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Title: Short-term traffic state prediction from latent structures: Accuracy vs. efficiency

Abstract

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

Authors:
ORCiD logo [1];  [2];  [2];  [2];  [2];  [2];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Washington, Seattle, WA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1649384
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 111; Journal Issue: -; Journal ID: ISSN 0968-090X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS

Citation Formats

Li, Wan, Wang, Jingxing, Fan, Rong, Zhang, Yiran, Guo, Qiangqiang, Siddique, Choudhury, and Ban, Xuegang. Short-term traffic state prediction from latent structures: Accuracy vs. efficiency. United States: N. p., 2019. Web. https://doi.org/10.1016/j.trc.2019.12.007.
Li, Wan, Wang, Jingxing, Fan, Rong, Zhang, Yiran, Guo, Qiangqiang, Siddique, Choudhury, & Ban, Xuegang. Short-term traffic state prediction from latent structures: Accuracy vs. efficiency. United States. https://doi.org/10.1016/j.trc.2019.12.007
Li, Wan, Wang, Jingxing, Fan, Rong, Zhang, Yiran, Guo, Qiangqiang, Siddique, Choudhury, and Ban, Xuegang. Tue . "Short-term traffic state prediction from latent structures: Accuracy vs. efficiency". United States. https://doi.org/10.1016/j.trc.2019.12.007. https://www.osti.gov/servlets/purl/1649384.
@article{osti_1649384,
title = {Short-term traffic state prediction from latent structures: Accuracy vs. efficiency},
author = {Li, Wan and Wang, Jingxing and Fan, Rong and Zhang, Yiran and Guo, Qiangqiang and Siddique, Choudhury and Ban, Xuegang},
abstractNote = {Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.},
doi = {10.1016/j.trc.2019.12.007},
journal = {Transportation Research Part C: Emerging Technologies},
number = -,
volume = 111,
place = {United States},
year = {2019},
month = {12}
}

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