Deep Learning-Based Algorithms in Solving Traffic Jam in Smart
Transportation
Jingru Deng
a
Data Science and Big Data Technology, South China University of Technology, Guangzhou, China
Keywords: Traffic Flow Prediction, Machine Learning, Smart Transportation, Artificial Neural Network.
Abstract: With the increase in the number of private cars, people's travel has become more convenient. However,
currently artificial intelligence has not played a particularly significant role in solving traffic congestion.
Traffic congestion has lots of disadvantage: it can increase people's commuting time; it can increase travel
costs and so on. This article aims at providing an overview of some possible ways in predicting traffic flow,
ranging from machine-learning based method to deep learning methods, which give a feasible scheme for the
traffic system to moderate the traffic light time and better smooth the traffic flow. In the discussion part, the
article analysis that machine-learning based methods still have shortcomings in terms of interpretability and
adaptability. In the future, the predicting method will be improved by adapting SHapley Additive exPlanations
and domain adaptation. Also, computational speed also needs to be taken into account, with the use of parallel
computing. The author proposes a framework that focuses on parallel computing. This article provides a good
overview of the field of predicting traffic flow.
1 INTRODUCTION
With the rapid development of Internet of Things
(IOT), transportation facilities in the city are
connected to the traffic network, giving rise to the
concept of smart transportation. Smart transportation
is considered a general term that encompasses route
optimization, parking, street lighting, accident
prevention and detection, road anomalies, and
infrastructure applications (Zantalis, 2019). Some
apps e.g. Gaode in the field of transportation, it can
help users optimize the route to destination. They can
easily obtain the location information of a certain
vehicle and predict the time it will take to reach a
certain station by installing mobile devices on public
transportation. Meanwhile, the increase in the number
of cars puts pressure on traffic system. In the urban
area, it is often easy to encounter traffic congestion at
intersections due to excessive traffic flow or
accidents. Traffic jams are detrimental to people’s
lives in many aspects. It takes more time for people
to wait and consumes more gasoline. Thus, how to
solve traffic congestion in a better way is what needs
to be considered.
a
https://orcid.org/0009-0009-2052-1122
Artificial intelligence technology includes data
excavation and intelligent algorithms, which means
that it can search for useful information through its
internal algorithm and can solve certain types of
issues. In real situation, collecting information on
vehicles can be regarded as data excavation, while
getting a solution for traffic jams is what the artificial
algorithm can do. Therefore, artificial intelligence
can be well integrated with intelligent transportation,
especially, the prediction of traffic flow.
In the aspect of predicting the traffic flow through
artificial intelligence, many researchers have carried
out some relative works. In early research,
researchers employed mathematical statistics or
traditional machine learning method to build traffic
flow prediction model. For example, Wang upgraded
the Cellular Automata model suitable for describing
the spread of traffic congestion by taking certain set
called “Squeezing to change lanes” into consideration
(Wang, 2010). Castro et al. utilized the advantages of
online learning methods to predict traffic flow under
different conditions, and thus proposed a supervised
statistical learning technique called Online
Regression Support Vector Machine (Castro-Neto,
2009). However, the conventional machine learning
Deng, J.
Deep Learning-Based Algorithms in Solving Traffic Jam in Smart Transportation.
DOI: 10.5220/0012958500004508
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 541-545
ISBN: 978-989-758-713-9
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
541
method has various disadvantages. Traditional
machine learning method requires people to establish
eigenvalues and has a significant workload in
identifying feature values. Moreover, it is impossible
to exhaustively list all the characteristics. With the
appearance of neural network, deep learning can find
out the rules without establishing eigenvalues for
them. Thus, researchers tried using deep learning to
tackle the prediction issue. Liang et al. improved the
accuracy of predicting urban traffic flow through
deep learning, specifically the Spatio-Temporal
Relation Network (STRN), which consists of
Convolutional Neural Network (CNN) (Liang, 2021).
