AACN that incorporates such an algorithm can be used to determine the probability of death or serious injury due to delayed treatment. Further, such a system can be used to improve policies and procedures to optimize emergency transport time.
引言高级自动碰撞通知(AACN)系统是一项汽车安全技术,旨在通过优化早期治疗方法来减少交通事故中的死亡人数。AACN 系统依赖于强大的伤害预测算法,然而,尽管治疗时间非常重要,但目前 AACN 系统中使用的伤害预测算法并未将这一关键时间段考虑在内。方法:本研究开发了一种车辆乘员伤害预测算法,除大量碰撞数据外,还使用了紧急运送时间,以确定道路碰撞中车辆乘员受重伤的风险。研究使用了两个来源的去标识化数据:南澳大利亚交通事故报告系统(TARS)数据库和高度详细的南澳大利亚重伤数据库(SID)。首先,将 TARS 数据(一个大型统计车祸数据集)归入逻辑回归分析,以生成基本伤害预测算法。然后,根据 SID 数据,将紧急交通时间对死亡和重伤风险的重要影响独立量化为几率比(OR)。将几率比转换为回归系数,然后将其引入基础伤害预测算法,从而生成增强型伤害预测算法。结果:根据 SID 计算出的 OR 显示,死亡和重伤风险随着运送时间的增加而增加:与 60 分钟或更短的运送时间相比,61-90 分钟(OR = 1.6)、91-120 分钟(OR = 3.3)和 > 120 分钟(OR = 4.9)。通过接收方操作特征(ROC)分析,对基础算法和增强型伤害预测算法进行了评估,结果表明,在对各自算法进行评估时,预测准确率从 AUC 0.70 提高到 AUC 0.73。伤害预测计算结果表明,运输时间和与年龄相关的人体伤害耐受力下降这两个风险因素的影响非常显著,而且都对严重伤害风险的增加有很大影响。结论:紧急运送时间对致命和严重伤害风险的影响是通过规模相对较小但数据丰富的 SID 确定的。随后,我们将这一结果纳入了从大型(TARS)统计碰撞数据集中构建的伤害预测算法中,从而产生了一种增强型伤害预测算法。实际应用:通过添加运输时间的影响来增强基本的伤害预测算法,采用这种算法的 AACN 可用于确定因延误治疗而导致死亡或重伤的概率。此外,这种系统还可用于改进政策和程序,以优化紧急运送时间。
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引用次数: 0
Fatal injuries among landscaping and tree care workers: Insights from NIOSH and state-based FACE reports 园林绿化和树木护理工人中的致命伤:从 NIOSH 和基于州的 FACE 报告中获得的启示
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-19 DOI: 10.1016/j.jsr.2024.10.005
Gregory D. Kearney , Nancy Romano , Anna Doub
Context: A comprehensive assessment of the National Institute for Occupational Safety and Health (NIOSH) and State-based Fatal Assessment and Control Evaluation (FACE) investigative reports involving landscaping and tree worker fatalities have not been fully examined. Methods: Narrative text from 93 FACE reports from 1987 to 2023 involving landscaping and tree care workers were reviewed, manually coded and analyzed on major variables. Univariate analyses was conducted to summarize results of decedent workers and workplace characteristics. Results: Among the total number of worker fatalities (n = 95), the most commonly reported incidents were, electrocutions from power lines (18.3%), falls from trees (16.1%), and incidents involving a worker being either caught, pulled, or dragged into wood-chipping machine (12.9%). More than 66.0% of fatal incidents occurred among tree care workers that had been on the job for one year or less. Among reports, 60.2% of employers lacked a written safety plan, and 34.4% did not provide job training to their workers. Conclusions: FACE case reports alone are not a valid measure of workplace fatalities. Nevertheless, the codification and descriptive summary of more than three decades of case reports increases understanding of circumstances and contributing risk factors associated with these tragic, and yet largely preventable incidents. A comprehensive approach is urgently needed that includes: (a) taking immediate action to reduce occupational risks while cultivating a robust safety culture across the industry, and (b) increasing research to evaluate the effectiveness of interventions and prevention measures. Practical Application: The interconnectedness of safety challenges requires a multi-faceted approach that includes addressing issues related to new and diverse workers, employer commitments to the implementation of safety plans, and comprehensive training and mentorship programs. Intervention strategies and implementation measures are essential to diminishing fatalities in these high-risk jobs.
