Driving performance comparing older versus younger drivers

Objectives: A cross-sectional study was conducted at the Touro University California campus to compare differences in reaction times and driving performance of younger adult drivers (18–40 years) and older adult drivers (60 years and older). Each test group consisted of 38 participants.

Methods: A Simple Visual Reaction Test (SVRT) tool was used to measure reaction times. The STISIM Drive M100 driving simulator was used to assess driving parameters. Driving performance parameters included mean lane position, standard deviation of mean lane position measured, mean speed, standard deviation of mean speed, car-following delay, car-following modulus, car-following coherence, off-road accidents, collisions, pedestrians hit, and traffic light tickets.

Results: Compared to younger participants, older drivers experienced significantly slower reaction times (510.0 ± 208.8 vs. 372.4 ± 96.1 ms, P =.0004), had more collisions (0.18 ± 0.39 vs. none, P =.0044), drove slower (44.6 ± 6.6 vs. 54.9 ± 11.7 mph, P <.0001), deviated less in speed (12.6 ± 4.3 vs. 16.8 ± 6.3, P =.0011), and were less able to maintain a constant distance behind a pace car (0.42 ± 0.23 vs. 0.59 ± 0.24; P =.0025).

Conclusions: Differences exist in driving patterns of older and younger drivers as measured by reaction times and driving simulator outcomes. These results are the first to compare these 2 specific adult age groups' driving performance as measured by a standardized driving simulator scenario. Identifying these differences is essential in addressing them and preventing future traffic injuries.     

Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Objective: We studied the changes in driving fatigue levels of experienced and inexperienced drivers at 3 periods of the day: 9:00 a.m.–12:00 p.m., 12:00 p.m.–2:00 p.m., and 4:00 p.m.–6:00 p.m.

Methods: Thirty drivers were involved in 120-min real-car driving, and sleepiness ratings (Stanford Sleepiness Scale, SSS; Hoddes et al. 1973 Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology. 1973;10:431–436.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), electroencephalogram (EEG) signals, and heart rates (HRs) were recorded. Together with principal component analysis, the relationship between EEG signals and HR was explored and used to determine a comprehensive indicator of driving fatigue. Then the comprehensive indicator was assessed via paired t test.

Results: Experienced and inexperienced drivers behaved significantly differently in terms of subjective fatigue during preliminary trials. At the beginning of trials and after termination, subjective fatigue level was aggravated with prolonged continuous driving. Moreover, we discussed the changing rules of EEG signals and HR and found that with prolonged time, the ratios of δ and β waves significantly declined, whereas that of the θ wave significantly rose. The ratio of (α + θ)/β significantly rose both before trials and after termination, but HR dropped significantly. However, one-factor analysis of variance shows that driving experience significantly affects the θ wave, (α + θ)/β ratio, and HR.

Conclusions: We found that in a monotonous road environment, fatigue symptoms occurred in inexperienced drivers and experienced drivers after about 60 and 80 min of continuous driving, respectively. Therefore, as for drivers with different experiences, restriction on continuous driving time would avoid fatigued driving and thereby eliminate traffic accidents. We find that the comprehensive indicator changes significantly with fatigue level. The integration of different indicators improves the recognition accuracy of different driving fatigue levels.     

Driving in mild cognitive impairment: The role of depressive symptoms

Objectives: Previous studies indicate a negative association between depression and driving fitness in the general population. Our goal was to cover a gap in the literature and to explore the link between depressive symptoms and driving behavior in individuals with mild cognitive impairment (MCI) through the use of a driving simulator experiment.

Methods: Twenty-four individuals with MCI (mean age = 67.42, SD = 7.13) and 23 cognitively healthy individuals (mean age = 65.13, SD = 7.21) were introduced in the study. A valid driving license and regular car use served as main inclusion criteria. Data collection included a neurological/neuropsychological assessment and a driving simulator evaluation. Depressive symptomatology was assessed with the Patient Health Questionnaire (PHQ-9).

Results: Significant interaction effects indicating a greater negative impact of depressive symptoms in drivers with MCI than in cognitively healthy drivers were observed in the case of various driving indexes, namely, average speed, accident risk, side bar hits, headway distance, headway distance variation, and lateral position variation. The associations between depressive symptoms and driving behavior remained significant after controlling for daytime sleepiness and cognition.

Conclusions: Depressive symptoms could be a factor explaining why certain patients with MCI present altered driving skills. Therefore, interventions for treating the depressive symptoms of individuals with MCI could prove to be beneficial regarding their driving performance.     

Driving restrictions post-stroke: Physicians' compliance with regulations

Objective: Suffering a stroke might lead to permanent cognitive and/or physical impairment. It has been shown that these impairments could have an impact on an individual's fitness to drive. In Sweden, as in many other countries, there are regulations on driving cessation post-stroke. Information on driving cessation should be given to all patients and noted in the journal. The present study sought to determine physician's compliance to driving regulations post-stroke as well as follow-up and gender aspects.

