Automatic Road Feature Extraction from State-Owned Mobile LiDAR Data for Traffic Safety Analysis and Evaluation

Xu, Hao; Weston, James; Liu, Hongchao

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Automatic Road Feature Extraction from State-Owned Mobile LiDAR Data for Traffic Safety Analysis and Evaluation

2022-01-20
By Xu, Hao ; Weston, James ; Liu, Hongchao

File Language:

English

Details

Creators:

Xu, Hao ; Weston, James ; Liu, Hongchao
Corporate Creators:

Regional Transportation Commission of Washoe County ; University of Nevada, Reno ; Texas Tech University
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report (September 2020 to January 2022)
Corporate Publisher:

Regional Transportation Commission of Washoe County
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.
Format:

PDF
Funding:

69A34520501080620
Download URL:

https://rosap.ntl.bts.gov/view/dot/60938/dot_60938_DS1.pdf
File Type:

[PDF - 812.80 KB]
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:75b7b9b9e23ee4d7c904a1732433684a12ab0e8b18f36298cac19d802cdc1718d697e05d65167eecdb32702ff0123ad057cc1e0c27a16ede6ed1059677ab98a5

File Language:

English

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