A Traffic Flow Data Quality Repair Model Based on Spatiotemporal Correlation-Reference-Cited by-同舟云学术

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A Traffic Flow Data Quality Repair Model Based on Spatiotemporal Correlation

Published:2024 Issue: Volume:12 Page:116816-116828
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Li Yan¹^ORCID,Xu Liangjie¹^ORCID,Qin Wendie¹^ORCID,Xie Cong²^ORCID,Ji Chuanwang³^ORCID

Affiliation:

1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China

2. School of Automotive and Traffic Engineering, Wuhan University of Science and Technology, Wuhan, China

3. School of Energy and Power Engineering, Dalian University of Technology, Dalian, China

Funder

International Science and Technology Cooperation Project of Hubei Province

Key Research and Development Plan Project of Hubei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10630521.pdf?arnumber=10630521

Reference37 articles.

1. Road Traffic Flow Prediction with a Time-Oriented ARIMA Model

2. A Map Reduce-Based Nearest Neighbor Approach for Big-Data-Driven Traffic Flow Prediction

3. A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction

4. Short-term traffic flow prediction based on optimized deep learning neural network: PSO-bi-LSTM;Naheliya;Phys. A, Stat. Mech. Appl.,2023

5. Attention Based Graph Bi-LSTM Networks for Traffic Forecasting

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