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On the Fundamental ...
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Shangguan, YidanHong Kong Polytechnic University
(author)
On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models
- Article/chapterEnglish2023
Publisher, publication year, extent ...
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2023
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electronicrdacarrier
Numbers
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LIBRIS-ID:oai:research.chalmers.se:a5ccc77e-db8d-4fe5-809c-b21f11333220
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https://research.chalmers.se/publication/537498URI
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https://doi.org/10.3390/math11163460DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:art swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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In traffic flow, the relationship between speed and density exhibits decreasing monotonicity and continuity, which is characterized by various models such as the Greenshields and Greenberg models. However, some existing models, i.e., the Underwood and Northwestern models, introduce bias by incorrectly utilizing linear regression for parameter calibration. Furthermore, the lower bound of the fitting errors for all these models remains unknown. To address above issues, this study first proves the bias associated with using linear regression in handling the Underwood and Northwestern models and corrects it, resulting in a significantly lower mean squared error (MSE). Second, a quadratic programming model is developed to obtain the lower bound of the MSE for these existing models. The relative gaps between the MSEs of existing models and the lower bound indicate that the existing models still have a lot of potential for improvement.
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Tian, XuechengHong Kong Polytechnic University
(author)
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Jin, ShengZhejiang University
(author)
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Gao, Kun,1993Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)gkun
(author)
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Hu, XiaosongChongqing University
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Yi, WenHong Kong Polytechnic University
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Guo, YuHong Kong Polytechnic University
(author)
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Wang, ShuaianHong Kong Polytechnic University
(author)
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Hong Kong Polytechnic UniversityZhejiang University
(creator_code:org_t)
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In:MATHEMATICS11:162227-7390
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