The usefulness of GTOPO30 for deriving the roadway elevation is questionable because of its low resolution and the inherent vertical uncertainty of the multiple elevation data sources. GTOPO30 is based on several different source datasets and has variable absolute vertical accuracy. Presently, the quality of readily available elevation data varies by source and acquisition technology. Geological Survey (USGS NED), Global Digital Elevation Model (GDEM), and Light Detection and Ranging (LIDAR) elevation datasets. Currently available datasets include the global 30 arc-second elevation (GTOPO30) dataset, elevation dataset from Shuttle Radar Topography Mission (SRTM), National Elevation Dataset from the U.S. Over the past few decades, new data processing methods and data collection, storage, query, and visualization technologies have significantly increased the availability and accessibility of elevation data. The earliest method for collecting elevation data was to manually survey and draw isolines of elevation. Traditional roadway Geographic Information System (GIS) data, however, contain only two dimensional geo-coordinates, missing the elevation information in most cases. Such findings on the relationship between roadway grade and safety, fuel consumption, and network performance indicate that the availability and quality of roadway elevation and grade data will be a critical consideration in the development of a next generation “green” highway design strategy that integrates life cycle maintenance, operation, safety, and environmental cost in the planning stage. Boriboonsomsin and Barth showed that the optimal speed in terms of fuel efficiency changes with grade. Previous work has also identified non-linear relationships between roadway grade and fuel economy. Likewise, degradation of vehicle performance and sight distance at vertical alignments are often causes of recurrent congestion and vehicle collisions. The Highway Capacity Manual (HCM) 2010, for example, assigns values of heavy vehicle to passenger car equivalency factors based on grade changes. Previous research has shown that vehicle performance and fuel efficiency are significantly affected by roadway elevation changes. Roadway elevation data play a critical role in a wide range of transportation analysis and design applications including roadway geometric design, infrastructure construction, safety analysis, fuel consumption estimation, highway capacity analysis, and emergency evacuation planning. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. This study also compares the GE elevation data with the elevation raster data from the U.S. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. Roadway elevation data is critical for a variety of transportation analyses.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |