Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods inVANET

© 2024 IJNRD | Volume 9, Issue 10 October 2024 | ISSN: 2456-4184 | IJNRD.ORG
IJNRD2410127 International Journal Of Novel Research And Development (www.ijnrd.org) b195
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Network Traffic Prediction Model Considering Road
Traffic Parameters Using Artificial Intelligence
Methods in VANET
1MAMIDISETTI YASWANTH 2 TULASI RAJU NETHALA 3 Dr.P.SRINIVASULU
1 M.Tech Scholar, Department of Computer Science and Engineering, Swarnandhra College of Engineering and Technology,
Narsapuram, AP, lndia,
2. Associate Professor, Department of Computer Science and Engineering, Swarnandhra College of Engineering and Technology,
Narsapuram, AP,India,
3.Professor and HOD, Department of Computer Science and Engineering, Swarnandhra College of Engineering and Technology,
Narsapuram, AP, India,
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.
Keywords – Vehicular network, network traffic
prediction, road traffic prediction, regression

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methods, classification methods, machine
learning algorithms, deep learning algorithms.
1. INTRODUCTION
One of the important technologies for the
Intelligent Transportation System (ITS) is
VANET 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. It can help have
the best path planning, make a better choice in
selecting the greater route for travelers and
decrease the traffic flow. Distinguishing that
where and when the traffic will happen is a
promising solution for managing transportation
[2]. However, the new perspective of network
traffic flow is that the traffic in the road could
affect network traffic. According to the V2V
communications in VANET, vehicles can send
packets to each other to forecast the road traffic.
By increasing the number of vehicles and traffic
on the road, the number of packets sent would
grow, leading to network traffic. Previous
studies worked on road traffic and network
traffic separately, and we investigated them in
the literature review. However, most of them
addressed the traffic problem on the road or in
the network independently, while in this paper,
we will discover the relation between road and
network traffic parameters together with the aim
of network traffic prediction. Intelligent ways
via machine learning (ML) techniques are the
optimum solutions that can address traffic
prediction problems with the aim of traffic flow
prediction. There are some computational
approaches like Bayesian modeling, fuzzy logic,
hybrid modeling, Neural Networks (NN), and
statistical modeling, which most of them,
specially the NN, are promising solutions aiming
to improve the accuracy of prediction in data
traffic flow [3]. The significant point that should
consider in all these ways, is the accuracy of
prediction. ML techniques are divided into three
types: Unsupervised Learning (training would be
based on unlabeled data), Supervised Learning
(training would be based on labeled data), and
Reinforcement Learning (it learns from the
performance of the learning agent). Moreover,
some types of ML schemes like Transfer
Learning and Online Learning are sub-
categorized by these three types of ML schemes
[4].
Fig.1: Example figure
Another promising solution in the case of a large
and complex dataset is deep learning (DL)
algorithms for prediction problems. It has
different types of algorithms that Recurrent
Neural Network (RNN) [5], [6] and
Convolutional Neural Network (CNN) [7] are
the two famous algorithms that are used in many
studies. Generally, the RNN has two modules
called Long Short-Term Memory (LSTM) [8]
and Gated Recurrent Unit (GRU) [9], [10],
where the LSTM algorithm is similar to RNN by
intention to address the vanishing problem. One
of the most critical features of these algorithms

