Latest revision as of 12:39, 12 February 2021

Abstract

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

Document type: Article

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

• http://downloads.hindawi.com/journals/jat/2018/3876841.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2018/3876841 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2018/3876841.pdf,

http://downloads.hindawi.com/journals/jat/2018/3876841.xml,

http://dx.doi.org/10.1155/2018/3876841 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1155/2018/3876841,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2018/3876841,

http://downloads.hindawi.com/journals/jat/2018/3876841.pdf,

https://academic.microsoft.com/#/detail/2901428328