背景:尚未对美国国家职业安全与健康研究所(NIOSH)和各州的死亡评估与控制评价(FACE)调查报告进行全面评估,这些报告涉及园林绿化和树木工人的死亡事故。研究方法:对 1987 年至 2023 年期间涉及园林绿化和树木护理工人的 93 份 FACE 报告的叙述性文字进行了审查、人工编码和主要变量分析。进行单变量分析,总结死者工人和工作场所特征的结果。结果:在工人死亡总数(n = 95)中,最常报告的事故是电线触电(18.3%)、从树上坠落(16.1%)以及工人被卷入、拉入或拖入削木机(12.9%)。超过 66.0% 的致命事故发生在工作一年或一年以下的树木护理工人身上。在报告中,60.2%的雇主没有书面安全计划,34.4%的雇主没有为工人提供工作培训。结论:仅凭 FACE 案例报告并不能有效衡量工伤死亡事故。尽管如此,对三十多年来的案例报告进行编纂和描述性总结,可以加深人们对与这些悲惨但在很大程度上可以预防的事故相关的情况和风险因素的了解。我们迫切需要采取综合措施,其中包括(a) 立即采取行动,降低职业风险,同时在整个行业培养强有力的安全文化,以及 (b) 加强研究,评估干预和预防措施的有效性。实际应用:安全挑战的相互关联性要求采取多方面的方法,包括解决与新工人和不同工人相关的问题、雇主对实施安全计划的承诺以及全面的培训和指导计划。干预策略和实施措施对于减少这些高风险工作的死亡事故至关重要。
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引用次数: 0
The aggressive driving performance caused by congestion based on behavior and EEG analysis 基于行为和脑电图分析的拥堵导致的激进驾驶表现
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-17 DOI: 10.1016/j.jsr.2024.10.004
Shuo Zhao, Geqi Qi, Peihao Li, Wei Guan
Introduction: Traffic congestion is closely related to traffic accidents, as prolonged traffic congestion often results in frustration and aggressive behavior. Moreover, in daily commuting, drivers often have to pass through multiple congested road sections, and aggressive driving performance due to exiting or re-entering traffic jams has rarely been analyzed. Method: To fill this research gap, we designed an intermittent traffic congestion scenario using a driving simulator and employed unsupervised learning algorithms to extract high-level driving patterns gathered with EEG data to investigate the continuous effects of traffic jams, particularly when drivers exit and re-enter traffic jam conditions. Results: We discovered that drivers, upon exiting congested areas, engage in abrupt braking with a decrease in braking time of approximately 0.47 s and smooth lane changes with an increase in lane change time of approximately 0.5 s to maintain high-speed driving conditions. When drivers re-enter a traffic jam, they exhibit more abrupt stop-and-go behaviors to escape the traffic jam. The results of the risk assessment of driving behavior indicated that after leaving congested areas, free-flow segments have greater risk factors than other segments. Electroencephalogram (EEG) data were analyzed to identify instances of mind-wandering when a driver transitions into free-flowing segments, followed by a substantial increase in brain activity upon re-entry into congested traffic conditions. Practical Applications: The research outcomes suggest that optimizing the road segments after congestion, using appropriate entertainment systems to reduce driver stress, and implementing adaptive traffic signals to achieve smooth transitions during intermittent congestion can reduce aggressive driving behavior and enhance traffic safety.
