Traffic flow prediction models – A review of deep learning techniques
Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient tr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2021.2010510 |
| _version_ | 1797763068944449536 |
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| author | Anirudh Ameya Kashyap Shravan Raviraj Ananya Devarakonda Shamanth R Nayak K Santhosh K V Soumya J Bhat |
| author_facet | Anirudh Ameya Kashyap Shravan Raviraj Ananya Devarakonda Shamanth R Nayak K Santhosh K V Soumya J Bhat |
| author_sort | Anirudh Ameya Kashyap |
| collection | DOAJ |
| description | Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios. |
| first_indexed | 2024-03-12T19:37:17Z |
| format | Article |
| id | doaj.art-3dd178d0ea2f47028878fa2bb178c7a7 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T19:37:17Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-3dd178d0ea2f47028878fa2bb178c7a72023-08-02T04:06:18ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2021.20105102010510Traffic flow prediction models – A review of deep learning techniquesAnirudh Ameya Kashyap0Shravan RavirajAnanya DevarakondaShamanth R Nayak KSanthosh K VSoumya J Bhat1Manipal Institute of TechnologyModelling and VisualisationTraffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios.http://dx.doi.org/10.1080/23311916.2021.2010510deep learningdeep neural networkshybrid modelsintelligent transport systemtraffic flow predictionunsupervised learning |
| spellingShingle | Anirudh Ameya Kashyap Shravan Raviraj Ananya Devarakonda Shamanth R Nayak K Santhosh K V Soumya J Bhat Traffic flow prediction models – A review of deep learning techniques Cogent Engineering deep learning deep neural networks hybrid models intelligent transport system traffic flow prediction unsupervised learning |
| title | Traffic flow prediction models – A review of deep learning techniques |
| title_full | Traffic flow prediction models – A review of deep learning techniques |
| title_fullStr | Traffic flow prediction models – A review of deep learning techniques |
| title_full_unstemmed | Traffic flow prediction models – A review of deep learning techniques |
| title_short | Traffic flow prediction models – A review of deep learning techniques |
| title_sort | traffic flow prediction models a review of deep learning techniques |
| topic | deep learning deep neural networks hybrid models intelligent transport system traffic flow prediction unsupervised learning |
| url | http://dx.doi.org/10.1080/23311916.2021.2010510 |
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