IntelliFlow: A Machine Learning-Driven Dynamic Traffic Management in Smart Cities
IntelliFlow: A Machine Learning-Driven Dynamic Traffic Management in Smart Cities
- Neue Suche nach: Alla, Kesava Rao
- Neue Suche nach: Thangarasu, Gunasekar
- Neue Suche nach: Alla, Kesava Rao
- Neue Suche nach: Thangarasu, Gunasekar
Efficient traffic management in smart cities necessitates advanced technologies and intelligent systems. This research proposes IntelliFlow-an integrated approach that combines machine learning algorithms and Internet of Things (IoT) capabilities for efficient traffic management in smart cities. It employs Random Forests, LSTM Networks and SVM to optimize traffic flow, enhance mobility and improve transportation efficiency. The algorithms are used for tasks such as traffic flow prediction, incident detection, congestion classification, travel time estimation, anomaly detection and traffic signal optimization. By leveraging ensemble learning techniques, IntelliFlow achieves significant improvements in traffic flow optimization, incident detection accuracy and congestion classification compared to individual algorithms. The proposed system presents a comprehensive and effective solution for dynamic decision-making, real-time optimization and improved mobility in smart cities..
Dokumentinformationen
IntelliFlow: A Machine Learning-Driven Dynamic Traffic Management in Smart Cities
2023-08-18
915896 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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