A survey on modern deep neural network for traffic prediction: Trends, methods and challenges

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since...

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Main Authors: TEDJOPUMOMO, David Alexander, BAO, Zhifeng, ZHENG, Baihua, CHOUDHURY, Farhana Murtaza, QIN, Kai
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/5995
https://ink.library.smu.edu.sg/context/sis_research/article/6998/viewcontent/Trajectory_Survey_TKDE.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-69982023-02-10T06:15:16Z A survey on modern deep neural network for traffic prediction: Trends, methods and challenges TEDJOPUMOMO, David Alexander BAO, Zhifeng ZHENG, Baihua CHOUDHURY, Farhana Murtaza QIN, Kai In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of them are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences between the different work, and finally provide a discussion regarding the challenges and future directions for this field. 2022-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5995 info:doi/10.1109/TKDE.2020.3001195 https://ink.library.smu.edu.sg/context/sis_research/article/6998/viewcontent/Trajectory_Survey_TKDE.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Deep Neural Network Deep Learning Traffic Flow Prediction Traffic Prediction Road Network Databases and Information Systems Numerical Analysis and Scientific Computing Transportation
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Deep Neural Network
Deep Learning
Traffic Flow Prediction
Traffic Prediction
Road Network
Databases and Information Systems
Numerical Analysis and Scientific Computing
Transportation
spellingShingle Deep Neural Network
Deep Learning
Traffic Flow Prediction
Traffic Prediction
Road Network
Databases and Information Systems
Numerical Analysis and Scientific Computing
Transportation
TEDJOPUMOMO, David Alexander
BAO, Zhifeng
ZHENG, Baihua
CHOUDHURY, Farhana Murtaza
QIN, Kai
A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
description In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of them are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences between the different work, and finally provide a discussion regarding the challenges and future directions for this field.
format text
author TEDJOPUMOMO, David Alexander
BAO, Zhifeng
ZHENG, Baihua
CHOUDHURY, Farhana Murtaza
QIN, Kai
author_facet TEDJOPUMOMO, David Alexander
BAO, Zhifeng
ZHENG, Baihua
CHOUDHURY, Farhana Murtaza
QIN, Kai
author_sort TEDJOPUMOMO, David Alexander
title A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
title_short A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
title_full A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
title_fullStr A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
title_full_unstemmed A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
title_sort survey on modern deep neural network for traffic prediction: trends, methods and challenges
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/5995
https://ink.library.smu.edu.sg/context/sis_research/article/6998/viewcontent/Trajectory_Survey_TKDE.pdf
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