Modeling Cyclists’ Route Choice Based on GPS Data
Bibliographic record
Abstract
With the increased emphasis on sustainable transportation, advancements are necessary in the technical methods used in the planning and engineering of investments for nonmotorized modes. This paper used GPS data on cyclists’ activities to estimate a utility or generalized-cost function that reflects cyclists’ evaluation of path alternatives. For 724 cycling trips, path attributes were compiled of the observed cycling path to four feasible but not-chosen alternatives. With two logit formulations, the relative importance of statistically significant path parameters—length, auto speed, grade, and the presence (or absence) of bike lanes—was estimated. Then the predictive powers of the models were tested on 181 trips that were observed in the same data set but were not used to calibrate the model. In the best case, this model correctly predicted the actual path for 65% of these trips; for an additional 13% of trips, the difference in probabilities of selecting the best alternative path and the actual path was less than 5%. These results were interpreted to mean that relatively robust path choice (and ultimately mode choice) models may be generated and included in enhanced multimodal travel forecasting models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".