Prediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of The Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model-Reference-Cited by-同舟云学术

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Prediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of The Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model

Published:2023-11-17 Issue: Volume:65 Page:8126-8131
ISSN:
Container-title:2023 China Automation Congress (CAC)
language:
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Author:

Li Lin¹,Zhao Jiahao¹,Coskun Serdar²,Langari Reza³

Affiliation:

1. School of Civil Engineering and Transportation, Northeast Forestry University,Harbin,Heilongjiang,China

2. Tarsus University,Department of Mechanical Engineering,Mersin,Turkiye

3. Texas A&M University,Department of Mechanical Engineering,College Station,Texas,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10450150/10449968/10450343.pdf?arnumber=10450343

Reference15 articles.

1. Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models;Guotao;IEEE Transactions on Industrial Electronics,2017

2. Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models

3. Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples

4. Short-term Traffic-state Prediction of Urban Road Networks Based on the Fusion of a Link-transmission Model and Deep Learning;Xiqun;China Journal of Highway and Transport,2021

5. Performance of continuum models for realworld traffic flows: Comprehensive benchmarking

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