Pedestrian crash estimation models for signalized intersections

The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables. The Pearson correlation coefficient was used to eliminate predictor variables that were correlated to each other. Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25 mile, 0.5 mile and 1 mile buffer widths to extract data and develop models was also evaluated.     

Modeling work zone crash frequency by quantifying measurement errors in work zone length

Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.     

Generalized nonlinear models for rear-end crash risk analysis

A generalized nonlinear model (GNM)-based approach for modeling highway rear-end crash risk is formulated using Washington State traffic safety data. Previous studies majorly focused on causal factor identification and crash risk modeling using Generalized linear Models (GLMs), such as Poisson regression, Logistic regression, etc. However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contribute factors to crashes) established via a link function can be often violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions. In this research, a GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables using the rear end accident data collected from 10 highway routes from 2002 through 2006. The results show for example that truck percentage and grade have a parabolic impact: they increase crash risks initially, but decrease them after the certain thresholds. Such non-monotonic relationships cannot be captured by regular GLMs which further demonstrate the flexibility of GNM-based approaches in the nonlinear relationship among data and providing more reasonable explanations. The superior GNM-based model interpretations help better understand the parabolic impacts of some specific contributing factors for selecting and evaluating rear-end crash safety improvement plans.     

Spatial regression analysis of traffic crashes in Seoul

Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects.The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands.     

基于内聚力模型的形状记忆合金短纤维增强树脂基复合材料的模拟分析

通过单纤维拔出实验和单轴拉伸实验, 测定了形状记忆合金(SMA)增强树脂基复合材料的界面脱粘剪切强度和单向随机分布SMA短纤维增强复合材料的拉伸强度。根据蒙特卡罗法和边界条件控制方程, 编写了适于软件调用的单向随机分布短纤维增强复合材料的APDL语言生成程序, 建立数值模拟模型。基于指数型内聚力模型, 对SMA纤维与环氧树脂基体界面分离(即界面脱粘)过程进行了有限元模拟。结果表明: 相同纤维体积分数下, 随着纤维长细比的减小, 复合材料整体弹性模量逐渐降低; 温度驱使SMA纤维弹性模量发生变化, 可以有效提高复合材料整体弹性模量。     

The application of machine learning models based on particles characteristics during coal slime flotation

In this study, four different machine learning (ML) models were used to simulate the migration behavior of minerals during coal slime flotation based on particle characteristics (shape, size, compositions, and types): random forest (RF), logistic regression (LR), AdaBoosting (Ada), and k-nearest neighbors (KNN). For ML model development, 70% of the total data was used for the training phase, and 30% was used for the testing phase. F-score and area under the curve (AUC) were used as the most vital indicators for evaluating the different ML models. Compared to the other ML models, the RF model had the best accuracy for simulating particle migration behavior during flotation. Furthermore, the RF model avoided the drawback of having to be retrained when the feed conditions changed. The results revealed that particle size and particle composition play the most significant role in coal slime flotation.     

基于数论网格法与Morris法的翼型气动特性敏感性分析

针对在风力机翼型气动特性敏感性分析中计算效率较低的问题，提出了一种基于数论网格（number theoretic net，NT-net）法与Morris法的翼型气动特性敏感性分析方法。首先，构建了拟合精度较高的翼型参数化模型；其次，阐述了NT-net法的计算原理，采用NT-net法对翼型参数化模型的多项式系数进行抽样；然后，以风力机翼型S832为研究对象，采用Morris法进行翼型气动特性的全局敏感性分析；最后，进一步分析在特定工况下风力机翼型参数化模型的多项式系数对翼型外形及气动特性的影响。结果表明：影响翼型气动特性的主要因素依次为翼型的最大相对厚度和最大相对弯度、前缘半径和后缘厚度；当来流攻角较小时，最大相对厚度和最大相对弯度取适当的较小值、前缘半径取适当的较大值可有效增强该工况下风力机翼型的气动特性，同时也验证了NT-net法的计算效率更高。研究结果为风力机翼型气动设计提供了理论参考。     

Numerical fatigue life analysis of a high frequency mechanical impact treated industrial component based on damage mechanics models

After post weld treatment with high frequency mechanical impact (HFMI) treatment of welds, a significant increase of fatigue life (up to a factor of 10) can be achieved. During the last years numerous experimental tests of welded joints with simple geometry under constant amplitude loading have been performed to quantify the positive effect of high frequency mechanical impact treatment. Due to the lack of methods for the prediction of the high frequency mechanical impact benefits, a widespread use of this process is not the case yet. Furthermore, it is still not clear if the results of these fatigue tests can be transferred to complex geometries and complex loading conditions such as in industrial applications. Therefore, an approach to assess the fatigue life of complex welded structures under variable amplitude loading was developed. For this purpose, high frequency mechanical impact treatment and fatigue load of simple welded specimen made of S690QL steel were simulated with finite element analysis (FEA) firstly. Then, the needed damage parameters for the fatigue life correlation were evaluated from the finite element post‐processing. The calculated life time to crack initiation was in good agreement with the experimental fatigue test results. In the next step, this procedure was implemented on a welded arm of an evacuator of type EW180B of the company Volvo Construction Equipment made of S700MC. The variable amplitude load measured under real service condition was transferred to single constant amplitude load cycles using a rainflow‐counting algorithm. By simulation and damage mechanics evaluation of each load cycle the total damage sum could be calculated and compared with the experimental results from Volvo Construction Equipment.