Analysis of mechanics of side impact test defined in UN/ECE Regulation 129

Objective: This article discusses differences between a side impact procedure described in United Nations/Economic Commission for Europe (UN/ECE) Regulation 129 and scenarios observed in real-world cases.

Methods: Numerical simulations of side impact tests utilizing different boundary conditions are used to compare the severity of the Regulation 129 test and the other tests with different kinematics of child restraint systems (CRSs). In the simulations, the authors use a validated finite element (FE) model of real-world CRSs together with a fully deformable numerical model of the Q3 anthropomorphic test device (ATD) by Humanetics Innovative Solution, Inc.

Results: The comparison of 5 selected cases is based on the head injury criterion (HIC) index. Numerical investigations reveal that the presence of oblique velocity components or the way in which the CRS is mounted to the test bench seat fixture is among the significant factors influencing ATD kinematics. The results of analyses show that the side impact test procedure is very sensitive to these parameters. A side impact setup defined in Regulation 129 may minimize the effects of the impact.

Conclusions: It is demonstrated that an artificial anchorage in the Regulation 129 test does not account for a rotation of the CRS, which should appear in the case of a realistic anchorage. Therefore, the adopted procedure generates the smallest HIC value, which is at the level of the far-side impact scenario where there are no obstacles. It is also shown that the presence of nonlateral acceleration components challenges the quality of a CRS and its headrest much more than a pure lateral setup.     

Cross-correlation between the controlled collision environment and real-world motor vehicle collisions: Evaluating the protection of the thoracic side airbag

Objective: Thoracic side airbags (tSABs) were integrated into the vehicle fleet to attenuate and distribute forces on the occupant's chest and abdomen, dissipate the impact energy, and move the occupant away from the intruding structure, all of which reduce the risk of injury. This research piece investigates and evaluates the safety performance of the airbag unit by cross-correlating data from a controlled collision environment with field data.

Method: We focus exclusively on vehicle–vehicle lateral impacts from the NHTSA's Vehicle Crash Test Database and NASS-CDS database, which are replicated in the controlled environment by the (crabbed) barrier impact. Similar collisions with and without seat-embedded tSABs are matched to each other and the injury risks are compared.

Results: Results indicated that dummy-based thoracic injury metrics were significantly lower with tSAB exposure (P <.001). Yet, when the controlled collision environment data were cross-correlated with NASS-CDS collisions, deployment of the tSAB indicated no association with thoracic injury (tho. MAIS 2+ unadjusted relative risk [RR] = 1.14; 90% confidence interval [CI], 0.80–1.62; tho. MAIS 3+ unadjusted RR = 1.12; 90% CI, 0.76–1.65).

Conclusion: The data from the controlled collision environment indicated an unequivocal benefit provided by the thoracic side airbag for the crash dummy; however, the real-world collisions demonstrate that no benefit is provided to the occupant. This has resulted from a noncorrelation between the crash test/dummy-based design taking the abstracting process too far to represent the real-world collision scenario.     

Crashes of novice teenage drivers: characteristics and contributing factors

OBJECTIVE: The initial months of licensure are especially hazardous for teenagers. Factors leading to crashes of novice 16-year-old drivers were identified. METHOD: Sixteen year-olds in Connecticut who were involved in nonfatal crashes during the first 8 months of licensure were interviewed, and police crash reports were examined. Crash types and contributing factors were identified. RESULTS: Three-fourths of the crash-involved teenagers were at fault. Their crashes resulted primarily when they ran off the road, rear ended another vehicle, or collided with another vehicle that had the right-of-way. Three factors contributed about equally to their crashes: failing to detect another vehicle or traffic control, speeding, and losing control of the vehicle or sliding. Slippery roads also were an important factor. Most failures to detect another vehicle or traffic control involved not looking thoroughly, distraction, or inattention. DISCUSSION: Based on the findings, potential countermeasures for reducing crashes of novice teenage drivers include adequate practice driving, in-vehicle monitoring devices, and electronic stability control. IMPACT ON INDUSTRY: More than half of the nonfatal, at-fault crashes of newly licensed 16-year-old drivers involved more than one contributing factor including speed, loss of control, and slippery roads. Efforts to reduce teenage crashes should focus on these factors.     

