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On the Fundamental ...
On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models
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- Shangguan, Yidan (author)
- Hong Kong Polytechnic University
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- Tian, Xuecheng (author)
- Hong Kong Polytechnic University
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- Jin, Sheng (author)
- Zhejiang University
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- Gao, Kun, 1993 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Hu, Xiaosong (author)
- Chongqing University
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- Yi, Wen (author)
- Hong Kong Polytechnic University
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- Guo, Yu (author)
- Hong Kong Polytechnic University
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- Wang, Shuaian (author)
- Hong Kong Polytechnic University
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(creator_code:org_t)
- 2023
- 2023
- English.
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In: MATHEMATICS. - 2227-7390. ; 11:16
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https://research.cha...
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https://doi.org/10.3...
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Abstract
Subject headings
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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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Keyword
- 90-10
- linear regression
- speed and density relationship
- quadratic programming
Publication and Content Type
- art (subject category)
- ref (subject category)
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