How Well Does the Traffic System Protect Transit from Congestion? Measuring Route-Level Costs That Congestion Imposes on Transit Operators and Users
Bibliographic record
Abstract
As transit agencies and road owners adopt the objective of protecting transit from congestion, it becomes important to have a method for measuring the cost that congestion imposes on transit. Congestion impacts transit both by lowering average speed and by increasing service unreliability. Altogether, five congestion impacts were identified: increased running time and recovery time for transit operators and increased riding time, waiting time, and buffer time for passengers. A methodology for estimating those impacts was developed using automatic vehicle location data. The basic approach was to compare the impact variables during various periods of the week against a base period when there is no congestion (late night and early morning), making adjustments to account for differences in demand that affect running time apart from congestion. The methodology was successfully applied to a sample of 10 bus routes in the Boston area. The cost of congestion on the sample routes was found to range from $1 to $2 per passenger, with annual costs as great as $8 M per year on some routes. Of the total congestion cost, just under 20% applies to the operator, with the remainder applying to passengers. And while the operator is mainly affected by increased average delay, passengers are mainly affected by worsening service reliability.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".