Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sanaz Shaker Sepasgozar,Samuel Pierre

doi:10.1109/access.2022.3144112

Sanaz Shaker Sepasgozar, Samuel Pierre

Open Access

https://doi.org/10.1109/access.2022.3144112

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Abstract

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.

Highlights

One of the important technologies for the Intelligent Transportation System (ITS) is Vehicular Ad hoc Networks (VANETs) that tries to make the environment safer and have better transportation using wireless communications [1].The traffic flow prediction with high accuracy is a significant issue in current transportation systems
We investigate the effect of the last two previous steps based on road and network parameters on network traffic happening with the aim of network traffic prediction using both machine learning and deep learning algorithms
The results show that the novel proposed Random Forest (RF)-Gated Recurrent Unit (GRU)-NTP model would predict the network traffic affected by road traffic happening in the VANET environment more accurately than pure algorithms

Summary

Introduction

The traffic flow prediction with high accuracy is a significant issue in current transportation systems. It can help have the best path planning, make a better choice in selecting the greater route for travelers and decrease the traffic flow. Intelligent methods can help us predict the traffic flow considering different effective parameters. Several studies have been proposed different models to predict network traffic and road traffic independently using learning algorithms. Some researchers attempt to predict the road traffic flow using weather conditions that affect traffic flow. From a certain point of view, we can divide the previous works into two parts: road traffic prediction and network traffic prediction, where most of them used different machine learning and deep learning algorithms

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