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is that they can learn dependencies for a long
time with the aim of prediction in time-series
datasets, and the GRU algorithm is like LSTM
with more minor complications due to the
number of its gate that leads to making it faster
than LSTM [11]. Furthermore, to extract more
features and bidirectional dependencies, Bi-
directional Long Short-Term Memory (Bi-
LSTM) algorithm can be used. In this kind of
algorithm, the sequence of the process can be
done in two directions (forward and backward)
using two different hidden layers [12].
2. LITERATURE REVIEW
Improving dynamic and distributed
congestion control in vehicular ad hoc
networks:
To provide reliable communications in
Vehicular Ad hoc Networks (VANets), it is vital
to take into account Quality of Services (QoS).
Delay and packet loss are two main QoS
parameters considered by congestion control
strategies. In this paper, a Multi-Objective Tabu
Search (MOTabu) strategy is proposed to control
congestion in VANets. The proposed strategy is
dynamic and distributed; it consists of two
components: congestion detection and
congestion control. In the congestion detection
component, congestion situation is detected by
measuring the channel usage level. In congestion
control component, a MOTabu algorithm is used
to tune transmission range and rate for both
safety and non-safety massages by minimizing
delay and jitter. The performance of the
proposed strategy is then evaluated with
highway and urban scenarios using five
performance metrics including the number of
packet loss, packet loss ratio, number of
retransmissions, average delay, and throughput.
Simulation results show that MOTabu strategy
significantly outperforms in comparison with
other strategies like CSMA/CA, D-FPAV,
CABS, and so on. Conducting congestion
control using our strategy can help provide more
reliable environments in VANets.
A hybrid deep learning based traffic flow
prediction method and its understanding:
Deep neural networks (DNNs) have recently
demonstrated the capability to predict traffic
flow with big data. While existing DNN models
can provide better performance than shallow
models, it is still an open issue of making full
use of spatial-temporal characteristics of the
traffic flow to improve their performance. In
addition, our understanding of them on traffic
data remains limited. This paper proposes a
DNN based traffic flow prediction model (DNN-
BTF) to improve the prediction accuracy. The
DNN-BTF model makes full use of weekly/daily
periodicity and spatial-temporal characteristics
of traffic flow. Inspired by recent work in
machine learning, an attention based model was
introduced that automatically learns to determine
the importance of past traffic flow. The
convolutional neural network was also used to
mine the spatial features and the recurrent neural
network to mine the temporal features of traffic
flow. We also showed through visualization how
DNN-BTF model understands traffic flow data
and presents a challenge to conventional
thinking about neural networks in the
transportation field that neural networks is
purely a “black-box” model. Data from open-
access database PeMS was used to validate the
proposed DNN-BTF model on a long-term

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horizon prediction task. Experimental results
demonstrated that our method outperforms the
state-of-the-art approaches.
Optimized structure of the traffic flow
forecasting model with a deep learning
approach:
Forecasting accuracy is an important issue for
successful intelligent traffic management,
especially in the domain of traffic efficiency and
congestion reduction. The dawning of the big
data era brings opportunities to greatly improve
prediction accuracy. In this paper, we propose a
novel model, stacked autoencoder Levenberg-
Marquardt model, which is a type of deep
architecture of neural network approach aiming
to improve forecasting accuracy. The proposed
model is designed using the Taguchi method to
develop an optimized structure and to learn
traffic flow features through layer-by-layer
feature granulation with a greedy layerwise
unsupervised learning algorithm. It is applied to
real-world data collected from the M6 freeway
in the U.K. and is compared with three existing
traffic predictors. To the best of our knowledge,
this is the first time that an optimized structure
of the traffic flow forecasting model with a deep
learning approach is presented. The evaluation
results demonstrate that the proposed model with
an optimized structure has superior performance
in traffic flow forecasting.
Artificial intelligence for vehicle-to-
everything: A survey
Recently, the advancement in communications,
intelligent transportation systems, and
computational systems has opened up new
opportunities for intelligent traffic safety,
comfort, and efficiency solutions. Artificial
intelligence (AI) has been widely used to
optimize traditional data-driven approaches in
different areas of the scientific research.
Vehicle-to-everything (V2X) system together
with AI can acquire the information from
diverse sources, can expand the driver's
perception, and can predict to avoid potential
accidents, thus enhancing the comfort, safety,
and efficiency of the driving. This paper presents
a comprehensive survey of the research works
that have utilized AI to address various research
challenges in V2X systems. We have
summarized the contribution of these research
works and categorized them according to the
application domains. Finally, we present open
problems and research challenges that need to be
addressed for realizing the full potential of AI to
advance V2X systems.
Visualizing and understanding recurrent
networks
Recurrent Neural Networks (RNNs), and
specifically a variant with Long Short-Term
Memory (LSTM), are enjoying renewed interest
as a result of successful applications in a wide
range of machine learning problems that involve
sequential data. However, while LSTMs provide
exceptional results in practice, the source of their
performance and their limitations remain rather
poorly understood. Using character-level
language models as an interpretable testbed, we
aim to bridge this gap by providing an analysis
of their representations, predictions and error
types. In particular, our experiments reveal the
existence of interpretable cells that keep track of
long-range dependencies such as line lengths,
quotes and brackets. Moreover, our comparative

