SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:research.chalmers.se:5d39a50d-955c-44c6-acde-97ab1f3d98e1"
 

Sökning: id:"swepub:oai:research.chalmers.se:5d39a50d-955c-44c6-acde-97ab1f3d98e1" > A Dynamic Transform...

A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation

Fang, Shan (författare)
Changan University, Peoples R China
Yang, Lan (författare)
Changan University, Peoples R China
Zhao, Xiangmo (författare)
Changan University, Peoples R China
visa fler...
Wang, Wei (författare)
Changan University, Peoples R China
Xu, Zhigang (författare)
Changan University, Peoples R China
Wu, Guoyuan (författare)
University of California
Liu, Yang, 1991 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Qu, Xiaobo, 1983 (författare)
Tsinghua University
visa färre...
 (creator_code:org_t)
2024
2024
Engelska.
Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:1, s. 174-198
  • Tidskriftsartikel (refereegranskat)
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)

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy