摘要
实时准确的短时交通流预测是实现智能交通系统中交通控制和交通诱导的关键技术之一。由于短时交通流数据的复杂性,首先采用饱和关联维数法和Cao氏法对交通流时间序列的嵌入维数和延迟时间进行计算,并采用Wolf方法计算相空间重构后的交通流时间序列的最大Lyapunov指数。结果表明,交通流时间序列具有混沌特性,可预测性较好。随后,分别采用基于ESN和Elman神经网络的预测方法对交通流时间序列进行预测,结果表明,两者在预测精度相当的情况下,前者的训练速度较后者有了极大的提高。
The real-time and precise short-term traffic flow prediction is the key factor for the realizing of traffic control and traffic guidance in the intelligent traffic system. As the complexity of short-term traffic flow data, the saturated correlation dimension method and Cao＇ s method are adopted to calculate the embedding dimension and delay time of traffic flow time series, and the Wolf method is applied to calculate the largest Lyapunov exponent of the reconstructed traffic flow time series. The result shows that the traffic flow series is a chaotic sequence with better predictability. Then the prediction methods based on ESN and Elman neural networks are applied to predict traffic flow time series respectively, it indicates that the former has quicker prediction speed on the condition of the same prediction accuracy.
出处
《湖南工业大学学报》
2013年第6期67-72,共6页
Journal of Hunan University of Technology
基金
湖南省教育厅科学研究基金资助项目(11C0816)
关键词
交通信息工程及控制
相空间重构
回声状态网络
ELMAN神经网络
交通流预测
traffic information engineering and control
phase space reconstruction
echo state network
Elman neural networks
traffic flow prediction
