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.     

温岭市东环高架交通对声环境的影响评价与预测

相较于一般的城市道路,城市高架具有通行能力大、行车速度高、车辆行驶状态复杂的特点,噪声污染极为突出.温岭市东环高架穿越温岭市区,是进出市区唯一的快速通道.以温岭市东环高架道路为例,对其进行实地监测从而了解其声环境,同时运用BREEZE NOISE软件模拟其噪声污染状况.根据监测模拟结果,从合理进行道路规划、装设声屏障和铺设低噪声路到提出高架复合道路噪声污染控制的对策建议.     

基于GM(1,1)模型群的阿什河氨氮浓度的预测研究

依据灰色系统理论,以2000年-2009年10年阿什河入江口断面枯水期氨氮浓度构造了一个由6个GM(1,1)模型组成的灰色动态模型群,并运用该模型群对其变化趋势进行了预测分析,得到令人满意的结果。研究表明,灰色动态模型群法能够充分利用近期水质资料信息预测未来水质变化趋势;以模型群统计平均值作为最终预测值,避免了单一灰色模型容易利用不稳定信息的缺陷,使得预测精度更加准确,预测结果更为可信。     

震前小震调制比空间异常消失的映震能力研究

利用小震调制比方法的时空异常进行跟踪分析,通过对比发现,近几年震前小震调制比的空间异常变化与有感地震的发生具有一定的对应关系.进一步的分析研究认为,利用震前小震调制比的空间异常消失可进行有感地震甚至中强地震的地点预测.     

天然水体中铝浓度的预测方法探讨

本文概括了天然水体中铝的形态分类和地球化学行为,综述了天然水体中Ａｌ＾３＋和有机铝的浓度的预测方法,提出了由矿物浓度积,无机铝和有机铝经验关系式,有机铝模型辅以配体浓度和热力学常数预测天然水体中各形态铝的浓度的方法。     

春节与疫情管控期间珠三角VOCs的组成和来源变化

挥发性有机物(VOCs)是对流层中臭氧(O3)生成过程的关键前体物,是O3与PM2.5协同治理的重要管控对象.基于2020年1月1日～2月29日珠江三角洲(珠三角)4个站点的在线VOCs观测数据,分析了春节与疫情管控期间珠三角地区VOCs组分、活性、臭氧生成潜势和来源的变化.结果表明,春节与疫情管控期间珠三角地区φ(V...     

“R1-R4” AND “BOISED” SEDIMENT PREDICTION MODEL TESTS USING FOREST ROADS IN GRANITICS1

ABSTRACT: Erosion and sedimentation data from research watersheds in the Silver Creek Study Area in central Idaho were used to test the prediction of logging road erosion using the R1-R4 sediment yield model, and sediment delivery using the “BOISED” sediment yield prediction model. Three small watersheds were instrumented and monitored such that erosion from newly constructed roads and sediment delivery to the mouths of the watersheds could be measured for four years following road construction. The errors for annual surface erosion predictions for the two standard road tests ranged from +31.2 t/ha/yr (+15 percent) to -30.3 t/ha/yr (-63 percent) with an average of zero t/ha/yr and a standard deviation of the differences of 18.7 t/ha/yr. The annual prediction errors for the three watershed scale tests had a greater range from -40.8 t/ha/yr (-70 percent) to +65.3 t/ha/yr (+38 percent) with a mean of -1.9 t/ha/yr and a standard deviation of the differences of 25.2 t/ha/yr. Sediment yields predicted by BOISED (watershed scale tests) were consistently greater (average of 2.5 times) than measured sediment yields. Hillslope sediment delivery coefficients in BOISED appear to be overly conservative to account for average site conditions and road locations, and thus over-predict sediment delivery. Mass erosion predictions from BOISED appear to predict volume well (465 tonnes actual versus 710 tonnes predicted, or a 35 percent difference) over 15 to 20 years, however mass wasting is more episodic than the model predicts.     

沙尘天气对北京市空气质量的影响及其预测预报

概述了北京市沙尘天气的分类、特征、判别方法,总结了2000—2002年沙尘天气对北京市空气质量的影响情况,提出了沙尘天气影响空气质量的短期(1～2d)预报方法。利用该方法预报了2002年4次主要沙尘天气过程,预报结果都比较准确。研究发现沙尘天气年际变化主要是受气候条件年际变化影响,提出根据气候条件年际变化预测来进行沙尘天气年际变化预测的初步方法。      

混沌理论在水质预测中的应用初探

基于混沌理论、相空间重构思想和嵌入理论分析涟水流域溶解氧的时间序列,计算吸引子维数和最大的Lyapunov指数,研究河流水质系统的复杂性特征。结果表明,河流水质时间序列具有混沌特性,其外在表现为貌似随机的无规则特点;但系统本身内在的、固有的规律(表现在最大的Lyapunov指数),表明短期预测河流水质变化是可行的,并应用混沌相空间模线性回归模型进行了短期预测,其预测结果对涟水流域的水质管理和控制具有一定的参考价值。     

基于可靠度的变压器声功率遗传算法研究

换流变压器是高压直流换流站中主要的噪声源,对换流变压器噪声的预测精度直接影响到后期整个换流站噪声预测模型的准确性,因此,准确获得换流变压器的声功率至关重要。根据《声学 户外声传播衰减 第2部分:一般计算方法》(ISO 9613-2:1996)中的户外声传播声功率计算方法,构建了声源声功率反推数学模型。利用某变压器周围大量测点现场实测数据,结合可靠度理论,识别并剔除了实测点中受相干声场影响较大的点,实现了换流变压器声功率反演计算,并对测点个数的选取进行了初步研究及优化。与其他声功率测量方法相比,该方法操作简单,对仪器设备要求较低,适用于复杂的声学环境,具有一定的应用前景。