Prediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of the Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model

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dc.contributor.authorLi, Lin
dc.contributor.authorZhao, Jiahao
dc.contributor.authorCoskun, Serdar
dc.contributor.authorLangari, Reza
dc.date.accessioned2025-03-17T12:22:47Z
dc.date.available2025-03-17T12:22:47Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description2023 China Automation Congress, CAC 2023 -- 17 November 2023 through 19 November 2023 -- Chongqing -- 198194
dc.description.abstractThe integrated perception, planning, and control of intelligent transportation system has become a research hotspot in academia. In this context, the safe running of an intelligent vehicle highly depends on the accurate prediction of its driving environment and working conditions. Traffic flow parameters represent the macro characteristics of an intelligent vehicle driving environment. Accurate prediction of traffic flow parameters is beneficial to improve the accuracy of trajectory prediction of vehicles in the surrounding environment. As two kinds of traffic flow parameter prediction models: the macroscopic traffic flow model and the data-based learning model have their own characteristics. To fully take advantage of these two models, our work proposes a study of the macroscopic traffic flow model based on a nonlocal gas-kinetic (GKT) and a deep learning model based on convolutional neural networks-long short term memory. Real data sets US-101 and PeMS are used respectively to predict traffic flow density and velocity. Finally, the Kalman filter is employed to fuse the results of the two models. The experiment shows that the prediction accuracy of traffic flow density and velocity can be improved as compared with the Macroscopic traffic flow model and the data-based learning model. © 2023 IEEE.
dc.identifier.doi10.1109/CAC59555.2023.10450343
dc.identifier.endpage8131
dc.identifier.isbn979-835030375-9
dc.identifier.scopus2-s2.0-85189332819
dc.identifier.scopusqualityN/A
dc.identifier.startpage8126
dc.identifier.urihttps://doi.org/10.1109/CAC59555.2023.10450343
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1389
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2023 China Automation Congress, CAC 2023
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectConvolutional neural networks
dc.subjectKalman filter
dc.subjectLong short-term memory
dc.subjectNon-local GKT model
dc.subjectTraffic flow parameter prediction
dc.titlePrediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of the Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model
dc.typeConference Object

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