Hybrid Methods for Short-Term Traffic Flow Prediction Based on ARIMA-GARCH Model and Wavelet Neural Network-Reference-Cited by-同舟云学术

Hybrid Methods for Short-Term Traffic Flow Prediction Based on ARIMA-GARCH Model and Wavelet Neural Network

Author:

Yao Ronghan¹^ORCID,Zhang Wensong²^ORCID,Zhang Lihui³^ORCID

Affiliation:

1. Associate Professor, School of Transportation and Logistics, Dalian Univ. of Technology, Dalian 116024, China (corresponding author). ORCID: .

2. Ph.D. Student, School of Transportation and Logistics, Dalian Univ. of Technology, Dalian 116024, China. ORCID: .

3. Associate Professor, Institute of Intelligent Transportation Systems, Zhejiang Univ., Zhejiang 310058, China. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

Reference50 articles.

1. Analysis of freeway traffic time-series data by using Box-Jenkins techniques;Ahmed M.;Transp. Res. Rec.,1979

5. Time-varying confidence interval forecasting of travel time for urban arterials using ARIMA-GARCH model;Cui Q.;J. Southeast Univ.,2014

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

4. Highway traffic parameter prediction model based on the combined deep learning model;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19