Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

Lower back pain and its association with whole-body vibration and manual materials handling among commercial drivers in Sabah

A cross-sectional study was undertaken to determine the prevalence of lower back pain (LBP) and its association with whole-body vibration (WBV) and manual materials handling (MMH). We studied 110 commercial vehicle drivers using a self-administered questionnaire and the VI-400Pro human vibration monitor. Prevalence of LBP was 66.4%. The percentage of drivers who had frequent manual handling of heavy loads was 45.5% and those who handled heavy loads in awkward postures accounted for 86.4%. Daily vibration A(8) averaged on the z axis was 0.25 (0.06) m·s^?2 and at vector sum was 0.29 (0.07) m·s^?2. Daily vibration exposures on the z axis, frequent manual handling of heavy loads and awkward posture during MMH were significantly associated with LBP. Drivers who are exposed to WBV and frequently handle heavy loads manually and with awkward postures probably have more LBP than drivers who are exposed to only one of these risk factors.     

配置SCR的柴油货车道路运行NOx排放分析

为了研究重型柴油货车在不同道路运行工况下的NO_x排放特性,以一辆配置选择催化还原(Selective CatalyticReduction,SCR)净化系统的国V排放标准的重型柴油货车为研究对象,开展实际道路运行车载排放测试,通过车载排放测试系统实时采集车辆行驶速度、NO_x排放体积分数与排气温度等数据,分析车速、排气温度、路况等对NO_x排放的影响。结果表明,车速低于40 km/h,NO_x排放随车速增大稍有增加; 40～70 km/h,随车速增大NO_x排放降低;高于70 km/h,随车速增大NO_x排放显著降低。车速与排气温度呈线性正相关,排气温度高于150℃,SCR才能显示出对NO_x的净化效果。市区工况车速低,排温低于150℃,SCR不能有效工作;市郊、高速工况下排温高于150℃,SCR催化效率提高,车速增大,排温升高,NO_x排放降低,因此NO_x排放市郊工况低于市区工况,高速工况低于市郊工况。配置SCR的重型柴油货车NO_x高排放区主要集中在中低车速、加速区间内。     

柴油车尾气微粒的燃烧性能

采用热解质量分析方法（TGA）对机动车柴油发动机尾气微粒（PM）的燃烧性能进行了研究。分别在空气和氮气条件下对柴油微粒的失重情况进行了测试。实验表明，柴油微粒的起始失重温度Tb为200℃左右，起始氧化温度Tox为410-450℃，最大失重温度Tmax为650℃-700℃，其可挥发组分（VOC）含量为48%-55%，增加氧气浓度仅在调温时对微粒燃烧有利。     

美国军用标准航天器试验基线与量级调整研究

对美国航天器试验的军用标准的发展进行了调研,系统地整理了MIL-STD-1540标准到SMC-S-016A标准的发展脉络,针对系统级、组件级试验基线的修订进行了整理、分析。以热试验为典型研究对象,整理了不同标准对于系统级、组件级热试验在试验温度范围、循环数、剪裁方法上的修改,对比了试验基线、试验量级的变迁,结合美国航天器型号与验证理念的发展,分析了其修订的依据,为我国相应标准的制定、修订提供了参考。     

北京市机动车排放遥感监测分析

机动车排放遥感监测反映实际道路行驶中的排放状况,对全面分析排放水平有很强的统计意义。北京市机动车排放遥感监测的CO、HC和NOx的平均浓度分别为1.94%、388×10-6和700×10-6。北京市机动车排放的CO、HC和NOx中50%分别来自于15.90%、13.98%、11.13%的高排放车,但某车辆对于一种污染物出现高排放并不意味着它对其他污染物也是高排放。根据遥感监测得到北京市轻型汽油车基于油耗的CO、HC和NOx平均尾气管排放因子分别为200.1g/L、11.05 g/L和6.68 g/L。     

