Abstract:
Intelligent vehicles with Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Unit (V2R) communications form Vehicular Ad hoc Networks (VANETs). This paper proposes a model to predict network traffic using road traffic parameters.
The model predicts network traffic flow based on road and network traffic using a Random Forest-Gated Recurrent Unit-Network Traffic Prediction algorithm (RF-GRU-NTP). This model predicts network and road traffic using V2R and V2V communication, respectively.
In the third phase, the hybrid proposed model selects important features from the combined dataset (including V2V and V2R communications) using the Random Forest (RF) machine learning algorithm, then applies deep learning algorithms to predict network traffic flow, where the Gated Recurrent Unit (GRU) algorithm performs best.
Simulations show that the proposed RF-GRU-NTP model outperforms other network traffic prediction algorithms in execution time and prediction errors.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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