Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation

Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation

The field of intelligent transportation systems is rapidly evolving, with increasing focus on addressing traffic congestion, a pervasive problem in urban environments. This study contributes to this domain by enhancing traffic prediction models. Traditional traffic models often fall short in accurat...

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Bibliographic Details
Main Authors: Younus Hasan Taher, Jit Singh Mandeep, Mohammad Tariqul Islam, Omer Tareq Abdulhae, Ahmed Thair Shakir, Md. Shabiul Islam, Mohamed S. Soliman
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Road traffic
deep reinforcement learning
Kalman filters
intelligent transportation systems
prediction methods
Online Access:https://ieeexplore.ieee.org/document/10820531/
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https://ieeexplore.ieee.org/document/10820531/

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