Model on empirically calibrating stochastic traffic flow fundamental diagram
This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to ge...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-12-01
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| Series: | Communications in Transportation Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772424721000159 |
| _version_ | 1819279121584226304 |
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| author | Shuaian Wang Xinyuan Chen Xiaobo Qu |
| author_facet | Shuaian Wang Xinyuan Chen Xiaobo Qu |
| author_sort | Shuaian Wang |
| collection | DOAJ |
| description | This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method. |
| first_indexed | 2024-12-24T00:22:52Z |
| format | Article |
| id | doaj.art-6ade3aa6d6d44d6fb9bc2c664e0cdbc4 |
| institution | Directory Open Access Journal |
| issn | 2772-4247 |
| language | English |
| last_indexed | 2024-12-24T00:22:52Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Communications in Transportation Research |
| spelling | doaj.art-6ade3aa6d6d44d6fb9bc2c664e0cdbc42022-12-21T17:24:32ZengElsevierCommunications in Transportation Research2772-42472021-12-011100015Model on empirically calibrating stochastic traffic flow fundamental diagramShuaian Wang0Xinyuan Chen1Xiaobo Qu2Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hung Hom, Hong Kong, SAR, China; Corresponding author.College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Corresponding author.Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, 41296, Sweden; Corresponding author. Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, 41296, Sweden.This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method.http://www.sciencedirect.com/science/article/pii/S2772424721000159Stochastic fundamental diagramSpeed distributionsTraffic control |
| spellingShingle | Shuaian Wang Xinyuan Chen Xiaobo Qu Model on empirically calibrating stochastic traffic flow fundamental diagram Communications in Transportation Research Stochastic fundamental diagram Speed distributions Traffic control |
| title | Model on empirically calibrating stochastic traffic flow fundamental diagram |
| title_full | Model on empirically calibrating stochastic traffic flow fundamental diagram |
| title_fullStr | Model on empirically calibrating stochastic traffic flow fundamental diagram |
| title_full_unstemmed | Model on empirically calibrating stochastic traffic flow fundamental diagram |
| title_short | Model on empirically calibrating stochastic traffic flow fundamental diagram |
| title_sort | model on empirically calibrating stochastic traffic flow fundamental diagram |
| topic | Stochastic fundamental diagram Speed distributions Traffic control |
| url | http://www.sciencedirect.com/science/article/pii/S2772424721000159 |
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