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analysis with finite horizon n-gram models
traces the source of the LSTM improvements to
long-range structural dependencies. Finally, we
provide analysis of the remaining errors and
suggests areas for further study.
3. METHODOLOGY
Previous studies worked on road traffic and
network traffic separately, and we investigated
them in the literature review. However, most of
them addressed the traffic problem on the road
or in the network independently, while in this
paper, we will discover the relation between
road and network traffic parameters together
with the aim of network traffic prediction.
Intelligent ways via machine learning (ML)
techniques are the optimum solutions that can
address traffic prediction problems with the aim
of traffic flow prediction.
Disadvantages:
1. By increasing the number of vehicles
and traffic on the road, the number of
packets sent would grow, leading to
network traffic.
2. Less accuracy of prediction in data
traffic flow.
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.
Advantages:
1. the proposed RF-GRU-NTP model has
better performance in execution time
2. The proposed RF-GRU-NTP model has
better performance in prediction errors
than other algorithms which used for
network traffic prediction.

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Fig.2: System architecture
MODULES:
To implement aforementioned project we have
designed following modules
 Data exploration: using this module we
will load data into system
 Processing: Using the module we will
read data for processing
 Splitting data into train & test: using this
module data will be divided into train &
test
 Model generation: Building the model -
Deep Learning - CNN, CNN+LSTM,
LSTM, BiLSTM, RNN, GRU and CNN
with KFoldVaildation and Machine
Learning - Random Forest, Decision
Tree, KNN, Support Vector Machine
and Voting Classifier.
 User signup & login: Using this module
will get registration and login
 User input: Using this module will give
input for prediction
 Prediction: final predicted displayed
4. IMPLEMENTATION
ALGORITHMS:
CNN:
A CNN is a kind of network architecture for
deep learning algorithms and is specifically used
for image recognition and tasks that involve the
processing of pixel data. There are other types of
neural networks in deep learning, but for
identifying and recognizing objects, CNNs are
the network architecture of choice.
CNN+LSTM:
A CNN-LSTM model is a combination of CNN
layers that extract the feature from input data
and LSTMs layers to provide sequence
prediction. The CNN-LSTM is generally used
for activity recognition, image labeling, and
video labeling.
LSTM:
LSTM stands for long short-term memory
networks, used in the field of Deep Learning. It

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is a variety of recurrent neural networks (RNNs)
that are capable of learning long-term
dependencies, especially in sequence prediction
problems.
BiLSTM:
A bidirectional LSTM (BiLSTM) layer learns
bidirectional long-term dependencies between
time steps of time series or sequence data. These
dependencies can be useful when you want the
network to learn from the complete time series at
each time step.
RNN:
Recurrent neural networks (RNNs) are the state
of the art algorithm for sequential data and are
used by Apple's Siri and Google's voice search.
It is the first algorithm that remembers its input,
due to an internal memory, which makes it
perfectly suited for machine learning problems
that involve sequential data.
GRU:
Gated recurrent units (GRUs) are a gating
mechanism in recurrent neural networks,
introduced in 2014 by Kyunghyun Cho et al.
The GRU is like a long short-term memory
(LSTM) with a forget gate, but has fewer
parameters than LSTM, as it lacks an output
gate.
Random Forest:
Random forest is a Supervised Machine
Learning Algorithm that is used widely in
Classification and Regression problems. It
builds decision trees on different samples and
takes their majority vote for classification and
average in case of regression.
Decision Tree:
A decision tree is a non-parametric supervised
learning algorithm, which is utilized for both
classification and regression tasks. It has a
hierarchical, tree structure, which consists of a
root node, branches, internal nodes and leaf
nodes.
KNN:
The k-nearest neighbors algorithm, also known
as KNN or k-NN, is a non-parametric,
supervised learning classifier, which uses
proximity to make classifications or predictions
about the grouping of an individual data point.
Support Vector Machine:
Support Vector Machine(SVM) is a supervised
machine learning algorithm used for both
classification and regression. Though we say
regression problems as well its best suited for
classification. The objective of SVM algorithm
is to find a hyperplane in an N-dimensional
space that distinctly classifies the data points.
Voting Classifier:
A voting classifier is a machine learning
estimator that trains various base models or
estimators and predicts on the basis of
aggregating the findings of each base estimator.
The aggregating criteria can be combined
decision of voting for each estimator output.

