土耳其一起交通事故分析

Yilmaz Ac, C. Ac, K. Aydın
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引用次数: 1

摘要

自1983年以来,第2918号土耳其公路交通法一直是土耳其交通事故的参考立法。尽管该法案包含了多种解释和定义,但仍存在不足,特别是在对交通事故分析至关重要的故障率的定义上。事故专家对事故的判断大多是主观臆断,没有对事故进行科学的分析,因为在法律上对故障率的定量指示不足。事故涉及的速度分析在事故调查中起着重要作用。可以定义一个更全面的参数,能量等效速度,来解释变形能量的耗散和严重程度,以及在车辆上形成的挤压量,这也提示了故障发生率。在这项研究中,从一个样本事故现场收集了可访问的数据(警察报告、刹车痕迹、变形情况、挤压深度等),并将其用作一个名为“vCrash”的事故重建软件的输入,该软件能够以2D和3D的方式模拟事故现场。利用784个参数实现了能量等效速度计算,预测误差较小。利用多层前馈神经网络和广义回归神经网络模型,以这些参数作为模型的教学数据,估计能量等效速度(碰撞前的速度,即没有滑痕的情况下的速度)。目的是利用这些神经网络方法,避免使用昂贵的仿真软件来模拟未来可能发生的事故。为了观察神经网络模型的性能,还对数据集进行了5倍交叉验证,分析了估计的标准误差(均方误差)和多个相关系数。结果表明,多层前馈神经网络模型在能量等效速度和故障率分析上均有较好的结果。基于仿真结果(能量等效速度和变形),假设一个故障率尺度,在预测模型上,假设特定介入的每一个预定的能量等效速度增量与相同介入的特定故障率增量对应,从而估计出故障率,提出科学、系统的方法,弥补行为的不足。
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Analysis of a Traffic Accident in Turkey
No.2918 Turkish Highway Traffic Act has been the reference legislation for traffic accidents in Turkey since 1983. Although this act consists of several explanations and definitions, it has still deficiencies especially in defining fault rates which are vital for traffic accident analyses. Accident experts determine fault rates mostly according to their initiatives without conducting scientific analyses on accidents due to inadequate quantitative instructions on fault rates in the act. Speed analyses of accident involvements play an important role in accident investigations. A more comprehensive parameter, Energy Equivalent Speed, may be defined to explain dissipation and severity of deformation energy and crush amounts formed on vehicles which also give hint about fault rates. In this study, accessible data were collected from a sample accident scene (police reports, skid marks, deformation situations, crush depths etc.) and used as inputs for an accident reconstruction software called “vCrash” which is able to simulate the accident scene in 2D and 3D. Energy equivalent speed calculations were achieved using 784 parameters with a prediction error. Multi-layer Feed Forward Neural Network and Generalized Regression Neural Network models were utilized for estimation of energy equivalent speeds (speeds just before the collision, i.e., in case of absence of skid marks) based on using these parameters as teaching data for the models. It was aimed that, by benefiting from these neural network methods, necessity of using expensive simulation softwares for probable accidents in future may be avoided. In order to observe performance of the neural network models, standard error of estimates (mean square error) and multiple correlation coefficients were also analyzed using 5-fold cross validation on the dataset. It was observed that, in general, Multi-layer Feed Forward Neural Network model yielded better results for both energy equivalent speed and fault rate analyses. Based on simulation results (energy equivalent speeds and deformations) and assumption of a fault rate scale, fault rates were estimated on prediction models by assuming correspondence of every predetermined increment in energy equivalent speed of specific involvement to a specific increment in fault rate of the same involvement to put forward a scientific and systematic approach and compensate deficiencies in the act.
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