深圳城市大气中非甲烷烃季节变化特征

应用GCMS-QP2010对深圳2015~2016年4个季节大气56种非甲烷碳氢化合物（NMHCs）进行在线监测分析.从成分来看,四季总NMHCs平均浓度为23.6×10^-9,呈现出冬季 > 秋季 > 夏季≈春季的变化特征,其中烷烃比例最高（65.4%~74.7%）,其次是芳香烃（13.3%~21.7%）和烯烃（7.1%~11.6%）,丙烷、甲苯、乙烷、正己烷、正丁烷、乙炔、2-甲基戊烷、异丁烷、乙烯和3-甲基戊烷是浓度最高的10个物种.相关性和日变化分析表明,深圳大气中NMHCs受到机动车、溶剂挥发相关工业源以及植物释放等多重来源的共同影响,其中甲苯、2-甲基戊烷、3-甲基戊烷和正己烷受工业源影响最为显著,而异戊二烯主要来自于天然源.     

Driving self-regulation and ride service utilization in a multicommunity,multistate sample of U.S. older adults

Objectives: This study examined a multicommunity alternative transportation program available 24 hours a day, 7 days a week, for any purpose, offering door-through-door service in private automobiles to members who either do not drive or are transitioning away from driving. Specific aims were to describe the characteristics of members by driving status and ride service usage of these members.

Methods: Data came from administrative records maintained by a nonprofit ride service program and include 2,661 individuals aged 65+ residing in 14 states who joined the program between April 1, 2010, and November 8, 2013. Latent class analysis was used to group current drivers into 3 classes of driving status of low, medium, and high self-regulation, based on their self-reported avoidance of certain driving situations and weekly driving frequency. Demographics and ride service use rate for rides taken through March 31, 2014, by type of ride (e.g., medical, social, etc.) were calculated for nondrivers and drivers in each driving status class.

Results: The majority of ride service users were female (77%) and aged 65–74 years (82%). The primary method of getting around when enrolling for the transportation service was by riding with a friend or family member (60%). Among the 67,883 rides given, nondrivers took the majority (69%) of rides. Medical rides were the most common, accounting for 40% of all rides.

Conclusions: Reported ride usage suggests that older adults are willing to use such ride services for a variety of trips when these services are not limited to specific types (e.g., medical). Further research can help tailor strategies to encourage both nondrivers and drivers to make better use of alternative transportation that meets the special needs of older people.     

Effects of vehicle power on passenger vehicle speeds

Objectives: During the past 2 decades, there have been large increases in mean horsepower and the mean horsepower-to–vehicle weight ratio for all types of new passenger vehicles in the United States. This study examined the relationship between travel speeds and vehicle power, defined as horsepower per 100 pounds of vehicle weight.

Methods: Speed cameras measured travel speeds and photographed license plates and drivers of passenger vehicles traveling on roadways in Northern Virginia during daytime off-peak hours in spring 2013. The driver licensing agencies in the District of Columbia, Maryland, and Virginia provided vehicle information numbers (VINs) by matching license plate numbers with vehicle registration records and provided the age, gender, and ZIP code of the registered owner(s). VINs were decoded to obtain the curb weight and horsepower of vehicles. The study focused on 26,659 observed vehicles for which information on horsepower was available and the observed age and gender of drivers matched vehicle registration records. Log-linear regression estimated the effects of vehicle power on mean travel speeds, and logistic regression estimated the effects of vehicle power on the likelihood of a vehicle traveling over the speed limit and more than 10 mph over the limit.

Results: After controlling for driver characteristics, speed limit, vehicle type, and traffic volume, a 1-unit increase in vehicle power was associated with a 0.7% increase in mean speed, a 2.7% increase in the likelihood of a vehicle exceeding the speed limit by any amount, and an 11.6% increase in the likelihood of a vehicle exceeding the limit by 10 mph. All of these increases were highly significant.

Conclusions: Speeding persists as a major factor in crashes in the United States. There are indications that travel speeds have increased in recent years. The current findings suggest the trend toward substantially more powerful vehicles may be contributing to higher speeds. Given the strong association between travel speed and crash risk and crash severity, this is cause for concern.