交通事故Pi-Sigma模糊神经网络预测模型

提出了基于Pi-Sigma模糊神经网络的交通事故预测模型，选用Takagi-Sugeno型模糊推理系统和BP神经网络，以年平均日交通量、交通负荷、设计速度、车道宽度为输入，以每公里年均事故次数为输出，利用哈尔滨市133条主次干道的道路交通条件数据和5年的交通事故数据对模型进行了训练和检验，并将该模型与模糊逻辑模型、BP神经网络模型进行了对比分析.结果表明，交通事故Pi-Sigma模糊神经网络预测模型在预测精度及计算效率上总体优于其他两种模型，较适合于大样本条件下交通事故的快速预测.

Crash Prediction Model Based on Pi-Sigma Fuzzy Neural Network

A crash prediction model based on the Pi-Sigma fuzzy neural network was put forward. The Takagi-Sugeno inference system and BP neural network were employed in the model. AADT, traffic load, design speed and lane width were taken as input variables while the number of crashes per kilometer per year as the output variable. The model was further calibrated and validated with the data which includes 133 segments from arterial road in Harbin City and 5 years'' crashes. Then, the model was compared with the fuzzy logic model and the BP neural network model. The results show that the Pi-Sigma fuzzy neural network crash prediction model is better than the other two models from the aspects of prediction accuracy and calculation efficiency, which makes it more suitable for the rapid crash prediction with big sample data.