Method: A retrospective study of medical records on stroke patients was carried out. The study covered all of the medical records on stroke incidents (n = 342) during a year at a typical medium to large-sized hospital in Sweden.

Results: A journal entry on driving cessation post-stroke was missing in 81% of the medical records. Only 2% of the patients were scheduled for a follow-up meeting specifically concerning fitness to drive. Significantly more men than women had an entry on driving in the journal.

Conclusions: We conclude that the Swedish regulations on driving cessation post-stroke were not followed at the participating hospital. It is crucial that all stroke patients receive information on driving cessation because their condition might affect fitness to drive. Analysis of follow-up records showed that there was no consistent method for assessment of a patient's fitness to drive. There was also a gender difference in the material, which warrants further investigation.     

Literature review on risky driving videos on YouTube: Unknown effects and areas for concern?

Objective: Entry of terms reflective of extreme risky driving behaviors into the YouTube website yields millions of videos. The majority of the top 20 highly subscribed automotive YouTube websites are focused on high-performance vehicles, high speed, and often risky driving. Moreover, young men are the heaviest users of online video sharing sites, overall streaming more videos, and watching them longer than any other group. The purpose of this article is to review the literature on YouTube videos and risky driving.

Methods: A systematic search was performed using the following specialized database sources—Scopus, PubMed, Web of Science, ERIC, and Google Scholar—for the years 2005–2015 for articles in the English language. Search words included “YouTube AND driving,” “YouTube AND speeding,” “YouTube AND racing.”

Results: No published research was found on the content of risky driving videos or on the effects of these videos on viewers. This literature review presents the current state of our published knowledge on the topic, which includes a review of the effects of mass media on risky driving cognitions; attitudes and behavior; similarities and differences between mass and social media; information on the YouTube platform; psychological theories that could support YouTube's potential effects on driving behavior; and 2 examples of risky driving behaviors (“sidewalk skiing” and “ghost riding the whip”) suggestive of varying levels of modeling behavior in subsequent YouTube videos.

Conclusions: Every month about 1 billion individuals are reported to view YouTube videos (ebizMBA Guide 2015 ebizMBA Guide. Top 15 most popular websites. 2015. Available at: http://www.ebizmba.com/articles/most-popular-websites [Google Scholar]) and young men are the heaviest users, overall streaming more YouTube videos and watching them longer than women and other age groups (Nielsen 2011 Nielsen. State of the media: the social media report. Q3. 2011. Available at: http://www.nielsen.com/us/en/insights/reports/2011/social-media-report-q3.html [Google Scholar]). This group is also the most dangerous group in traffic, engaging in more per capita violations and experiencing more per capita injuries and fatalities (e.g., Parker et al. 1995 Parker D, Reason J, Manstead ASR, Stradling SG. Driving errors, driving violations and accident involvement. Ergonomics. 1995;38:1036–1048.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Reason et al. 1990 Reason J, Manstead A, Stradling S, Baxter J, Campbell K. Errors and violations on the roads: a real distinction? Ergonomics. 1990;33:1315–1332.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Transport Canada 2015 Vingilis E, Yilderim-Yenier Z, Fischer P, et al. Self-concept as a risky driver: Mediating the relationship between racing video games and on-road driving violations in a community-based sample. Transp Res Part F Traffic Psychol Behav. 2016;43:15–23. [Google Scholar]; World Health Organization 2015 World Health Organization. Road traffic injuries. Fact sheet no. 358. 2015. Available at: http://www.who.int/mediacentre/factsheets/fs358/en/# Accessed March 14, 2016. [Google Scholar]). YouTube also contains many channels depicting risky driving videos. The time has come for the traffic safety community to begin exploring these relationships.     

晋西北地区土地沙漠化时空格局及其驱动因素

利用晋西北地区1975,1991,2000,2015,2019年遥感影像,采用转移矩阵模型、重心迁移模型和主成分分析法,研究晋西北地区土地沙漠化的时空变化及其驱动机制.结果表明：土地沙漠化面积在时间上呈现先增加、后减少的动态趋势,其中1975~1991年沙漠化土地面积增加1495.10km²,非沙漠化土地转移为轻度沙漠化土地面积最大（1506.12km²）;1991~2000年沙漠化土地面积增加689.09km²,轻度沙漠化土地转移为中度沙漠化土地面积最大（11098.72km²）;2000~2015年沙漠化土地面积减少2365.85km²,中度沙漠化土地转移为轻度沙漠化土地面积最大（10569.56km²）;2015~2019年沙漠化土地面积减少1931.39km²,轻度沙漠化土地转移为非沙漠化土地面积最大（1909.93km²）.空间上呈现先偏南、后偏西北的迁移趋势,其中1975~1991年、1991~2000年沙漠化土地重心总体上偏南方向迁移,重度沙漠化土地重心迁移距离最大,分别为19.26,20.06km;2000~2019年沙漠化土地重心向西北迁移,轻度沙漠化土地重心迁移距离最大,为30.29km.1975~2019年晋西北地区土地沙漠化动态变化是自然因素和人为因素共同作用的结果,人口数量和牲畜数量是影响沙漠化土地变化的主要人为因素,大风日数是影响沙漠化土地变化的主要自然因素.     

