Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

浅谈汽车内空气污染来源及影响因素

随着中国社会经济的不断发展,人民生活水平的不断提高,汽车已逐渐成为人们日常生活中的一部分。然而在世界的范围内,汽车内的空气污染问题已越来越得到重视,但就国内的情况而言,汽车内的空气污染问题却没有随着汽车的普及而得到人们足够的认识。有见及此,本文指出了造成汽车内空气污染的几种主要污染来源及其有可能产生的危害,同时讲述了影响车内空气质量的几种主要因素,并就此给出了相关污染减轻的建议,希望籍此减少汽车内空气污染对人们健康的危害。     

高超声速飞行器异型气膜孔无喷流热增量研究

目的获取高超声速飞行器气膜孔不喷流时的热负荷增量。方法通过计算流体力学(CFD)方法针对典型高超声速飞行器50km、飞行马赫数为15条件下的无开孔、有开孔气膜冷、有开孔无喷流3种工况开展壁面热流分布研究。结果无开孔的最大热流分布在头部滞止点附近,约为2.2 MW/m~2,有气膜冷却的工况热流最高值在侧面气膜孔没有覆盖到的部位,约为1.4 MW/m~2,有异型孔但是不喷流的工况,热流密度最大值主要分布在开孔附近,最大值大于3.3 MW/m~2。结论对于在高超声速飞行器表面开孔采用气膜冷却方式冷却时,如果由于某种原因气膜孔不喷流,那么在孔的附近乃至整个滞止区域附近的热流负荷将会大幅度升高。     

活性炭纤维在机动车尾气净化中的研究与应用展望

机动车尾气净化控制的一个难点在于冷启动阶段，此时由于温度较低，催化剂尚未完全起作用，导致排出的污染物浓度较高。阐述了活性炭纤维的基本特性，特别是其低温吸附与催化性能对NO和CO的转化作用，讨论了活性炭纤维作为机动车尾气净化材料所需的改性及方法，并展望了其应用前景。     

国五国六汽车尾气颗粒物的生态毒性分析

随着汽车使用量的增加,汽车尾气的环境污染及毒性效应已引起社会的关注.我国于2016年12月23日发布了《轻型汽车污染物排放限值及测量方法（中国第六阶段）》,现阶段尚未发现国五和国六两类汽车尾气颗粒物的生态毒性及比较的研究报道.该文选取国五与国六两类汽车为研究对象,对两类汽车尾气颗粒物的排放情况及主要的半挥发性有机物（SVOCs）进行分析;选用发光菌为受试生物,研究两类汽车尾气颗粒物对发光菌的毒性效应.结果发现：①单位距离下,国五和国六汽车尾气颗粒物质量分别为（8.81±2.92）和（1.51±0.35）mg·km^-1,表明单位距离下国六汽车尾气颗粒物质量显著低于国五汽车;②在检出的SVOCs中,多环芳烃为国五和国六汽车尾气颗粒物中主要的有机污染物;③国五货车、国五轿车和国六汽车尾气颗粒物对发光菌的EC₅₀分别为42.00、41.99和5.37 mg·L^-1,表明本次的研究车辆中单位质量国六汽车尾气颗粒物的毒性高于国五汽车.研究结果表明国六标准的实施可对我国环境空气质量的改善起到重要支撑作用,下一步还应继续开展国六汽车尾气颗粒物的生态毒理学研究工作.     

Gaseous emissions from compressed natural gas buses in urban road and highway tests in China

The natural gas vehicle market is rapidly developing throughout the world, and the majority of such vehicles operate on compressed natural gas(CNG). However, most studies on the emission characteristics of CNG vehicles rely on laboratory chassis dynamometer measurements, which do not accurately represent actual road driving conditions. To further investigate the emission characteristics of CNG vehicles, two CNG city buses and two CNG coaches were tested on public urban roads and highway sections. Our results show that when speeds of 0–10 km/hr were increased to 10–20 km/hr, the CO_2, CO, nitrogen oxide(NO_x), and total hydrocarbon(THC) emission factors decreased by(71.6 ± 4.3)%,(65.6 ± 9.5)%,(64.9 ± 9.2)% and(67.8 ± 0.3)%, respectively. In this study, The Beijing city buses with stricter emission standards(Euro Ⅳ) did not have lower emission factors than the Chongqing coaches with Euro Ⅱ emission standards. Both the higher emission factors at 0–10 km/hr speeds and the higher percentage of driving in the low-speed regime during the entire road cycle may have contributed to the higher CO_2 and CO emission factors of these city buses. Additionally, compared with the emission factors produced in the urban road tests, the CO emission factors of the CNG buses in highway tests decreased the most(by 83.2%), followed by the THC emission factors, which decreased by 67.1%.     

