Assessing a Model for Optimal Bus Stop Spacing with High-Resolution Archived Stop-Level Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With increasing attention given to performance and financial issues related to the operation of public transportation systems, tools are needed to improve the efficiency and effectiveness of service offerings. High-resolution archived stop-level bus performance data can be used to generate and test a bus stop spacing model with the goal of minimizing operating cost while maintaining a high degree of transit accessibility. Two cost components are considered in the spacing model: passenger access cost and in-vehicle passenger stopping cost. These are combined and optimized to minimize total cost. A case study was made of a bus route in Portland, Oregon, by using 1 year of stop-level archived data from the Tri-County Metropolitan Transportation District of Oregon, the regional transit provider of the Portland metropolitan area. The case study indicates that the theoretical optimized bus stop spacing is 1,200 ft, compared with the current value of 950 ft. Trade-offs are discussed, and an estimate is presented of transit operating cost savings based on optimized spacing. It is shown that because of availability of high-resolution archived data, this modeling tool can be applied routinely across multiple routes as part of an ongoing service-planning and performance-measurement process.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it