Range prediction for electric bicycles
Bibliographic record
Abstract
Thanks to their affordability and practicality, electric bicycles (e-bikes) are becoming popular, especially in urban areas. They are a zero-emission and zero-carbon alternative to cars and therefore have a high potential to mitigate climate change. However, range anxiety can be a significant barrier to the adoption of electric vehicles. To address this challenge, in this paper we focus on how an e-bike's remaining range can be accurately predicted. Using real data from the University of Waterloo WeBike field trial, combined with OpenStreetMap data, we evaluate two range prediction methods that take riding behaviour and route characteristics into account. Surprisingly, we find that predicting range for a particular cyclist based on his or her past energy consumption works as well as more complex methods that include additional information such as the route being travelled. Our findings also reveal which additional hardware and sensors e-bike manufacturers should provide in the future to make it easier to implement on-board range prediction. To the best of our knowledge, this is the first study of range prediction specifically for e-bikes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".