Citation

Al-Selwi, Hatem Fahd and Abd. Aziz, Azlan and Abas, Fazly Salleh (2021) Deep Learning-Based Vehicular Traffic Prediction for ITS Applications. In: 2nd FET PG Engineering Colloquium Proceedings 2021, 1-15 Dec. 2021, Online Conference. (Unpublished)

Text 19 Hatem Fahd Abdu Qaid Alselwi.pdf Restricted to Repository staff only Download (14kB)

Abstract

In this research, traffic data is formatted as a graph network problem and graph neural networks are used develop a deep learning model to capture the spatio-temporal characteristics in traffic data. The model development process requires several changes and evaluation to reach the optimal deep learning model that can reach an acceptable accuracy and efficiency.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep Learning, Connected Vehicles, Traffic Predicion, ITS, Resource Allocation
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL500-777 Aeronautics. Aeronautical engineering
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	26 Jan 2022 01:45
Last Modified:	27 Jan 2022 23:45
URII:	http://shdl.mmu.edu.my/id/eprint/9890

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