Highway traffic parameter prediction model based on the combined deep learning model-Reference-Cited by-同舟云学术

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Highway traffic parameter prediction model based on the combined deep learning model

Published:2024-01-19 Issue: Volume:39 Page:978-981
ISSN:
Container-title:2024 4th International Conference on Neural Networks, Information and Communication (NNICE)
language:
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Author:

Jiang Li¹,Duan Chenggang¹,Li Zhongqiu¹,Jiao Yanli²,Ge Jiqiang³,Chen Yusen⁴

Affiliation:

1. Hebei Expressway Yanchong Preparation Office,Zhangjiakou,China,075499

2. Hebei Provincial Communications Planning and Design Institute Co., Ltd,Shijiazhuang,China,050021

3. Beijing Jiaotong University,School of Traffic and Transportation,Beijing,China,100044

4. Beijing Waystone Information Technology Co., Ltd,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10498130/10498125/10498388.pdf?arnumber=10498388

Reference11 articles.

1. Forecasting Bus Passenger Flow Using Bi-LSTM with Attention Mechanism

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

3. Forecasting Bus Passenger Flow Using Bi-LSTM with Attention Mechanism

4. Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM

5. An optimized BP neural network for short-term traffic flow prediction [J];Jie;Sensors and Microsystems,2020

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