Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106159
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | en_US |
dc.contributor | Department of Logistics and Maritime Studies | en_US |
dc.creator | Shangguan, YD | en_US |
dc.creator | Tian, XC | en_US |
dc.creator | Jin, S | en_US |
dc.creator | Gao, K | en_US |
dc.creator | Hu, XS | en_US |
dc.creator | Yi, W | en_US |
dc.creator | Guo, Y | en_US |
dc.creator | Wang, SA | en_US |
dc.date.accessioned | 2024-05-03T00:45:32Z | - |
dc.date.available | 2024-05-03T00:45:32Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106159 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Shangguan Y, Tian X, Jin S, Gao K, Hu X, Yi W, Guo Y, Wang S. On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models. Mathematics. 2023; 11(16):3460 is available at https://dx.doi.org/10.3390/math11163460. | en_US |
dc.subject | Speed and density relationship | en_US |
dc.subject | Linear regression | en_US |
dc.subject | Quadratic programming | en_US |
dc.title | On the fundamental diagram for freeway traffic : exploring the lower bound of the fitting error and correcting the generalized linear regression models | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 11 | en_US |
dc.identifier.issue | 16 | en_US |
dc.identifier.doi | 10.3390/math11163460 | en_US |
dcterms.abstract | 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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Mathematics, Aug. 2023, v. 11, no. 16, 3460 | en_US |
dcterms.isPartOf | Mathematics | en_US |
dcterms.issued | 2023-08 | - |
dc.identifier.isi | WOS:001055991300001 | - |
dc.identifier.eissn | 2227-7390 | en_US |
dc.identifier.artn | 3460 | en_US |
dc.description.validate | 202405 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC)) | en_US |
dc.description.fundingText | JPI Urban Europe and Energimyndigheten | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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mathematics-11-03460-v3.pdf | 4.03 MB | Adobe PDF | View/Open |
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