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JACIII Vol.29 No.6 pp. 1305-1310
doi: 10.20965/jaciii.2025.p1305
(2025)

Research Paper:

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Vehicle Traffic Prediction and Analysis Using Hybrid Deep Learning Technique

Betty Paulraj*1,† ORCID Icon, Shilpi Sharma*2 ORCID Icon, Narayan C. Debnath*3 ORCID Icon, and Ramzi A. Haraty*4 ORCID Icon

*1Amity School of Engineering and Technology, Amity University
125 Noida, Uttar Pradesh 201303, India

†Corresponding author

*2Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University
E3-317A, ASET-CSE, Sector-125, Noida 201302, India

*3School of Computing and Information Technology, Eastern International University
81 Nam Ky Khoi Nghia Street, Hoa Phu Ward, Thu Dau Mot, Binh Duong, Ho Chi Minh City 75114, Vietnam

*4Department of Computer Sciences and Mathematics, Lebanese American University
P.O. Box 13-5053 Chouran, Beirut 1102, Lebanon

Received:
May 28, 2024
Accepted:
June 18, 2025
Published:
November 20, 2025
Creative Commons License
Keywords:
machine learning, intelligent transport system, prediction, neural networks, traffic analysis
Abstract

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.

Cite this article as:
B. Paulraj, S. Sharma, N. Debnath, and R. Haraty, “Vehicle Traffic Prediction and Analysis Using Hybrid Deep Learning Technique,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.6, pp. 1305-1310, 2025.
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Last updated on Nov. 19, 2025