A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos and Dionisis Kandris
Additional contact information
Fotios Zantalis: TelSiP 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, Greece
Grigorios Koulouras: TelSiP 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, Greece
Sotiris Karabetsos: TelSiP 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, Greece
Dionisis Kandris: microSENSES 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, Greece

Future Internet, 2019, vol. 11, issue 4, 1-23

Abstract: 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.

Keywords: internet of things; machine learning; smart transportation; smart city; intelligent transportation systems; big data (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/1999-5903/11/4/94/pdf (application/pdf)
https://www.mdpi.com/1999-5903/11/4/94/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:11:y:2019:i:4:p:94-:d:221586

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().