Zobrazeno 1 - 10
of 567
pro vyhledávání: '"TRAFFIC STATE ESTIMATION"'
Publikováno v:
IEEE Access, Vol 12, Pp 65869-65882 (2024)
Traffic management systems have primarily relied on live traffic sensors for real-time traffic guidance. However, this dependence often results in uneven service delivery due to the limited scope of sensor coverage or potential sensor failures. This
Externí odkaz:
https://doaj.org/article/2132206e42dd4e52910fc4657bbf4f18
Autor:
Lisa Kessler, Klaus Bogenberger
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 5, Pp 29-40 (2024)
This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referr
Externí odkaz:
https://doaj.org/article/e0f1ccb23b3f4cefa2680cddec5192a6
Publikováno v:
IET Intelligent Transport Systems, Vol 17, Iss 4, Pp 804-824 (2023)
Abstract Traffic flow/volume data are commonly used to calibrate and validate traffic simulation models. However, these data are generally obtained from stationary sensors (e.g. loop detectors), which are expensive to install and maintain and cover a
Externí odkaz:
https://doaj.org/article/16e831edd8764026a4caeb8788c3aa47
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Urban Road Traffic Spatiotemporal State Estimation Based on Multivariate Phase Space–LSTM Prediction
Publikováno v:
Applied Sciences, Vol 13, Iss 21, p 12079 (2023)
The road traffic state is usually analyzed from a temporal and macroscopic perspective; however, traffic flow parameters, such as density and spacing, can explain the evolution of traffic states from the microscopic perspective and the spatial distri
Externí odkaz:
https://doaj.org/article/77918da3c27b4f838d73a7625e660f22
Publikováno v:
Machines, Vol 11, Iss 12, p 1058 (2023)
Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Henc
Externí odkaz:
https://doaj.org/article/2c1acd5264c84f0abe0032dcb0d93d1b
Autor:
Archie J. Huang, Shaurya Agarwal
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 3, Pp 503-518 (2022)
We present a physics-informed deep learning (PIDL) approach to tackle the challenge of data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep learning (DL) neural network with the knowledge of traffic flow theory to
Externí odkaz:
https://doaj.org/article/73ec527f6dbf423899fbfcfb483c8ebe
Akademický článek
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Autor:
Kashinath Shafiza Ariffin, Mostafa Salama A., Lim David, Mustapha Aida, Hafit Hanayanti, Darman Rozanawati
Publikováno v:
Journal of Intelligent Systems, Vol 30, Iss 1, Pp 947-965 (2021)
Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework enta
Externí odkaz:
https://doaj.org/article/01a1a295371249048dda7a997654376b