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Automatic Road Feature Extraction from State-Owned Mobile LiDAR Data for Traffic Safety Analysis and Evaluation
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2022-01-20
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[PDF-812.80 KB]
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Edition:Final Report (September 2020 to January 2022)
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Abstract:The mobile LiDAR data is a promising solution to the existing road feature data gap based on its high accuracy and extended road network coverage. However, because of the lack of an automatic measuring tool, road features are currently read by data operators and measured manually in the cloud points. This project developed an ArcGIS toolbox—Automatic Road Feature Extraction from LiDAR (ARFEL)—that automatically extracts highly accurate road geometric features from mobile LiDAR data collected on roads. The tool integrated an intuitive user interface and a LiDAR-data processing engine as a toolbox of ArcGIS (the GIS software adopted by most traffic agencies). ARFEL takes the geolocated mobile LiDAR data as input, extracts road features required for safety analysis, and creates GIS data layers of road features. The performance of the tool was evaluated with different road types, and influencing factors were reported in this study.
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