Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.     

Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

2016年11-12月国内生产安全事故统计分析

统计了2016年11-12月国内发生的各种生产安全事故135起,其中包括交通事故、矿业事故、爆炸事故、火灾、毒物泄漏与中毒和其他事故.统计表明,在135起事故中,交通事故占56.30％,矿业事故占8.15％,爆炸事故占7.41％,火灾占7.41％,毒物泄漏与中毒占3.70％,其他事故占17.04％.135起事故共死亡496人.死亡人数的百分比分别为交通事故50.60％,矿业事故13.51％,爆炸事故4.64％,火灾3.43％,毒物泄漏与中毒2.62％,其他事故25.20％.2016年11-12月生产安全事故发生较多的5个省分别为山东(24起)、云南(11起)、广东(10起)、四川(8起)和湖南(8起);死亡人数较多的5个省、自治区分别为江西(90人)、山东(49人)、云南(49人)、内蒙古(37人)和湖北(32人).     

空气质量数值预报优化方法研究

基于源清单“Nudging”修正方法和XGBoost算法对徐州市2016年12月13个监测站点的PM_2．5、PM₁₀、O₃、SO₂、NO₂、CO等6种污染物浓度预报值进行修正,并分析了修正前后模式预报改善效果.在源清单“Nudging”修正部分,本文结合IDW空间插值算法对SO₂、NO₂、CO等3种污染物浓度预报值进行修正,与修正前后模拟结果相比,采用同化源模拟的预报浓度值与观测值的相关系数提高了0.06~0.27不等,平均绝对误差和均方根误差减少的幅度较为明显,平均相对偏差（MFB）和平均相对误差（MFE）均在理想水平范围内,NO₂修正效果最好,其次是SO₂和CO.基于XGBoost算法的统计修正部分,本文结合WRF气象预报要素建立统计回归模型,对6种污染物进行统计修正,经滚动修正之后,预报偏低或偏高现象得到很大的改善,除了SO₂之外,相关系数均提高到0.6~0.7左右,各项误差统计指标改进幅度非常明显.总体而言,本文采用的两种修正方法对中小尺度空气质量数值预报改进效果非常明显,反映了此优化方案的可行性和科学性.     

变温环境下结构高频瞬态能量响应预示

目的解决变温环境下结构的高频瞬态能量响应预示问题。方法基于统计能量分析方法,通过考虑时变参数对结构能量的影响和热效应引起的材料力学性能变化和热应力对统计能量分析参数的影响,提出了一种变温环境下结构高频瞬态能量响应预示方法。以双振子模型为例,通过与Newmark-beta法的对比验证数值方法的准确性。结果该能够较好地捕捉结构高频能量响应的周期性变化规律,其峰值时间和峰值能量与精确数值解基本吻合。以L型折板系统为例,研究了20～300℃变温环境下结构的瞬态能量响应变化规律,子系统1和子系统2之间的能量交换远小于两子系统中因阻尼引起的能量消耗,子系统2瞬态能量响应的峰值时间为4.3ms、峰值能量为0.09J。结论该方法具有较高的计算精度和计算效率,能够较好地适用于结构高频瞬态能量响应预示。     

Integrated Use of GLEAMS and GIS to Prevent Groundwater Pollution Caused by Agricultural Disposal of Animal Waste

/ In modern intensive animal farming the disposal of a large amount of waste is of great concern, as, if not properly performed, it can cause the pollution of water, mainly because of the high content of nitrate and phosphate. This paper presents the results of a study intended to assess the environmental sustainability of animal waste disposal on agricultural soils in the alluvial plain of the River Chiana (Tuscany, Italy), a particularly sensitive area because of the high vulnerability of the shallow aquifer and of the intensive agricultural and breeding activities. With this aim, a strategy has been employed, that consists of the integrated use of a management model and GISs. The consequences on groundwater of applying animal waste to different kind of soils and crop arrangements have been simulated by means of the management model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems, ver 2.01). As the huge amount of data required by such a sophisticated model does not allow applications at a scale larger than the field size, IDRISI and GRASS GIS packages have been used to divide the study area into land units, with homogeneous environmental characteristics, and then to generalize on these units the outputs of the model. The main conclusions can be synthesized as follows: The amount of animal waste produced in some of the investigated areas (i.e., municipal territory) is greater than that disposable on their own agricultural soil with no risks to the groundwater; consequently a cooperative approach among municipalities is necessary in order to plan waste disposal in a comprehensive and centralized way.KEY WORDS: Land use; Animal waste disposal; Groundwater protection; GIS, Management models     

