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A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories
Wei Yang1, Tinghua Ai2, Wei Lu3
1School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. ywgismap@whu.edu.cn.
Sensors (Basel, Switzerland)
|April 20, 2018
Summary
This study introduces a new method using Delaunay triangulation to extract road boundaries from crowdsourced GPS traces. The approach enhances data quality for road information updates.
Area of Science:
- Geoinformatics
- Geographic Information Systems (GIS)
- Spatial Data Analysis
Background:
- Crowdsourcing trajectory data is crucial for updating road information.
- Existing methods struggle with low-frequency or noisy GPS data.
- Accurate road boundary extraction is essential for navigation and urban planning.
Purpose of the Study:
- To develop a novel approach for extracting road boundary information from crowdsourced vehicle traces.
- To improve the accuracy and reliability of road boundary extraction from GPS data.
- To address challenges posed by low-frequency data and complex road structures.
Main Methods:
- Data preprocessing: Optimization and interpolation of raw GPS traces.
- Feature extraction: Utilizing Delaunay triangulation and Voronoi diagrams to compute road boundary descriptors.
- Model development: Integrating boundary descriptors and trajectory movement features for detection.
- Extraction: Employing a regional growing method based on seed polygons.
Main Results:
- The proposed method effectively extracts road boundaries from low-frequency GPS traces.
- It demonstrates suitability for multi-type road structures and varying time intervals.
- Experimental results show higher quality boundary information compared to existing methods.
Conclusions:
- The novel Delaunay triangulation-based approach provides a robust solution for road boundary extraction from crowdsourced GPS data.
- The method enhances the accessibility and accuracy of road information.
- This technique is valuable for applications requiring up-to-date road network data.
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