932 resultados para KEEP CLEAR Pavement Markings
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KEEP CLEAR pavement markings are widely used at urban signalised intersections to indicate to drivers to avoid entering blocked intersections. For example, ‘Box junctions’ are most widely used in the United Kingdom and other European countries. However, in Australia, KEEP CLEAR markings are mostly used to improve access from side roads onto a main road, especially when the side road is very close to a signalised intersection. This paper aims to reveal how the KEEP CLEAR markings affect the dynamic performance of the queuing vehicles on the main road, where the side road access is near a signalised intersection. Raw traffic field data was collected from an intersection at the Gold Coast, Australia, and the Kanade–Lucas–Tomasi (KLT) feature tracker approach was used to extract dynamic vehicle data from the raw video footage. The data analysis reveals that the KEEP CLEAR markings generate positive effects on the queuing vehicles in discharge on the main road. This finding refutes the traditional viewpoint that the KEEP CLEAR pavement markings will cause delay for the queuing vehicles’ departure due to the enlarged queue spacing. Further studies are suggested in this paper as well.
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Federal Highway Administration, Washington, D.C.
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Federal Highway Administration, Office of Research, Development and Technology, Washington, D.C.
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Texas Department of Transportation, Austin
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Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.
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Federal Highway Administration, Implementation Division, Washington, D.C.
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Federal Highway Administration, Office of Research, Washington, D.C.
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Mode of access: Internet.
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Federal Highway Administration, Office of Research, Washington, D.C.
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Federal Highway Administration, Office of Research, Washington, D.C.
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Federal Highway Administration, Traffic Systems Division, Washington, D.C.
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Federal Highway Administration, Implementation Division, Washington, D.C.
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Ohio Department of Transportation, Columbus
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Federal Highway Administration, Washington, D.C.
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Ohio Department of Transportation, Columbus