On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

高速公路交通流实时安全性评价

根据多条高速公路近3年的事故数据及实时交通流数据,分析了高速公路运营安全性的相关影响因素,在此基础上提出了车速变异系数指标及其分级标准,建立了考虑交通流量、平均车速、车速变异系数、天气条件等多因素的高速公路交通流运行风险预测多元模型,并提出了高速公路交通流实时行车风险指数TRI指标,制定了交通流状态实时安全性评价标准,最后给出了高速公路交通流混合交通状态下的实时安全风险评价流程。通过西攀高速公路的交通流实时数据,验证了模型的有效性。     

Perceptions of safety on a shared road: Driving,cycling, or walking near an autonomous vehicle

Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.     

基于事故逆向再现的车辆追尾事故仿真分析研究

为认定车辆碰撞事故的性质及责任，动态还原事故发生过程。提出1种逆向事故分析再现方法，经过坐标系旋转后构建确定方程组的车辆碰撞瞬间解析计算模型，并经过实车碰撞实验数据验证其计算精度与三维再现的准确性。利用Crashview软件对1起车辆追尾事故进行仿真分析，解析计算得出车辆碰撞瞬间车速和碰撞前行驶车速，实现事故二维过程重构和三维模拟再现。结果表明:计算分析车速与VDR记录车速误差在5%以内，模拟再现轨迹与事故现场实测痕迹基本吻合，所构建的方法可有效用于车辆追尾碰撞事故辅助分析鉴定，为事故过程分析提供理论依据。     

汽车道路试验安全管理体系建设研究

为了保障汽车道路试验安全、提高安全管理水平,设计提出汽车道路试验安全管理体系(SMS)总方案。根据OHSAS 18001和安全标准化规范的特点分析,论证了融合两种体系建立道路试验SMS的适用性。并从政策与目标、风险管理、安全保证和安全促进四个模块阐述了该体系的搭建思路、框架内容和搭建步骤。最后,以某自主品牌车企研发中心道路试验的应用情况为例进行效果评估,结果表明:汽车道路试验SMS能有效防控风险、提升试验安全管理水平。     

基于风险的危险品道路运输安全容量研究

为了确定危险品道路运输安全容量，保障危险品道路运输安全，基于风险理念，使用对比研究方法，提出危险品道路运输个人风险基准，建立改进的危险品道路运输定量风险评估模型。结合提出的风险基准，提出危险品运输安全容量确定方法，解决了单一类别和多种类别危险品运输安全容量确定问题。研究结果表明:在某城市化工集聚区的实例应用该方法，能够大幅度降低危险品道路运输潜在生命损失，有助于保障危险品道路运输安全。     

道路交通噪声预测声源简化研究

为了分析《环境影响评价技术导则声环境》(HJ 2.4—2009)中将道路声源简化为1条位于道路中心线处的线声源与按照车道数简化为多条线声源之间的误差,针对不同宽度的道路,推导了多条线声源与1条线声源在接收点噪声影响的误差计算公式,并基于Predictor-lima预测软件预测和现场噪声衰减规律实测进行了验证。研究结果表明,对于接收点到道路边缘的距离大于道路宽度的情况,可简化为1条线声源;对于接收点到道路边缘的距离小于道路宽度的情况,应按照车道数简化为多条线声源。     

Changes in motorcycle-related injuries and deaths after mandatory motorcycle helmet law in a district of Vietnam

Objective: Our study measured the change in head injuries and deaths among motorcycle users in Cu Chi district, a suburban district of Ho Chi Minh City.

Methods: Hospital records for road traffic injuries (RTIs) were collected from the Cu Chi Trauma Centre and motorcycle-related death records were obtained from mortality registries in commune health offices. Head injury severity was categorized using the Abbreviated Injury Score (AIS). Rate ratios (RRs) were used to compare rates pre- and post-law (2005/2006–2009/2010). Cu Chi's population, stratified by year, age, and sex, was used as the denominator.

Results: Of records identifying the transportation mode at the time of injury, motorcyclists accounted for most injuries (3,035, 87%) and deaths (238, 90%). Head injuries accounted for 70% of motorcycle-related hospitalizations. Helmet use was not recorded in any death records and not in 97% of medical records. Males accounted for most injuries (73%) and deaths (88%). The median age was 28 years and 32 years for injuries and deaths, respectively. Compared to the pre-law period, rates of motorcycle injuries (RR = 0.53; 95% confidence interval [CI], 0.49–0.58), head injuries (RR = 0.35; 95% CI, 0.31–0.39), severe head injuries (RR = 0.47; 95% CI, 0.34–0.63), and deaths (RR = 0.69; 95% CI, 0.53–0.89) significantly decreased in the post-law period.

Conclusions: Rates of head injuries and deaths among motorcycle riders decreased significantly after implementation of the mandatory helmet law in Vietnam. To further examine the impact of the motorcycle helmet law, including compliance and helmet quality, further emphasis should be placed on gathering helmet use data from injured motorcyclists.     

Reliability and validity of the Multidimensional Driving Style Inventory in Chinese drivers

Objective: The Multidimensional Driving Style Inventory (MDSI) has been widely used in assessing the associations between driving styles and traffic violations and accidents in different cultural contexts. Due to the lack of a valid instrument to assess driving style, studies concerning driving style and its influence factors are limited in China. Thus, this study aimed to adapt and validate a Chinese version of the MDSI.

Methods: Seven hundred and sixty drivers aged from 19 to 60 years old were asked to complete the MDSI and a personality scale (trait anger, sensation seeking, altruism, and normlessness). Exploratory factory analysis (EFA) and confirmatory factor analysis (CFA) were used to obtain the factorial structure of the MDSI. The external validity of the MDSI was then evaluated by examining the associations between driving styles and personality traits, demographic variables, and traffic violations and crashes.

Results: EFA revealed a 6-factor structure of the MDSI (i.e., risky, anxious, angry, distress reduction, careful, and dissociative driving styles). CFA confirmed that the model fit of the MDSI was acceptable. The MDSI factors were moderately or weakly correlated with trait anger, sensation seeking, altruism, and normlessness. Significant gender and age differences in driving styles were found. Moreover, drivers who had traffic violations or crashes in the past year scored higher on risky and angry driving styles and lower on careful driving style than those who had not have traffic violations or crashes.

Conclusions: The Chinese version of the MDSI proved to be a reliable, valid, and highly useful instrument. It could be used to assess Chinese drivers who are at risk due to their maladaptive driving styles.     

机动车尾气对人体健康的危害

随着城市机动车保有量的不断增加,机动车尾气已经成为影响我国城市大气环境的主要问题之一。本文以儿童和交通警察两类人群为例,分析了机动车尾气污染给人体呼吸系统、免疫系统、心脑血管系统等造成的危害。结果表明,由于儿童的呼吸带与机动车尾气排放带非常接近,交警在机动车尾气环境中暴露事件太长,导致这两类人群的呼吸系统极易遭到破坏,发生支气管炎等疾病的概率大大增加;而免疫球蛋白水平和血压发生改变导致了抵抗能力的下降。最后建议通过采取发展公共交通、加强城市交通管理、完善检查维修制度等有效措施加强机动车尾气治理,减少对人体健康产生的危害。