Vehicle Traffic Prediction and Analysis Using Hybrid Deep Learning Technique

The main objective of this study is to predict road traffic in unconditional situations in real time. The advancement of machine learning techniques paves the way for the prediction of traffic well in advance. This system is completely trained on the dataset of vehicle services with pre-scheduled ti...

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Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 29; H. 6; S. 1305 - 1310
Hauptverfasser: Paulraj, Betty, Sharma, Shilpi, Debnath, Narayan C., Haraty, Ramzi A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Schlagworte:
Accuracy
Algorithms
Big Data
Deep learning
Effectiveness
Informatics
Internet of Things
Machine learning
Neural networks
Traffic flow
ISSN:1343-0130, 1883-8014
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Zusammenfassung:The main objective of this study is to predict road traffic in unconditional situations in real time. The advancement of machine learning techniques paves the way for the prediction of traffic well in advance. This system is completely trained on the dataset of vehicle services with pre-scheduled timings. This advanced prediction improves the travel experience at large. As the system has to operate on the time-based data in an unconditional and unplanned environment, the effectiveness of the system is evaluated using deep learning models. The results obtained after testing were presented and a comparative analysis of the effectiveness of each model in terms of accuracy and correctness were studied.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p1305