Land Use as a Criterion for the Selection of the Trip Starting Locations of Park and Ride Mode Travelers
Bibliographic record
Abstract
In the attempt to study Light Rail Transit (LRT) systems, and their necessary underlying components, such as Park and Ride (P&R) sub-systems, this article aims to showcase the importance of land-use as a criterion in the selection of trip starting locations (i.e., points), that can potentially be used as the basis for quantitative studies on LRT and P&R systems. In order to achieve this goal, a method is introduced for the selection of locations that produce P&R mode trips based on the land-use attributes of sub-zones or neighborhoods, as they are included in Sustainable Urban Mobility Plans (SUMPs). Those land-use attributes are utilized as sub-criteria for the classification and valid selection of trip starting locations out of a broader dataset of available locations. As a second supportive technique that needs to be utilized for this study, an algorithm is introduced, which allows us to test the effectiveness of the method and the importance of land use as a criterion. The algorithm enables the calculation and comparison of the attributes of the trips to be followed by P&R mode users starting from selected trip starting locations for each zone in a city and having as destinations the several available P&R facilities. Results for the methods introduced in this article are showcased based on a case study on the mid-sized city of Cuenca, Ecuador, in which, several metrics, such as traveling times considering different traffic scenarios, are examined for the potential P&R mode trips as they emerge from real-world data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".