Zobrazeno 1 - 10
of 284
pro vyhledávání: '"traffic speed prediction"'
Publikováno v:
Promet (Zagreb), Vol 36, Iss 4, Pp 765-778 (2024)
Predicting traffic speed accurately and in real-time is crucial for the development of smart transportation systems. Given the nonlinear and stochastic nature of vehicle data, integrating diverse spatio-temporal data sources with the Improved Particl
Externí odkaz:
https://doaj.org/article/68779031c8834330b694755c29f377e7
Publikováno v:
IEEE Access, Vol 12, Pp 15222-15235 (2024)
Advanced technologies, driven by extensive data analysis, support the concept of intelligent cities, which aim to enhance the quality of people’s lives, minimize the consumption of energy, reduce pollution, and promote economic growth. The transpor
Externí odkaz:
https://doaj.org/article/ac13fec56da440d0bbc9f9f0740b242a
Autor:
Seung Bae Jeon, Myeong-Hun Jeong
Publikováno v:
Applied Sciences, Vol 14, Iss 14, p 6102 (2024)
The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic
Externí odkaz:
https://doaj.org/article/70914331d38345e7b3d87b9cc24ef4c2
Publikováno v:
Symmetry, Vol 16, Iss 3, p 308 (2024)
Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an un
Externí odkaz:
https://doaj.org/article/a93b16eab3124062985377d6b8429e47
Autor:
Eun Hak Lee
Publikováno v:
IEEE Access, Vol 11, Pp 113217-113226 (2023)
As the intelligent transportation system has been introduced, traffic speed prediction has become one of the foremost challenging tasks within complex urban road networks. The main idea of this study is to identify links that have a significant impac
Externí odkaz:
https://doaj.org/article/1c57f88d52284037929292c9a9baa1bb
Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 10, Pp 8996-9010 (2022)
The daily long-term traffic prediction is an important urban computing issue, and can give users a global insight into traffic. Accurate traffic prediction is conducive to rational route planning and efficient traffic resource allocation. However, it
Externí odkaz:
https://doaj.org/article/8566d276ed3845bd8f14996c7fb43900
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Archana Nigam, Sanjay Srivastava
Publikováno v:
Multimodal Transportation, Vol 2, Iss 1, Pp 100052- (2023)
Adverse weather conditions like fog, rainfall, and snowfall affect the driver’s visibility, mobility of vehicle, and road capacity. Accurate prediction of the macroscopic traffic stream variables such as speed and flow is essential for traffic oper
Externí odkaz:
https://doaj.org/article/7bd31052682f4eea98756c6f331ff3f0