关键词: 城市交通, 数据预测, 云&mdash, 自组织神经网络, 交通流

Abstract:

Modern transportation systems have complex structure, and the existence of fuzzy, stochastic and uncertainty factors increase the difficulty of huge data involved in qualitative and quantitative integrated analysis. This paper developed the cloud neural network self-organization of traffic flow forecasting model based on the characteristics of cloud model and self-organizing neural network. Using cloud model fuzziness and randomness advantages, the paper proposed the prediction model that can improve the reliability of selforganizing neural network prediction learning sample data to process data problems. Through comparing two models to a city traffic flow forecasting with actual data, the paper found that the forecasting model has higher coefficient of determination than the only using of self-organizing neural network. The results show that the model proposed in the traffic flow forecasting can improve accuracy and reduce generalization error.

Key words: urban traffic, data forecasting, cloud-self-organizing neural network, traffic flow