利用对数回归和人工神经网络对土耳其道路交通事故死亡人数预测模型进行改进

Ö. F. Cansiz
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引用次数: 0

摘要

土耳其高速公路上发生的交通事故造成了物质和精神上的损害。为了减少灾害,建立了预测模型。本研究使用1970年至2007年的人口和交通数据。这些数据由因变量和自变量组成。因变量为死亡人数(ND)。自变量包括人口(P),车辆登记数量(VN),车辆-公里(VK),驾驶员数量(DN)。利用人工神经网络(ANN)和Smeed增强的对数回归(LR)建立模型。采用实数对数建立的PVNVKDN模型是LR技术中性能最好的模型。利用历史数据集建立的VKDN是人工神经网络技术中最好的模型。对于随机选择数据创建的模型,最好的模型是VKDN。当比较最佳模型的性能时,VKDN因其错误率最低而成为最佳模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
By Using Logarithmic Regression and Artificial Neural Network to Improve Prediction Model of Dead Number Resulted from Road Traffic Accidents in Turkey
Traffic accidents occurred on highway in Turkey cause materially and morally damage. To decrease the damage, prediction model developed. In this study, demographic and traffic data which from 1970 to 2007 are used. These data are consist of dependent and independent variables. Dependent variable is formed Number of Dead (ND). As for independent variables are comprised Population (P), Registered Number of Vehicle (VN), Vehicle-km (VK), Number of Drivers (DN). Models are developed using Artificial Neural Network (ANN) and Logarithmic Regression (LR) enhanced by Smeed. PVNVKDN model developed taking real values logarithm is the best performance of models in LR technique. VKDN created by using historical data sets is the best model in ANN technique. As for models created by randomly selected data, the best model is VKDN. When performances of best models are compared, VKDN is the best model because of lowest error rate.
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