Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies
The smart city traffic management domain is perpetually a crucial sector that requires innovative strategies due to expanding urbanization and vehicle use. In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Fe...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10636149/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849339849333538816 |
|---|---|
| author | K. M. Karthick Raghunath C. Rohith Bhat Venkatesan Vinoth Kumar Velmurugan Athiyoor Kannan T. R. Mahesh K. Manikandan N. Krishnamoorthy |
| author_facet | K. M. Karthick Raghunath C. Rohith Bhat Venkatesan Vinoth Kumar Velmurugan Athiyoor Kannan T. R. Mahesh K. Manikandan N. Krishnamoorthy |
| author_sort | K. M. Karthick Raghunath |
| collection | DOAJ |
| description | The smart city traffic management domain is perpetually a crucial sector that requires innovative strategies due to expanding urbanization and vehicle use. In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Federated Learning (FL) to deal with urban traffic effectively. This approach leverages the sophisticated functionalities of TCNs to evaluate and estimate traffic trends, such as congested phases, traffic flow, and ideal mobility routes. The system guarantees data privacy and utilizes decentralized information analysis using Federated Learning. In this approach, every point in the intelligent city network, including traffic sensors and cameras, serves to collectively comprehend traffic patterns without disclosing raw data. Using this cooperative method not only improves the model’s ability to forecast outcomes accurately but also enables efficient real-time traffic management, with the ability to adapt to changing conditions. The method has shown significant efficacy in enhancing traffic flow and mitigating congestion. The critical criteria are a 20% drop in average commuting times, a 25% drop in traffic congestion, and a 15% enhancement in emergency response times. These statistics highlight the system’s efficiency in improving urban traffic control by integrating modern technologies. |
| format | Article |
| id | doaj-art-89fd70bb82a8494d97ce5eb4f6161f4a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-89fd70bb82a8494d97ce5eb4f6161f4a2025-08-20T03:44:02ZengIEEEIEEE Access2169-35362024-01-011211538611539910.1109/ACCESS.2024.344329810636149Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control StrategiesK. M. Karthick Raghunath0C. Rohith Bhat1https://orcid.org/0000-0003-3920-7597Venkatesan Vinoth Kumar2https://orcid.org/0000-0003-1070-3212Velmurugan Athiyoor Kannan3T. R. Mahesh4K. Manikandan5N. Krishnamoorthy6Department of Computer Science, JAIN (Deemed-to-be University), Global Campus, Bengaluru, Karnataka, IndiaSaveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IndiaSchool of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, IndiaSchool of Computer Science and Engineering, JAIN (Deemed-to-be University), Bengaluru, Karnataka, IndiaSchool of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, IndiaSchool of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, IndiaThe smart city traffic management domain is perpetually a crucial sector that requires innovative strategies due to expanding urbanization and vehicle use. In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Federated Learning (FL) to deal with urban traffic effectively. This approach leverages the sophisticated functionalities of TCNs to evaluate and estimate traffic trends, such as congested phases, traffic flow, and ideal mobility routes. The system guarantees data privacy and utilizes decentralized information analysis using Federated Learning. In this approach, every point in the intelligent city network, including traffic sensors and cameras, serves to collectively comprehend traffic patterns without disclosing raw data. Using this cooperative method not only improves the model’s ability to forecast outcomes accurately but also enables efficient real-time traffic management, with the ability to adapt to changing conditions. The method has shown significant efficacy in enhancing traffic flow and mitigating congestion. The critical criteria are a 20% drop in average commuting times, a 25% drop in traffic congestion, and a 15% enhancement in emergency response times. These statistics highlight the system’s efficiency in improving urban traffic control by integrating modern technologies.https://ieeexplore.ieee.org/document/10636149/Decentralized learningmobile patternstransporttraffic predictionaccuracynormalization |
| spellingShingle | K. M. Karthick Raghunath C. Rohith Bhat Venkatesan Vinoth Kumar Velmurugan Athiyoor Kannan T. R. Mahesh K. Manikandan N. Krishnamoorthy Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies IEEE Access Decentralized learning mobile patterns transport traffic prediction accuracy normalization |
| title | Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies |
| title_full | Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies |
| title_fullStr | Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies |
| title_full_unstemmed | Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies |
| title_short | Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies |
| title_sort | redefining urban traffic dynamics with tcn fl driven traffic prediction and control strategies |
| topic | Decentralized learning mobile patterns transport traffic prediction accuracy normalization |
| url | https://ieeexplore.ieee.org/document/10636149/ |
| work_keys_str_mv | AT kmkarthickraghunath redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies AT crohithbhat redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies AT venkatesanvinothkumar redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies AT velmuruganathiyoorkannan redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies AT trmahesh redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies AT kmanikandan redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies AT nkrishnamoorthy redefiningurbantrafficdynamicswithtcnfldriventrafficpredictionandcontrolstrategies |