论文部分内容阅读
为改善道路交通安全现状,针对道路交通的总体运行态势,建立道路交通安全的诊断指标体系,构建道路交通安全态势感知的组合权重诊断模型和神经网络诊断模型。其中,组合权重诊断模型利用AHP法和熵权法分别确定诊断指标的主客观权重,并经过融合得到组合权重,进而诊断道路交通安全等级;神经网络诊断模型利用人工神经网络智能算法对已有的样本诊断指标数据进行记忆学习,计算输入待诊断的指标数据即可得到道路交通安全等级。将这2个模型应用于南京市江宁区实例。结果表明,用这2个模型计算得到江宁区道路交通安全等级均为良好,诊断结果具有一致性。经过对比分析,这2个模型均具有较强的应用性,但是当诊断目的不同时,其适用性有差异。
In order to improve the status quo of road traffic safety, aiming at the general operation situation of road traffic, a diagnostic index system of road traffic safety is established, and a combined weight diagnosis model and neural network diagnosis model of road traffic safety situation awareness are constructed. Among them, the combined weight diagnostic model uses the AHP and entropy method to determine the subjective and objective weights of the diagnostic indicators, respectively, and obtains the combined weights through the fusion to further diagnose the road traffic safety level. The neural network diagnosis model uses the artificial neural network Sample diagnosis index data memory learning, calculate the input to be diagnosed index data to get road traffic safety level. Apply these two models to Jiangning District, Nanjing City. The results show that the traffic safety levels of Jiangning district are all good with the two models and the diagnostic results are consistent. After comparative analysis, these two models have strong applicability, but when the diagnostic purpose is different, its applicability is different.