An Analysis of Empirical Evidence of Cyclists’ Route Choice and Its Implications for Planning
Bibliographic record
Abstract
In this paper, the authors present results from a cycling study in the region of Waterloo, Ontario Canada. In total, they gathered both sociodemographic and observed travel data from 415 self-selected cyclists from March 2010 until February 2011. In this paper, the authors concentrate on how roadway networks and built environment influence the cyclists’ possible and observed path choices. Data on origins, destinations and actual paths were collected using low-cost GPS units. From the data collected, the authors generate shortest paths based on the x-y distance, shortest path along roadways, and shortest path including roads and bike paths. They compare these results using the concept of “excess travel” or required travel distances beyond the minimum possible distances. They are able to show that excess travel increases with indirect, curvilinear roadway networks and land uses that act as impediments to connectivity. They next compute the excess travel saved by the addition of the trails network. The results suggest that many very high cost paths can be eliminated with the addition of trails. Finally, the authors compare the difference in actual path and shortest path to compute excess travel for utilitarian (non-recreational) trips. They are able to demonstrate land patterns that do not support cyclist produce large penalties in terms of added lengths to cycling trips. Results discussed help to showcase the travel time savings that may be experienced with increased cycling infrastructure and connectivity, as well as prioritize future cycling investment. The authors conclude with key findings and a discussion of future work.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".