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

Journal Article · Tue Dec 24 04:00:00 EST 2019 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2019.12.007· OSTI ID:1649384

Li, Wan ^[1]; Wang, Jingxing ^[2]; Fan, Rong ^[2]; Zhang, Yiran ^[2]; Guo, Qiangqiang ^[2]; Siddique, Choudhury ^[2]; Ban, Xuegang ^[2]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Univ. of Washington, Seattle, WA (United States)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1649384
• Journal Information: Transportation Research Part C: Emerging Technologies, Journal Name: Transportation Research Part C: Emerging Technologies Journal Issue: - Vol. 111; ISSN 0968-090X
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS