Hybrid Traffic Flow Forecasting Model Based on MRA

摘要：

The presence of complex scaling behavior in traffic makes accurate forecasting of traffic a challenging task. This paper proposes a multi-scale decomposition & reconstruction approach for real-time traffic prediction. The proposed scheme combines the superior characteristics of wavelet neural networks, ARIMA and MRA. This multi-scale decomposition and reconstruction approach can better capture the correlations within traffic flows caused by different mechanisms, which may not be obvious when examining the raw data directly. The proposed hybrid prediction algorithm is applied to real-time traffic data from a large metropolitan area. It is shown that the proposed algorithm generally outperforms traffic prediction using a single prediction model approach and gives more accurate results.

关键词： multi-resolution analysis (MRA) wavelet neural network ARIMA traffic flow forecast

作者: Hongqiong Huang George F. List Tianhao Tang Alixandra Demers Tianzhen Wang

作者单位: Shanghai Maritime University, Shanghai, China, 200135 North Carolina State University, Raleigh, North Carolina, U.S.A

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机电自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 222-225

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）