Revolutionizing emergency vehicle response: a smart traffic management approach with adaptive CNN and hybrid deep learning

This study explores the important problem of urban traffic congestion, focusing on the challenges that emergency vehicles encounter. Existing traffic management systems frequently fail to efficiently prioritize emergency vehicles, relying on costly sensors or manual intervention. To solve these chal...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	International journal of information technology (Singapore. Online) Jg. 17; H. 3; S. 1793 - 1804
Hauptverfasser:	Vikraman, Bindu Puthentharayil, Mahadevan, Vanitha, Jabbar, Rani Fathima
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.04.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial Intelligence Artificial neural networks Classification Computer Imaging Computer Science Datasets Decision making Deep learning Emergency vehicles Forecasting techniques Image Processing and Computer Vision Machine Learning Management systems Neural networks Original Research Pattern Recognition and Graphics Roads & highways Software Engineering Surveillance Time series Traffic congestion Traffic control Traffic flow Traffic management Traffic surveillance Transportation planning Vehicles Vision Enhanced walrus optimization Traffic surveillance Adaptive faster R-CNN Stacked autoEncoder Modified denseNet201
ISSN:	2511-2104, 2511-2112
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!