引言交通拥堵与交通事故密切相关,因为长时间的交通拥堵往往会导致挫败感和攻击性行为。此外,在日常通勤中,驾驶员往往需要通过多个拥堵路段,而由于驶出或再次驶入交通拥堵路段而导致的攻击性驾驶表现却很少被分析。研究方法为了填补这一研究空白,我们利用驾驶模拟器设计了一个间歇性交通拥堵场景,并采用无监督学习算法提取脑电数据收集到的高级驾驶模式,以研究交通拥堵的连续影响,尤其是驾驶员驶出和再次驶入交通拥堵状态时的影响。结果:我们发现,驾驶员在驶出拥堵区域时会突然刹车,刹车时间减少约 0.47 秒,而平稳变道则会使变道时间增加约 0.5 秒,以维持高速行驶状态。当驾驶员再次进入交通拥堵路段时,他们会表现出更多的急停急转行为,以摆脱交通拥堵。驾驶行为风险评估结果表明,在驶离拥堵区域后,自由通行路段比其他路段具有更大的风险因素。通过分析脑电图(EEG)数据,可以识别出驾驶员在过渡到自由流畅路段时的思维游离情况,以及再次进入拥堵交通状况时大脑活动的大幅增加。实际应用:研究结果表明,优化拥堵后的路段、使用适当的娱乐系统来减轻驾驶员的压力,以及实施自适应交通信号来实现间歇性拥堵期间的平稳过渡,可以减少激进驾驶行为,提高交通安全。
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引用次数: 0
Rates and ratios of fatal and nonfatal drowning attended by ambulance in New South Wales, Australia between 2010 and 2021 2010 年至 2021 年期间澳大利亚新南威尔士州救护车处理的致命和非致命溺水事件的比率和比例
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-11 DOI: 10.1016/j.jsr.2024.09.019
Edwina Mead , Chen-Chun Shu , Pooria Sarrami , Rona Macniven , Michael Dinh , Hatem Alkhouri , Lovana Daniel , Amy E. Peden
Introduction: Drowning is a preventable cause of mortality, with 279 unintentional drowning deaths per year in Australia. Despite larger estimated numbers, less is known about nonfatal drowning compared to fatalities. This study aimed to examine the burden of fatal and nonfatal drowning in the Australian state of New South Wales using pre-hospital case capture. Methods: A cross-sectional analysis of individuals attended by an ambulance in NSW for drowning between 2010 and 2021 was conducted. Ambulance data (paper-based and electronic medical records) were linked to emergency department and death registry. Ratios of fatal to nonfatal drowning were constructed overall, by sex, age, and remoteness of incident and residential locations. Results: 3,973 ambulance-attended drowning patients were identified (an annual rate of 4.16/100,000 persons). Six percent (6.1%; n = 243) died within 30 days, 82.7% (n = 201) of which died on the day of incident, including at the scene. Mean survival time for those who died between 2 and 30 days was 4.6 days. The overall ratio of fatal to nonfatal incidents was 1:15. Ratios were highest for 10–19 year-olds (1:77), females (1:22), and in metropolitan incident (1:20) and residential (1:23) locations. Across the study drowning declined by 14 incidents and 0.18 fatalities per year. Discussion: Temporal trends indicate declining drowning incidents and fatalities. However, this study highlights significant numbers of nonfatal incidents among those traditionally seen as lower risk, such as adolescents and females, necessitating a widened focus on improving water safety among these groups. Conclusions: Nonfatal drowning results in significant, yet preventable health system burden in New South Wales. Practical Applications: This study highlights the importance of documenting the full burden of drowning, including health system impacts of a preventable cause of injury and death. Such data may be used to encourage further investment in primary prevention efforts.
导言:溺水是一种可预防的死亡原因,澳大利亚每年有 279 人死于意外溺水。尽管估计数字较大,但与致命溺水相比,人们对非致命溺水的了解较少。本研究旨在通过院前病例采集,对澳大利亚新南威尔士州致命性和非致命性溺水造成的负担进行研究。研究方法对 2010 年至 2021 年期间新南威尔士州因溺水而接受救护车救治的人员进行了横截面分析。救护车数据(纸质和电子病历)与急诊科和死亡登记处的数据相链接。按照性别、年龄、事发地点和居住地点的偏远程度,构建了致命溺水与非致命溺水的总体比率。结果共确认了 3,973 名救护车接诊的溺水患者(年发病率为 4.16/100,000)。6%(6.1%;n = 243)的患者在 30 天内死亡,其中 82.7%(n = 201)的患者在事发当天死亡,包括在现场死亡。在 2 至 30 天内死亡者的平均存活时间为 4.6 天。死亡与非死亡事件的总体比例为 1:15。其中,10-19 岁儿童(1:77)、女性(1:22)、大都市(1:20)和住宅区(1:23)的比例最高。在整个研究期间,溺水事件每年减少 14 起,死亡人数每年减少 0.18 人。讨论:时间趋势表明溺水事件和死亡人数在下降。然而,本研究强调了传统上被认为风险较低的人群(如青少年和女性)中发生的大量非致命事件,因此有必要更广泛地关注改善这些人群的水上安全。结论非致命性溺水给新南威尔士州的卫生系统造成了巨大的负担,但这是可以预防的。实际应用:这项研究强调了记录溺水造成的全部负担的重要性,包括可预防的伤亡原因对卫生系统造成的影响。这些数据可用于鼓励进一步投资初级预防工作。