基于车载诊断系统的轻型乘用车实际道路油耗特征分析

近年来的一系列研究表明,轻型车实际道路油耗与型式认证油耗间的差异在不断增大,之前的实际道路油耗研究大多基于用户上报数据或车载排放测试系统(PEMS).车载诊断系统(OBD)是一种监测发动机及排放控制系统实时工作状态的仪器.本研究通过OBD解码器采集瞬时发动机进气量计算机动车瞬态油耗并通过2辆轻型汽油车的实验室台架测试证明该方法具有很好的准确性(差异在±5%以内).本研究进一步在北京选取了7辆轻型汽油车,利用OBD解码器开展实际道路油耗测试,并研究运行工况及车辆载重对其实际道路油耗的影响.实际道路结果显示测试车辆在典型工况下的油耗比型式认证油耗高23.1%~46.4%,平均差异为29.2%.通过微观运行模态方法将实际道路油耗修正到法规测试循环(NEDC工况)的交通特征下,道路油耗仍然比型式认证油耗高18.7%±7.1%.研究表明,平均速度与实际道路油耗具有很强的相关性.实际道路油耗在30 km·h-1以下时会随速度降低而显著上升.此外,研究还发现如果车辆载重增加260 kg,实际道路油耗将上升6.2%±2.2%.     

东北地区能源消费碳排放时空演变特征及其驱动机制

运用探索性空间分析、冷热点分析、标准差椭圆分析和地理加权回归分析等地学方法,研究了我国东北地区2005—2014年间能源消费碳排放的时空演化特征及其驱动机制.结果表明:①东北三省的能源消费碳排放量从高到低分别为辽宁省、黑龙江省、吉林省,2014年碳排放量分别达到20266、9914、6411万t;②东北三省能源消费碳排放量呈比较显著的全局空间正相关特征,在统计学上显著性检验存在多个冷、热点,其中,热点区主要集中在辽宁省的大连市、沈阳市、本溪市和辽阳市,吉林省的吉林市,以及黑龙江省的大庆市和鸡西市,冷点区主要集中在黑龙江省的大兴安岭市、黑河市、佳木斯市和伊春市;③在省级尺度上东北三省碳排放重心在2005—2014年间呈往东偏移的趋势,在市级尺度上辽宁省和吉林省碳排放重心往西偏移,而黑龙江省碳排放重心则往南偏移;④在省级尺度上,能源消费碳排放主轴方向为"西南-东北",但有往北旋转的趋势,黑龙江省能源消费碳排放主轴呈现往北旋转的趋势,辽宁省和吉林省的能源消费碳排放主轴方向则呈往东旋转的趋势;⑤东北地区影响能源消费碳排放量的主要驱动因素的影响程度排序为:人均GDP产业结构总人口数城市化率老龄化.     

天津市滨海新区湿地景观格局变化及驱动力分析

湿地是生态系统的重要组成部分,是人类最重要的生存环境之一。天津滨海新区拥有丰富的湿地资源,但近年来随着经济的发展,人类活动的增强及相关政策的执行,湿地资源受到严重威胁。为全面掌握滨海新区近年来的湿地景观格局动态变化历程,本文以1986年、1996年、2006年和2016年TM遥感影像为主要数据源,运用遥感等技术手段,对滨海新区近30年来湿地景观格局时空演化进行动态分析、质心分析及指数分析,并简要讨论了湿地景观变化的主要驱动因子。结果表明：近30年间滨海新区湿地退化情况严重,累计退化面积达524.762 km²,其中2006?—?2016年湿地面积减幅最大,高达407.947 km²,减少区域集中分布在环渤海地带的淤泥质沙滩和浅海水域,转化后类型以建设用地为主。景观动态分析及质心分析结果表明：景观整体变化特征为破碎度逐渐增强、景观形状日趋复杂、景观生态功能日益减弱、景观分布向均衡化发展,整体湿地质心不断向内陆推移。驱动力分析结果显示：经济发展、人口增长和政策调控等人为因素是该时期景观格局变化的主要驱动因素。     

基于灰色-马尔可夫藕合模型的区域建设用地需求预测

建设用地需求的中长期准确预测是当前土地资源规划和利用的重点和难点。本文将灰色和马尔可夫两种预测模型相结合,建立灰色-马尔可夫藕合模型,以广东省揭阳市为例,对1997到2010年间主要建设用地规模进行了需求预测,同时与其它几种常规的预测方法进行了比较。结果表明,人文驱动力相关预测法误差相对较大,其中国内生产总值(GDP)相关法准确度要优于人口相关法。趋势线法计算简单,预测准确度也相对较高,但只适于短期预测。灰色预测法精确度较高,计算需要模型的支持、专业性强。灰色-马尔可夫预测法由于是对灰色预测的偏离值进行了纠正,因而准确度最高。尽管该方法需要大量数据的支持,计算量大,但仍是值得推荐的高准确度的区域建设用地的专业预测方法。