基于BP神经网络分析事故对路段通行能力的影响

Analysis of Accident Impact on Road Traffic Capacity Based on the BP Neural Network

以发生交通事故的人、机、环境等多方面采集的调查数据为基础,解析突发性交通状态局部演变过程,并总结事发点通行能力的7类主要影响因素和3类评价指标。采用SPSS主成分分析与BP神经网络相结合的方法,对调查数据进行降维,并在保证数据丢失最小原则下,将七类影响因素提取为五类,简化神经网络拓扑结构,提高建模质量,通过BP神经网络模型调整离散输入量,以预测的评估值与实际评估值的误差最小为学习训练目标,求解最佳的连接强度权值与偏置值,得出事故因素与通行能力的定量关系。

Based on survey data of man, vehicles and environment involved in accidents, this study analyzes localevolution of traffic breakdown and summarizes influential factors of accident point capacity which include seventypes of influential factors and three kinds of evaluating indicators. Dimensionality reduction of survey data is ful-filled by SPSS principal component analysis in combination with BP neural network. According to the minimum da-ta loss rule, seven types of influential factors are transformed to five principal components and neural network to-pology is simplified, thus improving the model quality. Adjustment of discrete inputs which use BP neural networkmodel learning and training generates the optimal connection weights-W and bias values-B when the error be-tween predicted value and actual value reaches the minimum. The quantitative relationship between the factors ofthe accident and the capacity is thereby worked out.