道路交通事故车辆行驶速度数值方法研究

为准确测算不同事故形态下的车辆行驶速度,客观公正地处理各类道路交通事故,笔者对常见的道路交通事故车辆行驶速度的计算方法进行分析和研究,系统地归纳了当前分析交通事故车辆行驶速度的一系列方法,主要是理论计算法、软件分析法、经验公式法、实验验证法、仪器读取法、监控信息测定法和人体特征损伤分析法等,为科学分析事故成因提供重要依据,也可作为道路交通事故处理工作的重要参考。     

摩托车防护服装设计方法研究

本课题在摩托车防护服装设计因素分析的基础上,对摩托车防护服装的设计方法进行了系统研究,探讨了能够实现安全防护性能和舒适性能的摩托车防护服的设计方法。通过系统完整的设计过程,设计人员能够形成一个正确的设计理念和体系化的设计步骤。     

Freight transport and non-driving work duties as predictors of falling asleep at the wheel in urban areas of Crete

BACKGROUND: This study investigated the impact of subjective reports of drowsy driving and non-driving duties on the falling asleep responses and road crash involvement of professional drivers in Crete. An attempt was also made to elucidate other driving parameters, such as freight transportation, which could be potential predictors of risky driving, after controlling for lifestyle patterns. METHOD: A sample of 317 professional drivers was studied through personal interviews. The interview questionnaire included items about sleep and fatigue as contributing factors to falling asleep probability and crash risk. In addition, the drivers reported the type of freight they carried in their last trip, as well as practices such as smoking and alcohol consumption. RESULTS: The first logistic regression analysis showed that the most significant predictors of falling asleep at the wheel were transportation of fruits/vegetables and livestock, non-driving hours of work, insufficient hours of sleep, and smoking. The second logistic regression analysis revealed all the previous items as powerful factors of crash probability, including the transportation of express freight and freezer. IMPACT: The findings of the current study are discussed as they pertain to directions for future studies and for the development of fatigue countermeasures.     

基于视频数据的城市隧道交通运行特征与安全研究

为降低城市隧道交通事故率,提高车辆出行安全,以上海市翔殷路跨江隧道为研究对象,利用视频采集技术、车辆跟踪调查采集隧道交通运行数据,分析城市隧道交通运行特征及驾驶员在隧道内驾驶行为特征,并进一步研究其与隧道交通事故的关系。对隧道内车辆的速度、密度分布,变道行为进行分析,发现隧道南线入口处内外断面平均车速的显著差异可以解释该处追尾事故高发,隧道内车辆频繁变换车道导致隧道侧碰事故占较大比例。为改善城市隧道交通安全,在隧道内进行限速和限制车辆变道是必要的。     

Application of machine learning technique for optimizing roadside design to decrease barrier crash costs,a quantile regression model approach

Introduction: In-transport vehicles often leave the travel lane and encroach onto natural objects on the roadsides. These types of crashes are called run-off the road crashes (ROR). Such crashes accounts for a significant proportion of fatalities and severe crashes. Roadside barrier installation would be warranted if they could reduce the severity of these types of crashes. However, roadside barriers still account for a significant proportion of severe crashes in Wyoming. The impact of the crash severity would be higher if barriers are poorly designed, which could result in override or underride barrier crashes. Several studies have been conducted to identify optimum values of barrier height. However, limited studies have investigated the monetary benefit associated with adjusting the barrier heights to the optimal values. In addition, few studies have been conducted to model barrier crash cost. This is because the crash cost is a heavily skewed distribution, and well-known distributions such as linear or poison models are incapable of capturing the distribution. A semi-parametric distribution such as asymmetric Laplace distribution can be used to account for this type of sparse distribution. Method: Interaction between different predictors were considered in the analysis. Also, to account for exposure effects across various barriers, barrier lengths and traffic volumes were incorporated in the models. This study is conducted by using a novel machine-learning-based cost-benefit optimization to provide an efficient guideline for decision makers. This method was used for predicting barrier crash costs without barrier enhancement. Subsequently the benefit was obtained by optimizing traffic barrier height and recalculating the benefit and cost. The trained model was used for crash cost prediction on barriers with and without crashes. Results: The results of optimization clearly demonstrated the benefit of optimizing the heights of road barriers around the state. Practical Applications: The findings can be utilized by the Wyoming Department of Transportation (WYDOT) to determine the heights of which barriers should be optimized first. Other states can follow the procedure described in this paper to upgrade their roadside barriers.     

Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

IntroductionAs a convenient and affordable means of transportation, the e-bike is widely used by different age rider groups and for different travel purposes. The underlying reasons for e-bike riders suffering from severe injury may be different in each case.MethodThis study aims to examine the underlying risk factors of severe injury for different groups of e-bike riders by using a combined method, integration of a classification tree and a logistic regression model. Three-year of e-bike crashes occurring in Hunan province are extracted, and risk factor including rider’s attribute, opponent vehicle and driver’s attribute, improper behaviors of riders and drivers, road, and environment characteristics are considered for this analysis.ResultsE-bike riders are segmented into five groups based on the classification tree analysis, and the group of non-occupational riders aged over 55 in urban regions is associated with the highest likelihood of severe injury among the five groups. The logistics analysis for each group shows that several risk factors such as high-speed roads have commonly significant effects on injury severity for different groups; while major factors only have significant effects for specific groups.Practical applicationBased on model results, policy implications to alleviate the crash injury for different e-bike riders groups are recommended, which mainly include enhanced education and enforcement for e-bike risky behaviors, and traffic engineering to regulate the use of e-bikes on high speed roads.