Mao et al. established a platform based on Spark and
Hadoop to achieve real-time monitoring of urban
traffic flow. This platform also has some other
functions, such as the ability to analyze specific types
(Mao, 2022). Due to the rapid development of this
field and its importance, it is necessary to make a
comprehensive overview of this direction.
The rest of the paper is constructed as follows. In
Section 2, The article will provide a thorough
explanation of the procedures used by others in their
workflow, such as traditional machine learning
method and Artificial Neural Network (ANN). In
section 3, the author will discuss shortcomings of
current researches and future development directions.
Finally, section 4 sums up the paper.
2 METHOD
2.1 Framework of Machine
Learning-Based Methods in
Predicting Traffic Flow
Machine learning process for traffic flow prediction
shown in Figure 1 can be loosely broken down into
six steps. The first step is data collection, which
obtains various kind of information e.g. weather,
historical traffic flow, time, holidays, events. Then, in
the data preprocessing progress, data will experience
steps like cleaning, normalization for the reason that
different data are of different importance for
predicting traffic flow forecasts, and there might be
some abnormal values. This step aims at enhancing
the quality of data. In the next step, a model that can
predict the traffic flow is constructed, consisting of
suitable algorithms. Algorithms such as Random
Forest, K-NN are prevailing in traditional machine
learning. In the fourth step, when the model is trained,
whose parameters is dynamically moderating in order
to get a more precise prediction value of the traffic
flow. In the fifth step, when the model is well trained,
it will be accessed by some index like some metrics
to figure out whether the model’s parameter or
algorithm need changing or not. Finally, the model
can be deployed and help police visualize where
traffic jam will take place. The section left describes
the details of model building and training.
Figure 1: The workflow of machine learning-based
methods in predicting traffic flow (Photo/ Picture
credit: Original).
2.2 Machine Learning Algorithms
2.2.1 Random Forest-Based Prediction
When it comes to model building and training
progress, Chen et al. proposed Random Forest
Prediction model shown in Figure 2 for traffic flow
prediction (Chen, 2018). Random Forest is a
combined algorithm that uses multiple tree learners
for classification and regression prediction. The
sample to be classified is denoted as Xt=Xt1, Xt2,
Xt3, where Xt1, Xt2, and Xt3 are mapped to the
predicted traffic flow, velocity, and occupancy of
target section in the upcoming period. Icons of
different shapes and colors represent distinct traffic
situations (Chen, 2018). When a sample to be
classified is given, each decision tree calculates the
percentage of different classes in the training sample
at the leaf node where the sample falls to determine
the classification result of the sample (Zhou, 2019).
Finally, the classification results from all the decision
trees are collected and voted. The one which has the
highest number of votes is the prediction of the traffic
state.
Figure 2: The framework of Random Forest Prediction
Model for traffic flow prediction (Photo/Picture credit:
Original).
2.2.2 K-Nearest Neighbour Classification
(K-NN)
K-NN shown in Figure 3 is another algorithm that can
be applied to build the model. K-NN requires 5
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542
elements: Complete historical datasets, state vector,
distant standard, K nearest neighbour matching and
prediction algorithm (Cover, 1967). The first step is
to construct a historical database from data that has
been preprocessed. The historical database needs to
be large enough and has representative data. The
second step is to define the state vector. Next, select
a distance metric criterion. Then, search for K
neighbours of the current vector and put it into
prediction algorithm to get a prediction result. The
prediction algorithm can be divided into 2 parts:
arithmetic mean prediction algorithm and weighted
prediction algorithm (Lin, 2015).
Figure 3: The framework of K-NN for traffic flow
prediction (Photo/Picture credit: Original).
2.3 Deep Learning Methods
What the framework of deep learning methods look
like is quite similar to that of the machine learning
methods. However, since deep learning can extract
abstract features and learn automatically in the model
building progress, it is better than classical machine
learning.
