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Machine Learning Applications in Surface Transportation Systems: A Systematic Review.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Applications in Surface Transportation Systems: A Systematic Review./
Author:	Behrooz, Hojat.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	65 p.
Notes:	Source: Masters Abstracts International, Volume: 83-07.
Contained By:	Masters Abstracts International83-07.
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28864400
ISBN:	9798762196819

Machine Learning Applications in Surface Transportation Systems: A Systematic Review.
Behrooz, Hojat.

Machine Learning Applications in Surface Transportation Systems: A Systematic Review. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 65 p.

Source: Masters Abstracts International, Volume: 83-07.

Thesis (Master's)--Stevens Institute of Technology, 2021.

This item must not be sold to any third party vendors.

Surface transportation evolves through technological advancements using parallel knowledge areas such as machine learning (ML). While ML innovations have influenced many industries, the transportation industry has not yet taken full advantage of the ML and processing power capabilities for applied projects. To evaluate the gap, I utilize a systematic review approach to locate, categorize, and synthesize the principal concept of research papers in surface transportation systems using ML algorithms and decompose them into their fundamental elements. I explore more than 100 articles, literature review papers, and books. Results show while 80% of papers concentrate on forecasting, multi-layer perceptions, long-short term memory, supporting vector machine, random forest, K nearest neighbored, K number of centroids clustering (K-mean), supporting vector regression, and fuzzy logic are the most preferred ML algorithms.Sophisticated and powerful ML algorithms, including graph neural networks, generative adversarial network, and variational autoencoder have been minimally used. The root cause analysis shows a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide transportation datasets publicly. The transportation sector must offer an open-source platform to showcase the sector's concerns and build high-quality, high-frequency open-access integrated spatiotemporal datasets for outside knowledge areas, including artificial intelligence. The plat-form will enable ML experts to use algorithms on applied transportation problems to accelerate technology advancements while directly benefiting society and the economy by resolving mobility and safety issues. This dissertation provides the basis for an open-source platform that re-searchers can utilize to identify the existing literature where ML algorithms have been used to solve a surface transportation problem and identify research opportunities and gaps for further research and development.

ISBN: 9798762196819Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Deep learning

Machine Learning Applications in Surface Transportation Systems: A Systematic Review.
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500 $a Source: Masters Abstracts International, Volume: 83-07.
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520 $a Surface transportation evolves through technological advancements using parallel knowledge areas such as machine learning (ML). While ML innovations have influenced many industries, the transportation industry has not yet taken full advantage of the ML and processing power capabilities for applied projects. To evaluate the gap, I utilize a systematic review approach to locate, categorize, and synthesize the principal concept of research papers in surface transportation systems using ML algorithms and decompose them into their fundamental elements. I explore more than 100 articles, literature review papers, and books. Results show while 80% of papers concentrate on forecasting, multi-layer perceptions, long-short term memory, supporting vector machine, random forest, K nearest neighbored, K number of centroids clustering (K-mean), supporting vector regression, and fuzzy logic are the most preferred ML algorithms.Sophisticated and powerful ML algorithms, including graph neural networks, generative adversarial network, and variational autoencoder have been minimally used. The root cause analysis shows a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide transportation datasets publicly. The transportation sector must offer an open-source platform to showcase the sector's concerns and build high-quality, high-frequency open-access integrated spatiotemporal datasets for outside knowledge areas, including artificial intelligence. The plat-form will enable ML experts to use algorithms on applied transportation problems to accelerate technology advancements while directly benefiting society and the economy by resolving mobility and safety issues. This dissertation provides the basis for an open-source platform that re-searchers can utilize to identify the existing literature where ML algorithms have been used to solve a surface transportation problem and identify research opportunities and gaps for further research and development.
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