Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province,China

Accurate assessment of seismic landslides hazard is a prerequisite and foundation for postdisaster relief of earthquakes.An Ms 5.7 earthquake occurring on September 7,2012,in Yiliang County,Yunnan Province,China,triggered hundreds of landslides.To explore the characteristics of coseismic landslides caused by this moderate-strong earthquake and their significance in predicting seismic landslides regionally,this study uses an artificial visual interpretation method based on a planet image with 5-m resolution to obtain the information of the coseismic landslides and establishes a coseismic landslide database containing data on 232 landslides.Nine influencing factors of landslides were selected for this study:elevation,relative elevation,slope angle,aspect,slope position,distance to river system,distance to faults,strata,and peak ground acceleration.The real probability of coseismic landslide occurrence is calculated by combining the Bayesian probability and logistic regression model.Based on the coseismic landslides,the probabilities of landslide occurrence under different peak ground acceleration are predicted using a logistic regression model.Finally,the model established in this paper is used to calculate the landslide probability of the Ludian Ms 6.5 earthquake that occurred in August 2014,78.9 km away from the macro-epicenter of the Yiliang earthquake.The probability is verified by the real coseismic landslides of this earthquake,which confirms the reliability of the method presented in this paper.This study proves that the model established according to the seismic landslides triggered by one earthquake has a good effect on the seismic landslides hazard assessment of similar magnitude,and can provide a reference for seismic landslides prediction of moderate-strong earthquakes in this region.

Modeling household car ownership using ordered logistic regression model

Considering both the discrete and ordered nature of the household car ownership an ordered logistic regression model to predict household car ownership is established by using the data of Nanjing Household Travel Survey in the year 2012. The model results show that some household characteristics such as the number of driver licenses household income and home location are significant.Yet the intersection density indicating the street patterns of home location and the dummy near the subway and the bus stop density indicating the transit accessibility of home location are insignificant.The model estimation obtains a good γ2 the goodness of fit of the model and the model validation also shows a good performance in prediction.The marginal effects of all the significant explanatory variables are calculated to quantify the odds change in the household car ownership following a one-unit change in the explanatory variables.

Public priorities and consumer preferences for selected attributes of automated vehicles

Public opinion and consumer preferences are among the various constraints on the rollout of automated cars, as they will affect the decision-making of both automotive industry actors and public-sector regulators. This study contributes to the growing body of the literature regarding this issue, through a moderate-scale survey （n = 370） that incorporated both prioritization/attitudinal questions （regarding public opinion） and a stated-prefer- ence module （to identify consumer preferences）. The sur- vey protocol includes a stated-preference approach to investigate consumers＇ preferences for the possibility of very high rates of speed in automated cars on long-distance journeys. We found separately identifiable effects for average travel speeds （manifested as journey duration） and maximum travel speed in the stated-preference scenarios. In the ＇prioritization＇ component of the survey, respondents ranked having the ＇highest possible level of safety＇ as the single most important benefit that they would like auto- mated cars to deliver, ahead of benefits such as being able to performing activities while traveling or having traffic congestion reduced. This result has consequences for the car-following distances that are programmed into the control algorithms of automated cars. Documenting this finding is important, as decisions must be made in the near future by driving-algorithm designers, public-sector regu- lators, and ultimately the judiciary regarding the guidelines for acceptable automated driving-behavior instructions.

Multinomial Logistic Regression Model for Predicting Driver＇s Drowsiness Using Only Behavioral Measures

The aim of this study was to explore the effectiveness of behavioral evaluation measures for predicting drivers＇ subjective drowsiness. Behavioral measures included neck bending angle, back pressure, foot pressure, COP （center of pressure） movement on sitting surface and tracking error in driving simulator task. Drowsy states were predicted by means of the multinomial logistic regression model where behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. First, we compared the effectiveness of two methods （correlation coefficient-based method and odds ratio-based method） for determining the order of entering behavioral measures into the prediction model. It was found that the prediction accuracy did not differ between both methods. Second, the prediction accuracy was compared among the numbers of behavioral measures. The prediction accuracy did not differ among four, five and six behavioral measures and it was concluded that entering at least four behavioral measures into the prediction model is enough to achieve higher prediction accuracy. Third, the prediction accuracy was compared between the strongly drowsy and the weakly drowsy groups. The prediction accuracy differed between the two groups and the proposed method was effective under the condition where drowsiness was induced to a larger extent.

基于改进的Tomek Link方法预测电信客户流失

为提高电信客户流失检测的准确性和效率,提出了一综合方法。首先应用XGB-RFE方法对特征进行筛选,以选择最相关的特征,其次采用基于分层交叉验证框架下的数据平衡技术,来处理不平衡数据。通过这两种方法的综合应用,旨在提高模型性能和可解释性。结果表明,基于分层交叉验证框架下的Tomek Link欠采样技术,显著提高了各个模型的性能。此外,将该方法应用于TabNet模型中,同样取得了良好的效果。这一综合方法对于电信客户流失预测具有实际应用价值,有望提高流失检测率,改善业务决策。

DATA DRIVEN MODELING FOR POWER TRANSFORMER LIFESPAN EVALUATION

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation＇s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit （RTU） system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.