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By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data
Elmira Berjisian1, Alexander Bigazzi1, Hamed Barkh1
1Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Plos One
|December 20, 2023
Summary
Crowd-sourced GPS data can accurately estimate road grade for walking and cycling analysis. This method is more reliable than elevation models and offers a scalable solution for active transportation planning.
Area of Science:
- Geospatial analysis
- Transportation engineering
- Active transportation research
Background:
- Road grade significantly impacts walking and cycling behavior and outcomes.
- Precise road grade data is scarce, hindering travel information and analysis.
- Crowd-sourced GPS data offers a potential solution for generating road grade datasets.
Purpose of the Study:
- To examine the accuracy of crowd-sourced GPS data for estimating roadway grade profiles.
- To validate and modify existing elevation estimation methods for road grade analysis.
- To develop network-wide road grade datasets for enhanced travel analysis.
Main Methods:
- External validation of an elevation estimation method using field surveying data.
- Modification and evaluation of methods for road grade estimation.
- Aggregation of crowd-sourced GPS data using partition-around-medoid clustering, Savitzky-Golay filter, and DBSCAN.
- Implementation with an average of 150 GPS traces per location.
Main Results:
- Crowd-sourced GPS data provides relatively accurate road grade estimates.
- Grade estimates are more reliable than elevation estimates compared to alternative data sources.
- The most accurate method achieved a root mean square error (RMSE) of 1% road grade.
Conclusions:
- A modest amount of crowd-sourced GPS data can generate accurate road grade estimates.
- This method is superior to low-resolution elevation models but less accurate than LiDAR-derived data.
- Findings enable the incorporation of precise road grade information into street networks for active transportation analysis.
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