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

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

Li, Wan / Wang, Jingxing / Fan, Rong / Zhang, Yiran / Guo, Qiangqiang / Siddique, Choudhury / Ban, Xuegang (Jeff)

Highlights • A PLS-based model is proposed for short-term traffic state prediction. • PLS can produce comparable results to deep learning models in a more efficient manner. • PLS model can learn day-to-day variations and spatial dependencies of traffic states. • PLS model is validated by real-world traffic data.

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.

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Dokumentinformationen

Titel :

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

Beteiligte:

Li, Wan (Autor:in) / Wang, Jingxing (Autor:in) / Fan, Rong (Autor:in) / Zhang, Yiran (Autor:in) / Guo, Qiangqiang (Autor:in) / Siddique, Choudhury (Autor:in) / Ban, Xuegang (Jeff) (Autor:in)

Erschienen in:

Tramsportation Research, Part C: Emerging Technologies ; 111 ; 72-90

Erscheinungsdatum :

2019-12-15

Format / Umfang :

19 pages

ISSN :

0968-090X

DOI :

https://doi.org/10.1016/j.trc.2019.12.007

Medientyp :

Aufsatz (Zeitschrift)