Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

Driver's obesity and road crash risks in the United States

Objective: We assessed obesity trends in U.S. drivers involved in fatal crashes since 1999 and distinguished whether crash risk factors were different between obese and nonobese drivers.

Methods: We included only drivers of passenger cars involved in fatal traffic crashes between January 1, 1999, and December 31, 2012. Obesity was classified according to the World Health Organization guidelines and profiled between 1999 and 2012 using the adjusted prevalence ratio (aPR) from log-binomial regression models. Differences in crash risks (e.g., driver's fatality, drunk driving, seat belt nonuse) between obese and nonobese drivers were estimated as adjusted odds ratios (aORs) using logistic regression models.

Results: A total of 753,024 U.S. drivers were involved in fatal crashes, for which obesity information was available for 534,887. About 56% (n = 299,078) were driving passenger cars. The prevalence of class I obesity increased from 10% in 1999 to 14% in 2012 (aPR = 1.50, 95% confidence interval [CI], 1.42–1.58), class II obesity from 3 to 5% (aPR = 2.22, 95% CI, 2.05–3.01), and class III obesity from 1 to 2% (aPR = 2.65; 95% CI, 2.27–3.10). Compared to nonobese controls, obese drivers had significantly higher risks for fatality (1.10 ≤ aOR ≤ 1.47), seat belt nonuse (1.00 ≤ aOR ≤ 1.21), need for extrication (1.01 ≤ aOR ≤ 1.23), and ambulance transport time ≥30 min (1.01 ≤ aOR ≤ 1.28). Compared to nonobese controls, obese drivers were less likely to drink drive (0.41 ≤ aOR ≤ 0.72) or speed >65 mph (0.78 ≤ aOR ≤ 0.93).

Conclusion: The rising national prevalence of obesity extends to U.S. drivers involved in fatal crashes and indicates the need to improve seat belt use, vehicle design, and postcrash care for this vulnerable population.     

南水北调东线（江苏段）沿线污染物总量达标研究

基于江苏省2010年污染源普查资料和统计年鉴,选取化学需氧量（ COD）和氨氮（ NH4－N）为研究因子,统计南水北调东线江苏段污染物排放量并计算入输水干线污染物量;根据南水北调办治污工程规划,计算污染物削减量;根据2012年江苏省水环境功能区划,核定研究区域限排总量;结果污染物量COD、氨氮削减量分别为33114吨、2935吨,污染源结构中城镇生活源比例从23．34％降到9．10％,工业企业源比例从15．13％降到8．96％,污染源点源呈下降趋势,面源呈增长趋势,污染物入输水干线总量达到2015年限排总量要求。     

北京大气中 NH3的分布和变化情况研究

PM2．5现在是中国大气中的主要污染物,而NH3作为前驱物,越来越受到大家的重视,在以往NH3监测中,往往采取手工采样和化学分析的方法进行监测,难以长时间不间断获得NH3的实时浓度。而选取利用Thermo公司的17i仪器则可以做到实时监测NH3的变化,继而分别对北京市区、农村和交通站周边的NH3进行了连续监测,发现市区浓度高于郊区,夏天浓度高于冬天,交通源对NH3排放有较大影响。     

某皮革厂皮革废水处理工程案例分析

皮革废水属于高浓度难降解有机工业废水,具有排放水量大、水质水量不均、污染物成分复杂等特点,给皮革废水处理带来极大的难度。以河北省辛集市某皮革厂废水处理工程为例,简要描述了该皮革厂皮革的生产工序、废水水量及水质特征,重点论述了皮革废水的处理方法、投资费用、运行费用及运行效果,并对该工艺所采用的理念、方法及效果进行了探讨。     

徐州市尾水导流信息化监测平台的设计与应用研究

南水北调徐州市截污导流工程是保障江苏省南水北调出省水质达到Ⅲ类水标准的关键性工程.徐州市尾水导流信息化监测平台,基于地理信息系统,集数据采集与传输、实时监测、历史数据分析、视频监控、预警预报于一体,对工程现场的各种类型的监测数据和工况信息进行采集、分析和展示应用,是南水北调徐州市截污导流工程实现全面信息化管理的重要技术支撑,为东线水资源安全提供有力保障.     

高速公路环境影响评价指标及标准探讨

为了定量进行环境影响评价,对评价指标给出分级评价标准。建立了分层评价指标体系,一层指标选择社会环境、生态环境、环境空气和环境噪声四大方面:二层指标共有13个。根据相关规范以及研究成果对部分评价指标的分级标准进行换算计算,其他指标根据调研的广东省3条高速公路(开阳高速、粤赣高速、京珠高速粤北段)进行评价指标的样本量计算。最终得到高速公路环境影响评价分级标准,以广东省潮惠高速公路作为实例得到了应用。     

锦州市交通污染现状与趋势分析

通过对近10年数据的统计分析,掌握锦州市交通污染现状,并对今后的污染趋势进行了分析.     

江苏省声环境质量二十年演变规律及污染防治措施

综合分析1991年至2010年声环境质量二十年演变规律,得出环境噪声在污染治理方面所采取和措施     

锦州市道路交通噪声污染状况及防治对策

在对锦州市道路交通噪声10多年来监测数据统计的基础上,从分析所在城市交通噪声的污染现状入手,讨论其污染成因,并提出控制与削减城市交通噪声污染的对策和建议。