基于多元线性回归的煤矿驾驶员情绪预测模型*

为研究煤矿人车驾驶员情绪状态不良引发的煤矿驾驶事故问题,设计眼动实验并建立基于多元线性回归的驾驶员情绪状况预测模型对驾驶员情绪状况进行预测;通过眼动仪采集煤矿驾驶员良好情绪与不良情绪状态下的各项眼动指标,记录其主观情绪状态;使用多元线性回归方程对数据进行分析与建模,采用平均相对误差对预测模型进行评估。结果表明:每秒注视点个数、平均扫视速度、反应时间、危险源辨识个数在情绪变化前后存在显著差异,且与情绪状态呈较强相关;基于多元线性回归的驾驶员情绪状况预测模型预测精度较高,平均相对误差为8.16%。模型适用于煤矿人车驾驶员的情绪监测,可为煤矿驾驶员安全行驶提供保障。     

基于遥感数据的京津冀地区PM2.5时空分布特征

基于MODIS AOD遥感数据,采用多元线性回归模型对PM2.5地面监测数据进行模拟估算,同时加入降水量、相对湿度等气象因子以提高模型精度,结合GIS空间分析技术,得到2015—2016年京津冀地区空间连续的PM2.5浓度分布。结果表明:利用多元线性回归模型反演PM2.5浓度效果较好,R 2均在0.59~0.84之间。在时间上,京津冀地区PM2.5浓度呈现出夏季最低、秋季稍高、冬春两季最高的变化趋势;在空间上,2015年和2016年京津冀地区PM2.5浓度有明显的区域差异,均呈现出西北低、东南高的分布格局,大致与燕山山脉和太行山脉走向一致。     

西宁冬季PM2.5和PM10手工与自动监测现状研究

为研究PM_(2.5)和PM_(10)手工与自动监测仪器在高海拔地区的适用性,于2014年12月至2015年1月(冬季)在青海省西宁市开展了为期34 d的监测比对实验。PM监测数据表明:手工监测数据之间都有差异,除了受监测滤膜种类的影响,还存在监测仪器间的系统误差。石英滤膜的PM监测数据都高于聚丙烯滤膜,尤其是PM_(2.5)更为明显,偏高近1/4;石英滤膜与聚丙烯滤膜的PM监测数据具有较好的相关性,PM_(10)监测数据的相关系数为0.97。自动监测数据之间进行了同期比对研究,发现TEOM1405DF(微振荡天平法)和APM-2(β射线法)的PM监测值较低,BAM-1020(β射线法)的PM监测值最高;而Grimm(光散射法)的PM监测值居中。BAM1020配备动态加热系统(DHS),其PM监测数据比没有配备DHS的APM-2偏高40%。基于PM监测比对研究,建议在空气污染严重时加密对各监测仪器的运行维护,并加强长期观测以全面评估PM监测。     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.     

10种事故致因模型的对比研究

为了给事故致因模型的研究提供理论支持,为日常管理中事故致因模型的选用提供参考依据,从事故的影响对象、模型的组成和事故发生的路径3个维度对10种事故致因模型进行了对比分析。研究结果表明:事故致因模型对事故影响对象的涵盖越来越全面;事故致因模型的组成日趋全面,且趋于对模型中各模块给出清晰定义以能保证分析结果的准确性;故发生的路径描述由链式向系统网状发展;根据充分性和效率的需求对模块化模型和非模块化的模型进行选择。     

浙江省碳排放时空格局及影响因素研究

利用2005~2010年浙江省各县市区的社会经济数据和能耗数据,测算了各县市区的碳排放总量、人均碳排放量和地均碳排放量,采用空间自相关方法揭示了县域尺度下相邻县市区碳排放指标的空间关联性,运用地理加权回归模型定量描述了各县市区碳排放总量影响因素的空间异质性。研究结果表明:(1)研究期内,浙江省的碳排放总量从30 486万t增加到49 559万t。人均碳排放量和地均碳排放量也均呈增长趋势,且空间差异显著。(2)各县市区碳排放总量、人均碳排放量和地均碳排放量均存在较强的空间自相关性,总体上浙北浙东呈高高集聚,浙西南呈低低集聚。(3)人口和投资是影响碳排放总量的重要因素,且存在空间异质性。投资对浙西南碳排放的影响逐渐增大。     

基于地理加权回归克里金的降水数据融合及其在水文预报中的应用

地理加权回归克里金(GWRK)是在地理加权回归(GWR)基础上扩展得到的一种既能考虑回归关系的空间非平稳性又能考虑回归变量空间自相关性的降水数据融合方法。以赣江流域为例,在评价TRMM卫星数据精度的基础上,分别以GWRK和GWR方法构建了站点-卫星降水数据融合模型,然后采用降水融合数据驱动GR4J水文模型进行水文预报。根据站点尺度降水融合数据精度及水文预报表现,对GWRK和GWR构建的降水融合模型效果进行评价,结果表明:较之GWR方法,GWRK方法能较明显的提高降水融合数据在站点尺度上的精度,但是由于输入到水文模型中的数据为面降水数据,受空间均化的影响,对水文预报精度的提高不如对站点尺度降水融合数据精度的提高明显。     

基于Delaunay三角网和地理加权回归的降水空间异常探测方法研究

针对目前已有的降水资料质量控制方法适应性差,降水数据难以模拟局地降水规律的问题。以湖南省为实验区域,利用1985~2014年夏季降水观测数据,定义局部地理空间内降水量发生显著偏离的气象站点为异常,发展了一种结合Delaunay三角网和地理加权回归的降水空间异常探测方法。该方法通过构建不规则三角网来获取各个区域之间稳定合理的地理邻近关系,根据空间邻近关系获取自适应回归带宽,并纳入地理加权回归模型进行计算,最后根据回归残差项的统计分布性质开展空间异常度量与分析。实验表明该方法能有效检测出降水空间异常,可应用于降水数据二次处理时的质量控制阶段,有助于进一步探索局部地理空间内的降水规律。     

Regression models to estimate total concentrations of polybrominated diphenyl ethers: data from NHANES 2003–2004

The objective of this study was to develop regression models to estimate the total concentration of polybrominated diphenyl ethers in serum based on the known concentrations of a limited number of congeners. Because of the possible adverse health effects associated with the exposure to polybrominated diphenyl ethers, it is of interest to know their total concentrations. Data from the National Health and Nutrition Examination Survey for 2003–2004 (N = 1859) were used to develop regression models to estimate both wet weight and lipid-adjusted total concentrations. Only the knowledge of three congeners, namely, 2,2′,4,4′-tetrabromodiphenyl ether, 2,2′,4,4′,5-pentadibromodiphenyl ether, and 2,2′,4,4′,5,5′-hexabromodiphenyl ether was required to use these models. Other than the concentrations of these three congeners, age, gender, and smoking status were the only information needed to use these models. Optionally, models were developed that could also use the race/ethnicity of the participants. All models explained more than 98% of the known variability in the observed total concentration levels. Over 98% of the model generated, predicted values were found to be within 5% of the observed values.