A mesoscopic cycling energy modelling approach based on emissions modelling principles
Bibliographic record
Abstract
Many governments around the world have ambitious goals to increase active travel to reduce emissions and physical inactivity in built-up areas. However, one of the challenges to designing interventions that promote cycling is that we have a limited understanding of how cycling behaviour is impacted by the physical demands of cycling. Similarly, assessing the physical activity impacts of interventions is challenging because available approaches neglect important differences in riding intensity across trips. These challenges could be overcome, in part, through explicit investigation of cyclists’ energy expenditure, but we currently lack the tools for including this factor in practical travel analyses. Surrogate variables like speed risk conflating distinct effects pathways (e.g., effort and travel time considerations), microscopic mathematical models are data-intensive, and macroscopic mathematical models do not give us information on energy expenditure variability or distribution. This thesis proposes a mesoscopic approach for modelling cycling energy by developing a novel modelling framework (built on motor vehicle emissions modelling principles), determining key model design elements (segmenting variables and operating mode definitions), illustrating the potential accuracy of a mesoscopic model (in comparison to a microscopic model), and identifying key research and data needs for further model development. Applied on a naturalistic dataset containing cycling trips taken in Vancouver, Canada in 2017, the proposed mesoscopic model can predict mean positive cycling motive work rate (energy used to change the energy state of a bicycle and rider system, at the road-tyre interface) to within 40W (30%) of microscopic estimates. Gender, e-assist, and speed (as average trip speed or self-rated speed) are the key segmenting variables explaining variability in cycling motive work rates across trips; adding additional segmenting variables did not importantly improve model accuracy. Future work to develop mesoscopic cycling energy models for travel analysis should focus on investigating alternative operating mode definitions and analysis units, identifying potential interaction effects, collecting data on non-utilitarian and e-bike trips, investigating the influence of equipment and rider mass on motive work rate, and direct measurement of rider mechanical work rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".