Car-following informed neural networks for real-time vehicle trajectory imputation and prediction-Reference-Cited by-同舟云学术

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Car-following informed neural networks for real-time vehicle trajectory imputation and prediction

Published:2024-07-09 Issue: Volume: Page:1-26
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Yin Yu-Hang¹²³,Lü Xing¹²³,Li Shu-Kai⁴,Yang Li-Xing⁴,Gao Ziyou⁴

Affiliation:

1. State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, People's Republic of China

2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, People's Republic of China

3. Department of Mathematics, Beijing Jiaotong University, Beijing, People's Republic of China

4. School of Systems Science, Beijing Jiaotong University, Beijing, People's Republic of China

Funder

Beijing Jiaotong University

Xidian University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2024.2374523

Reference67 articles.

1. Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results

2. Dynamical model of traffic congestion and numerical simulation

3. Traffic State Estimation Per Lane in Highways with Connected Vehicles

4. Chen S. Q. and X. Lü. 2024. “Adaptive Network Traffic Control with Approximate Dynamic Programming Based on a Non-Homogeneous Poisson Demand Model.” Transportmetrica B12: 2336029.

5. Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data

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