A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

Generality of Models that Predict the Distribution of Species: Conservation Activity and Reduction of Model Transferability for a Threatened Bustard

Abstract:  Predictive models can help clarify the distribution of poorly known species but should display strong transferability when applied to independent data. Nevertheless, model transferability for threatened tropical species is poorly studied. We built models predicting the incidence of the critically endangered Bengal Florican ( Houbaropsis bengalensis ) within the Tonle Sap (TLS) floodplain, Cambodia. Separate models were constructed with soil, land-use, and landscape data and species incidence sampled over the entire floodplain (12,000 km²) and from the Kompong Thom (KT) province (4000 km²). In each case, the probability of Bengal Florican presence within randomly selected 1 × 1 km squares was modeled by binary logistic regression with multimodel inference. We assessed the transferability of the KT model by comparing predictions with observed incidence elsewhere in the floodplain. In terms of standard model-validation statistics, the KT model showed good spatial transferability. Nevertheless, it overpredicted florican presence outside the KT calibration region, classifying 491 km² as suitable habitat compared with 237 km² predicted as suitable by the TLS model. This resulted from higher species incidence within the calibration region, probably owing to a program of conservation education and enforcement that has reduced persecution there. Because both research and conservation activity frequently focus on areas with higher density, such effects could be widespread, reducing transferability of predictive distribution models.     

南北极大气气溶胶单颗粒成分特点研究

运用定量电子探针微区分析技术(EPMA)分别测定了采自北极新奥尔松地区(78?55′N、11?56′E)和南极乔治王岛(62?13′S、58?47′W)极昼天气下PM10大气颗粒物样品.结果表明,北极和南极大气颗粒物化学成分存在很大差异,表现出各自不同的特点.北极的颗粒类型中,“反应的海盐”和“矿物尘”分别占颗粒总数的44%和27%,“新鲜海盐”所占的比例不到10%,“反应的海盐”中以含硝酸盐的颗粒为主,反映了外来物质或人为污染对该地气溶胶影响较大;南极的颗粒类型中,“新鲜海盐”占总数的74%左右,“反应的海盐”占19%,反应的海盐全部含硫酸盐、未发现含硝酸盐的颗粒,推测与海盐反应的含硫物质来源于海洋浮游生物代谢过程产生的二甲硫醚(DMS)及其降解产物,而与人为污染无关.     

畜禽源氨气排放因子估算方法研究

畜禽养殖过程中产生的氨气(NH_3)在人为源NH_3排放中所占的比重较大,对二次气溶胶的形成起重要作用.由于我国畜禽饲养品种多样,各地气候条件和饲养规模存在较大差异,且影响NH_3排放的因素复杂多变,因此,获得准确的畜禽NH_3排放因子较为困难.本文以山西省太原市及其周边地区典型猪场、鸡场、牛场、羊场为实验场所,以杜洛克猪与长白猪的杂交猪、海兰褐蛋鸡、白羽肉鸡、杜泊羊与晋中绵羊的杂交羊、中国黑白花奶牛、金色阿奎丹与西门塔尔杂交肉牛为研究对象,通过3种方法估算它们的NH_3排放因子.结果表明:基于日粮成分和氮代谢沉积率的粗蛋白估算法过程简单、计算便捷、适合所有种类的畜禽,但未考虑NH_3挥发过程中的吸收转化及施肥利用,因而误差较大;基于排泄物含氮率和NH_3挥发率的物质流法涉及粪肥管理的不同阶段,全面考虑了NH_3产生、损失的全过程,与实际情况接近,但参数较多、周期较长、计算过程复杂;基于畜禽舍内NH_3浓度的直接测量法数据来源可靠、参数少,但只能用于相对封闭的环境,不适合牛场和羊场等敞开或半敞开的圈舍,且该方法没有考虑粪尿离开畜禽舍以后的储藏、还田过程,计算结果偏小.通过3种方法的互相印证和比对,猪、奶牛、肉牛、羊、蛋鸡、肉鸡6种实验动物的NH_3排放因子分别为6.90、36.53、22.99、3.94、0.54、0.46 kg·头(只)~(-1)·a~(-1).本研究可以为我国畜禽源NH_3排放因子的本地化测定提供一定的参考,并为准确估算畜禽NH_3排放量、做好农业源NH_3排放清单编制和污染防治工作提供参考.