{"title":"Rates and ratios of fatal and nonfatal drowning attended by ambulance in New South Wales, Australia between 2010 and 2021","authors":"Edwina Mead ,&nbsp;Chen-Chun Shu ,&nbsp;Pooria Sarrami ,&nbsp;Rona Macniven ,&nbsp;Michael Dinh ,&nbsp;Hatem Alkhouri ,&nbsp;Lovana Daniel ,&nbsp;Amy E. Peden","doi":"10.1016/j.jsr.2024.09.019","DOIUrl":"10.1016/j.jsr.2024.09.019","url":null,"abstract":"<div><div><em>Introduction</em>: Drowning is a preventable cause of mortality, with 279 unintentional drowning deaths per year in Australia. Despite larger estimated numbers, less is known about nonfatal drowning compared to fatalities. This study aimed to examine the burden of fatal and nonfatal drowning in the Australian state of New South Wales using pre-hospital case capture. <em>Methods:</em> A cross-sectional analysis of individuals attended by an ambulance in NSW for drowning between 2010 and 2021 was conducted. Ambulance data (paper-based and electronic medical records) were linked to emergency department and death registry. Ratios of fatal to nonfatal drowning were constructed overall, by sex, age, and remoteness of incident and residential locations. <em>Results:</em> 3,973 ambulance-attended drowning patients were identified (an annual rate of 4.16/100,000 persons). Six percent (6.1%; n = 243) died within 30 days, 82.7% (n = 201) of which died on the day of incident, including at the scene. Mean survival time for those who died between 2 and 30 days was 4.6 days. The overall ratio of fatal to nonfatal incidents was 1:15. Ratios were highest for 10–19 year-olds (1:77), females (1:22), and in metropolitan incident (1:20) and residential (1:23) locations. Across the study drowning declined by 14 incidents and 0.18 fatalities per year. <em>Discussion:</em> Temporal trends indicate declining drowning incidents and fatalities. However, this study highlights significant numbers of nonfatal incidents among those traditionally seen as lower risk, such as adolescents and females, necessitating a widened focus on improving water safety among these groups. <em>Conclusions:</em> Nonfatal drowning results in significant, yet preventable health system burden in New South Wales. <em>Practical Applications:</em> This study highlights the importance of documenting the full burden of drowning, including health system impacts of a preventable cause of injury and death. Such data may be used to encourage further investment in primary prevention efforts.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 373-380"},"PeriodicalIF":3.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The maximum potential benefits of safety systems on light van crashes in the United States 安全系统对美国轻型货车碰撞事故的最大潜在效益
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-10 DOI: 10.1016/j.jsr.2024.09.021
Aimee E. Cox, Jessica B. Cicchino
Introduction: The retail landscape has shifted from brick-and-mortar sales to e-commerce, which surged during the COVID-19 pandemic. Light vans are popular vehicles to meet the rising home delivery demands. Two New Car Assessment Programs developed van ratings programs based on their equipment of safety features. This study was designed to estimate the maximum potential benefits that safety technologies could provide light vans based on their historical involvement in relevant crash scenarios. Methods: We used U.S. crash data from 2016—2021 to estimate the average annual total (police-reported), injury, and fatal crashes involving light vans. We determined the proportion of total crashes where front crash prevention, lane departure prevention, blind spot detection, and intelligent speed assistance systems might help the driver prevent crashes or mitigate their severity. We determined the proportions of injury and fatal crashes that resulted in an injury to someone not traveling in the light van. Results: Of the systems studied, front crash prevention that detects vehicles, pedestrians, and cyclists was relevant to largest percentage of light van crashes and could prevent as many as 17% of their involvements, 14% of their injury crashes, and 19% of their fatal crashes. Combined, the four systems have the potential to reduce up to 26% of light van crashes, 22% of their injury crashes, and 36% of their fatal crashes. Sixty-two percent of injury crashes and 56% of fatal crashes relevant to these technologies resulted in injuries or fatalities to occupants of other vehicles or other road users. Conclusions: Light vans are a growing market that can benefit from safety technology, especially when considering their impact on others with whom they share the road. Practical Applications: People and businesses in the market for a light van should seek these systems. Aftermarket products can be installed on light vans not equipped with them.
