Zobrazeno 1 - 10
of 1 194
pro vyhledávání: '"Traffic state"'
Publikováno v:
International Journal of Intelligent Computing and Cybernetics, 2024, Vol. 17, Issue 2, pp. 330-362.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJICC-09-2023-0269
Publikováno v:
In Transportation Research Part C February 2025 171
Autor:
Abirami Ashok, Bhargava Rama Chilukuri
Publikováno v:
IEEE Access, Vol 12, Pp 106211-106235 (2024)
For homogeneous traffic, where all vehicles are the same type, the traffic state is characterised by speed, flow, density, queue length, etc. In mixed traffic conditions, variations in static and kinematic characteristics among vehicles and the resul
Externí odkaz:
https://doaj.org/article/64edf64d1d6a46d8a902a55c81c421fe
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
Autor:
Xiaoli Deng, Yao Hu
Publikováno v:
Promet (Zagreb), Vol 35, Iss 5, Pp 681-697 (2023)
A macroscopic fundamental diagram (MFD) is an important basis for road network research. It describes the functional relationship between the average flow and average density of the road network. We proposed an MFD estimation method based on the traf
Externí odkaz:
https://doaj.org/article/4ad48d7b4fab439688ce919438192512
Akademický článek
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Publikováno v:
EURO Journal on Transportation and Logistics, Vol 13, Iss , Pp 100144- (2024)
Predicting freeway traffic states is, so far, based on predicting speeds or traffic volumes with various methodological approaches ranging from statistical modeling to deep learning. Traffic on freeways, however, follows specific patterns in space–
Externí odkaz:
https://doaj.org/article/097df00a226b48cdbb5a95740e77bb01
Publikováno v:
Future Transportation, Vol 3, Iss 3, Pp 840-857 (2023)
This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense
Externí odkaz:
https://doaj.org/article/b1360a8562224047a252a4d132a278ec
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