短时车流量组合预测模型

Integrated forecasting model for short-term traffic flow

随着道路车量不断增多,由交通异常事件造成的非正常拥堵情况严重影响了出行者的出行效率和路网的整体运行水平.因此,需要准确及时地检测出非正常拥堵情况,通过诱导、疏通等方式改善拥堵状况.对车流量的准确预测是检测非正常拥堵的有效方法.根据交通流量的不确定性和非线性的特点,将改进的BP神经网络模型和ARIMA模型进行组合,建立组合预测模型.实验结果表明,组合模型的预测结果比单个模型的预测结果理想,且达到较高的预测精度.

With the continuous increase of road vehicles, occasional congestion caused by traffic accidents seriously affect the commuting efficiency of traveler and the overall operation level of road network. Real-time and exact fore- casting of short-term traffic flow volume is the key point to intelligent traffic system and precondition to solve the congestion situation by route guidance and clearing. According to the uncertain and non-linear features of traffic vol- ume, a model integrated of the improved BP neural network and autoregressive integrated moving average （ARIMA） model is established to forecast the short-term traffic flow. el has an advantage over the single models in forecasting The case application result shows that the combined rood- performance and forecasting accuracy.