Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

Abstract The development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enha...

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Autores principales:	Noor Afiza Mat Razali, Nuraini Shamsaimon, Khairul Khalil Ishak, Suzaimah Ramli, Mohd Fahmi Mohamad Amran, Sazali Sukardi
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Traffic flow Congestion prediction Machine learning Deep learning Connected vehicles Hybrid machine learning Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/608a0252a4a74f7e86b80097f6036316
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spelling	oai:doaj.org-article:608a0252a4a74f7e86b80097f60363162021-12-05T12:03:21ZGap, techniques and evaluation: traffic flow prediction using machine learning and deep learning10.1186/s40537-021-00542-72196-1115https://doaj.org/article/608a0252a4a74f7e86b80097f60363162021-12-01T00:00:00Zhttps://doi.org/10.1186/s40537-021-00542-7https://doaj.org/toc/2196-1115Abstract The development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.Noor Afiza Mat RazaliNuraini ShamsaimonKhairul Khalil IshakSuzaimah RamliMohd Fahmi Mohamad AmranSazali SukardiSpringerOpenarticleTraffic flowCongestion predictionMachine learningDeep learningConnected vehiclesHybrid machine learningComputer engineering. Computer hardwareTK7885-7895Information technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENJournal of Big Data, Vol 8, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Traffic flow Congestion prediction Machine learning Deep learning Connected vehicles Hybrid machine learning Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Traffic flow Congestion prediction Machine learning Deep learning Connected vehicles Hybrid machine learning Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Noor Afiza Mat Razali Nuraini Shamsaimon Khairul Khalil Ishak Suzaimah Ramli Mohd Fahmi Mohamad Amran Sazali Sukardi Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
description	Abstract The development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.
format	article
author	Noor Afiza Mat Razali Nuraini Shamsaimon Khairul Khalil Ishak Suzaimah Ramli Mohd Fahmi Mohamad Amran Sazali Sukardi
author_facet	Noor Afiza Mat Razali Nuraini Shamsaimon Khairul Khalil Ishak Suzaimah Ramli Mohd Fahmi Mohamad Amran Sazali Sukardi
author_sort	Noor Afiza Mat Razali
title	Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
title_short	Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
title_full	Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
title_fullStr	Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
title_full_unstemmed	Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
title_sort	gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/608a0252a4a74f7e86b80097f6036316
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Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

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