导言:在 COVID-19 大流行期间,零售业已从实体销售转向电子商务。为满足日益增长的送货上门需求,轻型厢式货车成为热门车型。两个 "新车评估计划 "根据面包车的安全性能设备制定了面包车评级计划。本研究旨在根据轻型货车在相关碰撞事故中的历史表现,估算安全技术可为其带来的最大潜在效益。方法:我们使用 2016-2021 年的美国碰撞数据来估算涉及轻型货车的年均总碰撞事故(警方报告)、受伤事故和死亡事故。我们确定了前部碰撞预防、车道偏离预防、盲点检测和智能车速辅助系统可帮助驾驶员预防碰撞或减轻碰撞严重程度的碰撞事故占总碰撞事故的比例。我们还确定了导致非轻型厢式货车乘客受伤和死亡的碰撞事故比例。结果:在所研究的系统中,检测车辆、行人和骑车人的前部碰撞预防系统与轻型厢式货车碰撞事故的相关比例最高,可预防多达 17% 的肇事事故、14% 的受伤事故和 19% 的致命事故。这四个系统加在一起,有可能减少多达 26% 的轻型货车碰撞事故、22% 的受伤碰撞事故和 36% 的致命碰撞事故。与这些技术相关的62%的受伤碰撞事故和56%的致命碰撞事故导致其他车辆的乘员或其他道路使用者受伤或死亡。结论:轻型货车是一个不断增长的市场,可以从安全技术中获益,特别是考虑到它们对与之共用道路的其他人的影响。实际应用:在轻型厢式货车市场上,人们和企业应寻求这些系统。售后市场产品可安装在未配备这些系统的轻型货车上。
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引用次数: 0
Multi-label material and human risk factors recognition model for construction site safety management 用于建筑工地安全管理的多标签材料和人为风险因素识别模型
IF 3.9 2区 工程技术 Q1 ERGONOMICS Pub Date : 2024-10-09 DOI: 10.1016/j.jsr.2024.10.002
Jeongeun Park , Sojeong Seong , Soyeon Park , Minchae Kim , Ha Young Kim
Introduction: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore, this study aims to propose a deep learning model–based multi-label risk factor recognition (MRFR) framework that automatically recognizes multiple potential material and human risk factors at construction sites. The research answers the following questions: How can a deep learning model be developed and optimized to recognize and classify multiple material and human risk factors automatically and concurrently at construction sites, and how can the decision-making process of the model be understood and improved for practical application in preemptive safety management? Methods: Data comprising 14,605 instances of eight types of material and human risk factors were collected from construction sites. Multiple risk factors can occur concurrently; thus, an optimal model for multi-label recognition of possible risk factors was developed. Results: The MRFR framework combines material and human risk factors into a single label while achieving satisfactory performance with an F1 score of 0.9981 and a Hamming loss of 0.0008. The causes of mispredictions by MRFR were analyzed by interpreting the decision basis of the model using visualization. Conclusion: This study found that the model must have sufficient capacity to detect multiple risk factors. Performance degradation in MRFR is primarily due to difficulties recognizing visual ambiguities and a tendency to focus on nearby objects when perspective is involved. Practical applications: This study contributes to safety management knowledge by developing a model to recognize multi-label material and human risk factors. Furthermore, the results can be used as guidelines for data collection methods and model improvement in the future. The MRFR framework can be used as an algorithm to recognize risk factors preemptively and automatically at real-world construction sites.
导言:建筑工地容易出现许多安全风险因素,但由于实际原因,安全管理人员很难管理这些风险因素。此外,手动直观地识别多种风险因素也具有挑战性。因此,本研究旨在提出一种基于深度学习模型的多标签风险因素识别(MRFR)框架,该框架可自动识别建筑工地潜在的多种物质和人为风险因素。本研究回答了以下问题:如何开发和优化深度学习模型,以自动并发识别建筑工地上的多种材料和人为风险因素并对其进行分类,以及如何理解和改进该模型的决策过程,以便在先期安全管理中实际应用?方法:从建筑工地收集了 14,605 个数据,包括八种物质和人为风险因素。多种风险因素可能同时出现,因此开发了一种对可能的风险因素进行多标签识别的最佳模型。结果:MRFR 框架将材料和人为风险因素合并为一个标签,同时取得了令人满意的性能,F1 分数为 0.9981,汉明损失为 0.0008。通过可视化解释模型的决策依据,分析了 MRFR 预测错误的原因。结论:本研究发现,模型必须有足够的能力来检测多种风险因素。MRFR 性能下降的主要原因是难以识别视觉模糊性,以及在涉及透视时倾向于关注附近的物体。实际应用:本研究通过开发一种识别多标签物质和人为风险因素的模型,为安全管理知识做出了贡献。此外,研究结果还可作为今后数据收集方法和模型改进的指导方针。MRFR 框架可作为一种算法,在现实世界的建筑工地上预先自动识别风险因素。
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引用次数: 0
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Journal of Safety Research
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