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A Dynamic Transform...
A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation
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- Fang, Shan (författare)
- Changan University, Peoples R China
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- Yang, Lan (författare)
- Changan University, Peoples R China
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- Zhao, Xiangmo (författare)
- Changan University, Peoples R China
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- Wang, Wei (författare)
- Changan University, Peoples R China
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- Xu, Zhigang (författare)
- Changan University, Peoples R China
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- Wu, Guoyuan (författare)
- University of California
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- Liu, Yang, 1991 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Qu, Xiaobo, 1983 (författare)
- Tsinghua University
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:1, s. 174-198
- Relaterad länk:
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a balance between accuracy and efficiency. This article investigates the error variation when the traffic oscillation is simulated by the intelligent driver model (IDM). Then, it divides the traffic oscillation into four phases (coasting, deceleration, acceleration, and stationary) by using the space headway of multiple steps. To simulate traffic oscillation between multiple human-driven vehicles, a dynamic transformation CF model is proposed, which includes the long-time prediction submodel [modified sequence-to-sequence (Seq2seq)] model, short-time prediction submodel (Transformer), and their dynamic transformation strategy]. The first submodel is utilized to simulate the coasting and stationary phases, while the second submodel is utilized to simulate the acceleration and deceleration phases. The results of experiments indicated that compared to K-nearest neighbors, IDM, and Seq2seq CF models, the dynamic transformation CF model reduces the trajectory error by 60.79–66.69% in microscopic traffic flow simulations, 7.71–29.91% in mesoscopic traffic flow simulations, and 1.59–18.26% in macroscopic traffic flow simulations. Moreover, the runtime of the dynamic transformation CF model (Inference) decreased by 14.43–66.17% when simulating the large-scale traffic flow.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Oscillators
- TV
- Analytical models
- Predictive models
- Trajectory
- Vehicle dynamics
- Behavioral sciences
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Fang, Shan
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Yang, Lan
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Zhao, Xiangmo
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Wang, Wei
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Xu, Zhigang
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Wu, Guoyuan
-
visa fler...
-
Liu, Yang, 1991
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Qu, Xiaobo, 1983
-
visa färre...
- Om ämnet
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Samhällsbyggnads ...
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och Transportteknik ...
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Elektroteknik oc ...
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och Reglerteknik
- Artiklar i publikationen
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IEEE Intelligent ...
- Av lärosätet
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Chalmers tekniska högskola