Empirical study of the effects of physics-guided machine learning on freeway traffic flow modelling: model comparisons using field data-Reference-Cited by-同舟云学术

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Empirical study of the effects of physics-guided machine learning on freeway traffic flow modelling: model comparisons using field data

Published:2023-10-20 Issue: Volume: Page:1-28
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Zhang Zhao¹,Yuan Yun¹^ORCID,Li Mingchen²,Lu Pan³,Yang Xianfeng Terry¹⁴

Affiliation:

1. Department of Civil & Environmental Engineering, University of Utah, Salt Lake City, UT, USA

2. Department of Electrical Engineering, University of South Florida, Tampa, FL, USA

3. Department of Transportation, Logistics, and Finance, North Dakota State University, Fargo, ND, USA

4. Department of Civil & Environmental Engineering, University of Maryland, College Park, MD, USA

Funder

National Science Foundation

Mountain-Plains Consortium

Publisher

Informa UK Limited

Subject

General Engineering,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2023.2264949

Reference63 articles.

1. Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

2. Data Fusion Based Hybrid Approach for the Estimation of Urban Arterial Travel Time

3. Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

4. Resurrection of "Second Order" Models of Traffic Flow

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