SELECTED CHALLENGES IN RUNOFF GENERATION RESEARCH IN FORESTS FROM THE HILLSLOPE TO HEADWATER DRAINAGE BASIN SCALE1

ABSTRACT: When faced with practical forest land management issues such as the impacts of logging or forest conversion to other land uses, planners ideally require a comprehensive understanding of within drainage basin hydrological processes to determine the most vulnerable areas to increased storm runoff and erosion. Land managers in particular need to know the source areas and magnitude of inputs to the storm hydrograph, in terms of water quantity, sediment and solute transport; and the routing of such hydrographs from headwater to larger drainage basins. The latter includes an overall assessment at various scales of the impacts of forest disturbance and conversion on the water balance. This paper will focus on runoff generation in terms of identifying the various pathways and source areas. Such aspects will be linked with the need for a more comprehensive effort towards the field testing of so-called ‘physically based’ models of runoff generation. Some of the controversial issues arising from the difficulties in reconciling results from hydrochemical investigations with complementary hydrometric studies will be highlighted. Subsequently, attention will be given to topographic-wetness models, which have promising applications in forestland management. In addition, alternative simple models for application at the catchment scale will be assessed. The latter is in recognition that at smaller scales, heterogeneity both in time and space of soil hydraulic properties demand a greater number of parameters in modelling. Such considerations can even prove an obstacle in terms of the confident application of ‘physically based’ models.     

A SIMULATION OF RIVER-BASIN RESPONSE TO MESOSCALE METEOROLOGICAL FORCING: THE SUSQUEHANNA RIVER BASIN EXPERIMENT (SRBEX)1

ABSTRACT: A mesoscale meteorological model, a surface hydrology model, and a ground-water hydrology model are linked to simulate the hydrographic response of a large river basin to a single storm. Synoptic climatology is employed to choose a representative hydro-climatic event. The mesoscale meteorological model uses three nested domains to simulate relatively high-resolution precipitation over a sub-basin of the Susquehanna River Basin. The hydrology models simulate surface runoff and ground-water baseflow using both analyzed and simulated precipitation. The hydrologic abstractions are handled using both Curve Number and Green-Ampt routines. To support the linkage of the numerical models, special attention is given to data resampling and reprojection. The mesoscale meteorological model simulation captures the spatial and temporal structure of the storm event, while the hydrology models represent the timing of the event well. The Curve Number method generates a realistic hydrograph with both analyzed and simulated precipitation. In contrast, the hydrographic response generated by the Green-Ampt routine is inferior. Several interrelated factors contribute to these results, including: the nature of the precipitation event chosen for the experiment; the tendency of the mesoscale meteorological model to underpredict low intensity, widespread precipitation in this case; and the influence of the surface soil-texture characteristics on infiltration rates.     

CRITICAL DATA REQUIREMENTS FOR PREDICTION OF EROSION AND SEDIMENTATION IN MOUNTAIN DRAINAGE BASINS1

ABSTRACT: The potential for understanding and, where necessary, managing sedimentation in humid mountain drainage basins increases with awareness of the conditions that lead to shallow landsliding, debris flows, and catastrophic sedimentation in stream channels. Progress in understanding has involved: improved recognition of source areas and the potential for downstream effects of slope failure; improved understanding of hydrological conditions required for failure; and a general theory of slope stability in shallow colluvium, including the role of plants, fires, timber harvest, and other disturbances. The theory acknowledges spatial variability in topographic and geotechnical terrain characteristics, the stochastic nature of climatic triggering events such as forest fires and rainstorms, and the integrating nature of channel networks in modulating the cumulative effects of transient processes within a basin. Anthropogenic fire regimes, road effects, and timber harvest can readily be included. Continued application and modification of the theory over an expanded geographical range require improvements in field data and their systematic storage in spatial databases. Improvements in digital topographic data for mountain basins, systematic network-wide surveys of channel conditions, and new technology for rapid documentation of soil depths in landslide source areas would enhance the prediction of mass failure, its consequences for channel habitat, and the basin-wide or regional distribution of hillslope and channel conditions. Computations of the probabilities of transient effects throughout basins could then form the basis of ecological risk analyses. Large-scale spatial data sets of a few critical variables are required before this next level of understanding can be developed and applied to sedimentation impacts on ecosystems and other resources.     

A Multimedia Chemical Fate Model with Time-Dependent Air-Water Transfer Rates

A multimedia model for the temporal evolution of the concentration of chemical species in a water basin and its bottom sediment layer has been tested with time-dependent air-water transfer rates varying with the meteorological conditions over the basin. The multimedia model uses the chemical fugacity approach in a system of two ordinary differential equations for the chemical species partition in the two mentioned media, with transfer through the sediment-water and the air-water interfaces. The air-water transfer rates are estimated by a micrometeorological preprocessor. A scenario in which known concentrations in air of a soluble pollutant (benzene) induce water and sediment pollution is tested on both synthetic (MonteCarlo-simulated) and real series of meteorological data. It is found that the use of time-dependent transfer coefficients affects not only the relaxation time, but also the long term concentration of the pollutant in water and sediment, that differs between 10 and 40% in the examined cases when compared with the same multimedia model using average constant transfer rates as usual. This is shown to be due to the statistical correlations between meteorological parameters and air pollutant concentrations, which stresses the advantages of a time-dependent estimation of the transfer coefficients. Correction terms are proposed to take into account the correlation effects when a constant parameters multimedia model is used.