Approximate Inference of Traffic Flow State at Signalized Intersections Using a Bayesian Learning Framework-Reference-Cited by-同舟云学术

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Approximate Inference of Traffic Flow State at Signalized Intersections Using a Bayesian Learning Framework

Published:2023-05 Issue:5 Volume:24 Page:4765-4776
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Zhang Nan¹^ORCID,Yang Xiaoguang²,Guo Haifeng³^ORCID,Dong Hongzhao⁴,Ma Wanjing²^ORCID

Affiliation:

1. SenseTime Research, Hangzhou, China

2. School of Transportation Engineering, Tongji University, Shanghai, China

3. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

4. School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10121021/10101685.pdf?arnumber=10101685

Reference51 articles.

1. Real-Time Lagrangian Traffic State Estimator for Freeways

2. State Estimation for Polyhedral Hybrid Systems and Applications to the Godunov Scheme for Highway Traffic Estimation

3. Incorporation of Lagrangian measurements in freeway traffic state estimation

4. Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

5. An Extended Kalman Filter Application for Traffic State Estimation Using CTM with Implicit Mode Switching and Dynamic Parameters

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A conditional diffusion model for probabilistic estimation of traffic states at sensor-free locations;Transportation Research Part C: Emerging Technologies;2024-09

2. Scalable Reinforcement Learning Framework for Traffic Signal Control Under Communication Delays;IEEE Open Journal of Vehicular Technology;2024

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