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5. EXPERIMENTAL RESULTS
Fig.3: Home screen
Fig.4: User registration
Fig.5: user login
Fig.6: User input
Fig.7: Prediction result
6. CONCLUSION
In this paper, we proposed an RF-GRU-NTP
model with the aim of network traffic flow
prediction based on the traffic in the road and
network simultaneously. We divided our
research into three phases. In the first phase, we
focused on network traffic prediction. We used
the V2R dataset and considered the receiving
packets sent by vehicles to the RSUs as a
network parameter to predict network traffic
flow. Then, we tried different machine learning
algorithms like the RF, NB, KNN, and SVM
algorithms, and we evaluated them using some
classification metrics. After all evaluations, the
RF has the better performance to predict
network traffic flow while our target was
‘‘packet receiving.’’ In the second phase, we
tried to predict the road traffic flow using the
V2V dataset while our target was ‘‘sender
speed’’ to define the road traffic. We assumed
that the traffic would happen on the road if the
senders’ speed were less than 60 Km/h.
Therefore, we implemented different deep
learning algorithms, including the LSTM, GRU,
and Bi-LSTM. Finally, we evaluated the results
using some regression evaluation metrics,
which, based on the results we got, the GRU was
the fittest algorithm for road traffic prediction.
Then at the third phase, we implemented our

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target, which is network traffic flow considering
road traffic flow, by combining machine
learning and deep learning algorithms. For this
purpose, we combined V2V and V2R datasets,
and used the RF algorithm for feature selection.
We found the most important features, which
were ‘‘packet receive’’ and ‘‘receiver speed’’
that can affect ‘‘sender speed’’ and the network
traffic flow. Then by implementing the proposed
RF-GRU-NTP model, we predicted network
traffic flow. Therefore, we compared our results
with a pure algorithm like LSTM and Bi-LSTM
to make sure that the proposed model has good
results in network traffic flow prediction. The
main complexity of the proposed model was
combining two datasets in order to implementing
machine learning and deep learning algorithms
with the aim of network traffic prediction
considering different types of parameters. To the
best of our knowledge, this is the first research
that predicts the network traffic flow based on
road traffic flow. However, by growing up the
number of vehicles, the volume of produced data
by them would take shape of big data which in
our future work we will implement our proposed
model in big data.
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Biography of authors:
MAMIDISETTI YASWANTH was a M.Tech
scholar in Department of Computer Science and
Engineering, Swarnandhra College of
Engineering and Technology, Narsapuram,
AP,India. His was interested to do research in
machine learning and artificial intelligence.
TULASI RAJU NETHALA was a assistant
professor in Department of Computer Science
and Engineering, Swarnandhra College of
Engineering and Technology, Narsapuram, AP,
lndia. His current research work is machine
learning and artificial intelligence typically
explores the development of algorithms and
models that enable computers to learn from data
and make predictions or decisions. His work
often includes research in areas like neural
networks, natural language processing, computer
vision, and deep learning, aimed at solving real-
world problems through automated systems.
Dr.P.SRINIVASULU was a professor in
Department of Computer Science and
Engineering, Swarnandhra College of
Engineering and Technology, Narsapuram, AP,
lndia. His current research work is specializing
in machine learning and artificial intelligence
(AI) typically focuses on advanced
computational techniques that enable machines
to learn from data, identify patterns, and make

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decisions without being explicitly programmed.
He often have expertise in a variety of AI
subfields, including deep learning, neural
networks, natural language processing, and
reinforcement learning. His work may involve
developing new algorithms, applying AI to solve
real-world problems (like forecasting,
automation, or image recognition), and
exploring ethical concerns related to AI
deployment.

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