Abstract

A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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The different versions of the original document can be found in:

https://doaj.org/toc/1867-0717,
https://doaj.org/toc/1866-8887 under the license cc-by
http://link.springer.com/article/10.1186/s12544-019-0345-9/fulltext.html,
http://dx.doi.org/10.1186/s12544-019-0345-9
https://etrr.springeropen.com/articles/10.1186/s12544-019-0345-9,
https://link.springer.com/article/10.1186/s12544-019-0345-9,
https://academic.microsoft.com/#/detail/2914716043 under the license https://creativecommons.org/licenses/by/4.0
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1186/s12544-019-0345-9
Licence: Other

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