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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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820531/ |
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| author | Younus Hasan Taher Jit Singh Mandeep Mohammad Tariqul Islam Omer Tareq Abdulhae Ahmed Thair Shakir Md. Shabiul Islam Mohamed S. Soliman |
| author_facet | Younus Hasan Taher Jit Singh Mandeep Mohammad Tariqul Islam Omer Tareq Abdulhae Ahmed Thair Shakir Md. Shabiul Islam Mohamed S. Soliman |
| author_sort | Younus Hasan Taher |
| collection | DOAJ |
| description | 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 accurately predicting traffic flow, particularly in dynamic urban settings. This limitation necessitates the development of more adaptive and accurate predictive models to manage traffic congestion effectively. In urban environments, traffic estimation at multi-intersection nodes and connecting roadis approached using statistical filters (Kalman or particle filters). The challenge lies in accurately predicting the vehicular traffic state at intersections, considering the dynamic nature of urban traffic and potential communication failures. This study introduces a novel approach in traffic modeling using statistical filters. The method involves the use of dynamically interchangeable state transition matrices, aligned with specific traffic signal control actions. This allows for more precise predictions under varying traffic conditions and control scenarios. The effectiveness of the proposed models is validated across multiple intersections in urban settings. The evaluation focuses on the models’ scalability, adaptability to complex traffic networks, and robustness in communication failure scenarios. Our proposed Kalman filter showcased superior performance in traffic prediction with an average RMSE of 12.15975, demonstrating a significant improvement over the average measurement RMSE of 13.996875 and over traditional smooth Kalman filter and traditional particle filter. |
| format | Article |
| id | doaj-art-b2d5d4353fe0421f85136fc6360ba899 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b2d5d4353fe0421f85136fc6360ba8992025-01-21T00:02:32ZengIEEEIEEE Access2169-35362025-01-01138140815710.1109/ACCESS.2024.352511910820531Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State EstimationYounus Hasan Taher0https://orcid.org/0000-0001-5412-0321Jit Singh Mandeep1https://orcid.org/0000-0002-1499-0426Mohammad Tariqul Islam2https://orcid.org/0000-0002-4929-3209Omer Tareq Abdulhae3https://orcid.org/0000-0002-3342-0488Ahmed Thair Shakir4https://orcid.org/0000-0002-6636-0194Md. Shabiul Islam5Mohamed S. Soliman6https://orcid.org/0000-0002-9431-4195Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment (FKAB), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment (FKAB), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment (FKAB), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment (FKAB), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment (FKAB), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaFaculty of Engineering, Multimedia University, Cyberjaya, Selangor, MalaysiaDepartment of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi ArabiaThe 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 accurately predicting traffic flow, particularly in dynamic urban settings. This limitation necessitates the development of more adaptive and accurate predictive models to manage traffic congestion effectively. In urban environments, traffic estimation at multi-intersection nodes and connecting roadis approached using statistical filters (Kalman or particle filters). The challenge lies in accurately predicting the vehicular traffic state at intersections, considering the dynamic nature of urban traffic and potential communication failures. This study introduces a novel approach in traffic modeling using statistical filters. The method involves the use of dynamically interchangeable state transition matrices, aligned with specific traffic signal control actions. This allows for more precise predictions under varying traffic conditions and control scenarios. The effectiveness of the proposed models is validated across multiple intersections in urban settings. The evaluation focuses on the models’ scalability, adaptability to complex traffic networks, and robustness in communication failure scenarios. Our proposed Kalman filter showcased superior performance in traffic prediction with an average RMSE of 12.15975, demonstrating a significant improvement over the average measurement RMSE of 13.996875 and over traditional smooth Kalman filter and traditional particle filter.https://ieeexplore.ieee.org/document/10820531/Road trafficdeep reinforcement learningKalman filtersintelligent transportation systemsprediction methods |
| spellingShingle | Younus Hasan Taher Jit Singh Mandeep Mohammad Tariqul Islam Omer Tareq Abdulhae Ahmed Thair Shakir Md. Shabiul Islam Mohamed S. Soliman Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation IEEE Access Road traffic deep reinforcement learning Kalman filters intelligent transportation systems prediction methods |
| title | Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation |
| title_full | Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation |
| title_fullStr | Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation |
| title_full_unstemmed | Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation |
| title_short | Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation |
| title_sort | filter for traffic congestion prediction leveraging traffic control signal actions for dynamic state estimation |
| topic | Road traffic deep reinforcement learning Kalman filters intelligent transportation systems prediction methods |
| url | https://ieeexplore.ieee.org/document/10820531/ |
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