On-board measurements of gaseous pollutant emission characteristics under real driving conditions from light-duty diesel vehicles in Chinese cities

A total of 15 light-duty diesel vehicles(LDDVs) were tested with the goal of understanding the emission factors of real-world vehicles by conducting on-board emission measurements. The emission characteristics of hydrocarbons(HC) and nitrogen oxides(NOx) at different speeds, chemical species profiles and ozone formation potential(OFP) of volatile organic compounds(VOCs) emitted from diesel vehicles with different emission standards were analyzed. The results demonstrated that emission reductions of HC and NOxhad been achieved as the control technology became more rigorous from Stage I to Stage IV. It was also found that the HC and NOxemissions and percentage of O2 dropped with the increase of speed, while the percentage of CO2 increased. The abundance of alkanes was significantly higher in diesel vehicle emissions, approximately accounting for 41.1%–45.2%, followed by aromatics and alkenes. The most abundant species were propene,ethane, n-decane, n-undecane, and n-dodecane. The maximum incremental reactivity(MIR)method was adopted to evaluate the contributions of individual VOCs to OFP. The results indicated that the largest contributors to O3 production were alkenes and aromatics, which accounted for 87.7%–91.5%. Propene, ethene, 1,2,4-trimethylbenzene, 1-butene, and1,2,3-trimethylbenzene were the top five VOC species based on their OFP, and accounted for 54.0%-64.8% of the total OFP. The threshold dilution factor was applied to analyze the possibility of VOC stench pollution. The majority of stench components emitted from vehicle exhaust were aromatics, especially p-diethylbenzene, propylbenzene, m-ethyltoluene, and p-ethyltoluene.     

The useful field of view assessment predicts simulated commercial motor vehicle driving safety

Objective: The Useful Field of View (UFOV) assessment, a measure of visual speed of processing, has been shown to be a predictive measure of motor vehicle collision (MVC) involvement in an older adult population, but it remains unknown whether UFOV predicts commercial motor vehicle (CMV) driving safety during secondary task engagement. The purpose of this study is to determine whether the UFOV assessment predicts simulated MVCs in long-haul CMV drivers.

Method: Fifty licensed CMV drivers (M_age = 39.80, SD = 8.38, 98% male, 56% Caucasian) were administered the 3-subtest version of the UFOV assessment, where lower scores measured in milliseconds indicated better performance. CMV drivers completed 4 simulated drives, each spanning approximately a 22.50-mile distance. Four secondary tasks were presented to participants in a counterbalanced order during the drives: (a) no secondary task, (b) cell phone conversation, (c) text messaging interaction, and (d) e-mailing interaction with an on-board dispatch device.

Results: The selective attention subtest significantly predicted simulated MVCs regardless of secondary task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC in the simulated drive. The e-mail interaction secondary task significantly predicted simulated MVCs with a 4.14 times greater risk of an MVC compared to the no secondary task condition. Subtest 3, a measure of visual speed of processing, significantly predicted MVCs in the email interaction task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC during the email interaction task.

Conclusions: The UFOV subtest 3 may be a promising measure to identify CMV drivers who may be at risk for MVCs or in need of cognitive training aimed at improving speed of processing. Subtest 3 may also identify CMV drivers who are particularly at risk when engaged in secondary tasks while driving.     

Sustainable design for automotive products: Dismantling and recycling of end-of-life vehicles

The growth in automotive production has increased the number of end-of-life vehicles (ELVs) annually. The traditional approach ELV processing involves dismantling, shredding, and landfill disposal. The “3R” (i.e., reduce, reuse, and recycle) principle has been increasingly employed in processing ELVs, particularly ELV parts, to promote sustainable development. The first step in processing ELVs is dismantling. However, certain parts of the vehicle are difficult to disassemble and use in practice. The extended producer responsibility policy requires carmakers to contribute in the processing of scrap cars either for their own developmental needs or for social responsibility. The design for dismantling approach can be an effective solution to the existing difficulties in dismantling ELVs. This approach can also provide guidelines in the design of automotive products. This paper illustrates the difficulty of handling polymers in dashboards. The physical properties of polymers prevent easy separation and recycling by using mechanical methods. Thus, dealers have to rely on chemical methods such as pyrolysis. Therefore, car designers should use a single material to benefit dealers. The use of materials for effective end-of-life processing without sacrificing the original performance requirements of the vehicle should be explored.     

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM_2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m³ to 12.283 µg/m³ compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM_2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.