A Review of Machine Learning and IoT in Smart Transportation
With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/11/4/94 |
| _version_ | 1818997950188093440 |
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| author | Fotios Zantalis Grigorios Koulouras Sotiris Karabetsos Dionisis Kandris |
| author_facet | Fotios Zantalis Grigorios Koulouras Sotiris Karabetsos Dionisis Kandris |
| author_sort | Fotios Zantalis |
| collection | DOAJ |
| description | With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers. |
| first_indexed | 2024-12-20T21:53:46Z |
| format | Article |
| id | doaj.art-e20afb04a8b44ce09998ccc1c35cd7da |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-12-20T21:53:46Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-e20afb04a8b44ce09998ccc1c35cd7da2022-12-21T19:25:30ZengMDPI AGFuture Internet1999-59032019-04-011149410.3390/fi11040094fi11040094A Review of Machine Learning and IoT in Smart TransportationFotios Zantalis0Grigorios Koulouras1Sotiris Karabetsos2Dionisis Kandris3TelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, GreeceTelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, GreeceTelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, GreecemicroSENSES Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, GreeceWith the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.https://www.mdpi.com/1999-5903/11/4/94internet of thingsmachine learningsmart transportationsmart cityintelligent transportation systemsbig data |
| spellingShingle | Fotios Zantalis Grigorios Koulouras Sotiris Karabetsos Dionisis Kandris A Review of Machine Learning and IoT in Smart Transportation Future Internet internet of things machine learning smart transportation smart city intelligent transportation systems big data |
| title | A Review of Machine Learning and IoT in Smart Transportation |
| title_full | A Review of Machine Learning and IoT in Smart Transportation |
| title_fullStr | A Review of Machine Learning and IoT in Smart Transportation |
| title_full_unstemmed | A Review of Machine Learning and IoT in Smart Transportation |
| title_short | A Review of Machine Learning and IoT in Smart Transportation |
| title_sort | review of machine learning and iot in smart transportation |
| topic | internet of things machine learning smart transportation smart city intelligent transportation systems big data |
| url | https://www.mdpi.com/1999-5903/11/4/94 |
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