2.3.1 Convolutional Neural Network
CNN is characterized by its deep feedforward neural
network that utilizes convolutional computation and
deep structure (Qiu, 2022). Ma et al. migrate the
application of CNN to the field of traffic forecasting
shown in Figure 4. In the data preprocessing process,
the traffic flow is transformed into graph. There are 2
main steps in the model building process. Firstly,
images that have spatiotemporal characteristics and
are produced by traffic network are the main resource
of the model input (Ma, 2017). Secondly, both the
convolutional layer and pooling layers, which are two
of the most crucial components of the CNN model,
extract features from the input and integrate with each
other at the same time (Ma, 2017). Finally, through a
fully connected layer, vectors representing features
are transformed into model outputs (Ma, 2017).
Figure 4: The framework of CNN for traffic flow prediction
(Photo/Picture credit: Original).
2.3.2 Spatio-Temporal Relation Network
(STRN)
STRN shown in Figure 5 is another model that can
predict traffic flow precisely (Liang, 2021). This
model collects traffic flow at different times and
selects key time steps. Then, it preprocesses the data
by dividing the data into three categories: closeness,
period and trend. These three categories are processed
in a convolutional layer. Also, external factors like
weather, POI&RN are preprocessed through Meta
Learner. Then, all of the preprocessed data are sent to
the backbone network and the GloNet for model
learning.
Figure 5: The framework of STRN for traffic flow
prediction (Photo/Picture credit: Original).
3 DISCUSSIONS
Although significant progress has been achieved in
this field, the deep learning-based model has some
drawbacks. The interpretability of deep learning is
relatively poor. It can provide people a prediction
result, but it is challenging for people to understand
why it gets such a result. Especially when error occurs
in prediction, people have no way to find out the
reason. Also, the applicability needs to be further
improved. In previous researches, the prediction
models are used in certain roads or areas. If the
circumstance changes, for example, the prediction
model needs to be applied in a more complicated
situation, it may cause error. Furthermore, although
currently there have been many traffic flow prediction
models, their computing speed is very important since
it need to predict the real-time traffic flow.
There are some ways that can be considered to
tackle the issues above in the future. Firstly, the
SHapley Additive exPlanations (SHAP) can com-
pensate for the shortcomings of poor interpretability
because it can quantify the contribution of each factor
to the prediction results and help understand the
decision process of the model. The Domain
Adaptation can tackle the applicability issue through
feature-based and instance-based approach. Finally, it
is possible to take parallel computing into
consideration to solve the computing speed problem.
Deep Learning-Based Algorithms in Solving Traffic Jam in Smart Transportation
543
Figure 6: The potential framework in the future (Photo/Picture credit: Original).
A possible framework shown in Figure 6 is proposed
in this study. The model can be splited into three
layers. The first layer is data collection layer, then
comes the computational and analysis layer and the
last layer is the application layer. In the first layer, it
is required to collect external factors and past
situation and preprocess them. Then, in the
computational and analysis layer, since external
factors, closeness, period, and trend can be
considered independent of each other, parallel
computing is feasible for them. These four factors are
placed on the four computing nodes of Spark for
parallel computation and processed through
convolutional layers. However, there are many
redundant and irrelevant information in the obtained
four processing results. Therefore, matrix joint
analysis can be applied to capture key features and
extract effective information for these four results.
Input the results into Graph Transformer for parallel
computing, while in the previous research it will be
processed and analyzed by Backbone Network and
Glonet, but this method is difficult to parallelize. Next,
by using reinforcement learning models with the goal
of minimizing congestion, the optimized future traffic
light duration is obtained. The last layer is the
application layer, it depends on whether the
transportation department adopts this optimization
plan.
4 CONCLUSIONS
In this work, the article provided a review of machine
learning in traffic flow prediction. It summarizes
some traditional machine-based methods and deep
learning methods in predicting traffic flow. The
methods of this research include Random Forest, K-
NN, CNN and STRN. Through the discussion and
analysis, currently the machine-learning method still
has some disadvantages such as interpretability,
applicability and computing speed. This article has
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544
some drawbacks because some classic models are out
of consideration. In the future, further study plans to
carry out a more comprehensive job and incorporate
more algorithms related to traffic flow prediction into
the system.
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