Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET
MAMIDISETTI YASWANTH
, TULASI RAJU NETHALA , Dr.P.SRINIVASULU
Vehicular network, network traffic prediction, road traffic prediction, regression methods, classification methods, machine learning algorithms, deep learning algorithms
Vehicular Ad hoc Networks (VANETs) are established on vehicles that are intelligent and can have Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Units (V2R) communications. In this paper, we propose a model for predicting network traffic by considering the parameters that can lead to road traffic happening. The proposed model integrates a Random Forest- Gated Recurrent Unit- Network Traffic Prediction algorithm (RF-GRU-NTP) to predict the network traffic flow based on the traffic in the road and network simultaneously. This model has three phases including network traffic prediction based on V2R communication, road traffic prediction based on V2V communication, and network traffic prediction considering road traffic happening based on V2V and V2R communication. The hybrid proposed model which implements in the third phase, selects the important features from the combined dataset (including V2V and V2R communications), by using the Random Forest (RF) machine learning algorithm, then the deep learning algorithms to predict the network traffic flow apply, where the Gated Recurrent Unit (GRU) algorithm gives the best results. The simulation results show that the proposed RF-GRU-NTP model has better performance in execution time and prediction errors than other algorithms which used for network traffic prediction.
"Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.b195-b205, October-2024, Available :https://ijnrd.org/papers/IJNRD2410127.pdf
Volume 9
Issue 10,
October-2024
Pages : b195-b205
Paper Reg. ID: IJNRD_301237
Published Paper Id: IJNRD2410127
Downloads: 00059
Research Area: Science and Technology
Country: West Godavari , Andhra Pradesh , India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
