Cycling in three dimensions : developing road grade information for bicycle travel analysis
Bibliographic record
Abstract
Road grade is a major factor influencing cyclist physiology and travel decisions. Research studying cycling and other non-motorized transportation modes often use coarse elevation data sources to obtain the necessary grade information. In addition, routing applications such as Google Maps, Strava and RideWithGPS append the GPS data collected with elevation data from the coarse elevation datasets which can be inaccurate and inadequate. The objective of this research is to determine the best methods of obtaining road grade information on a network scale for bicycle travel analysis and to understand the limitations of the coarse data sources. Multiple elevation data sources, high resolution and coarse, are collected for the city of Vancouver, BC Canada. Different road grade estimation algorithms are then applied to the data sources at eight locations in the city where ground truth elevation data were surveyed using a total station. Different cycling performance measures were used to compare the elevation and road grade estimates of the locations to identify the data sources that accurately represent the true ground elevation for cycling analysis. Finally, the elevated structures in the City of Vancouver are characterized to help infer grade information in the absence of high resolution data sources. Results show that elevation data collected from Light Detection and Ranging (LiDAR) are the most accurate for elevated and non-elevated roads with mean absolute error in the elevation not exceeding 0.6 meters. Additionally, road grades derived from LiDAR data sources were closest to measured grade data. In the absence of LiDAR, coarse data sources can provide adequate grade estimates for cycling analysis on non-elevated structures. However, on elevated structures, especially ones without a single dominant grade, coarse datasets can only provide estimates of total elevation change or mean grade. Overall, the results show that it is vital to understand the accuracy and limitations of elevation data sources used in analysis and modeling of active travel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".