Hybrid short-term traffic flow prediction model of intersections based on improved complete ensemble empirical mode decomposition with adaptive noise-Reference-Cited by-同舟云学术

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Hybrid short-term traffic flow prediction model of intersections based on improved complete ensemble empirical mode decomposition with adaptive noise

Published:2019-04 Issue:4 Volume:11 Page:168781401984181
ISSN:1687-8140
Container-title:Advances in Mechanical Engineering
language:en
Short-container-title:Advances in Mechanical Engineering

Author:

Tian Xiujuan¹^ORCID,Yu Dexin²³⁴,Xing Xue⁵,Wang Shiguang²^ORCID,Wang Zhuorui²

Affiliation:

1. School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, China

2. College of Transportation, Jilin University, Changchun, China

3. Jilin Engineering Research Center for Intelligent Transportation System, Changchun, China

4. Jilin Province Key Laboratory of Road Traffic, Changchun, China

5. College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, China

Funder

National Key Technology Research and Development Plan Project

Publisher

SAGE Publications

Subject

Mechanical Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1177/1687814019841819

Reference32 articles.

1. A regression-based approach to short-term system load forecasting

2. A Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting

3. Short-term load forecasting using lifting scheme and ARIMA models

4. Hybrid Kalman Filters for Very Short-Term Load Forecasting and Prediction Interval Estimation

5. The Application of Adaptive Kalman Filter in Traffic Flow Forecasting

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4. A new traffic flow prediction model based on cosine similarity variational mode decomposition, extreme learning machine and iterative error compensation strategy;Engineering Applications of Artificial Intelligence;2022-10

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