Designing a Parallel Limited-Stop Service Bus Route in Montreal Using Automatic Vehicle Location and Passenger Counting Data
Bibliographic record
Abstract
In recent years, several transit agencies have been trying to be more competitive withthe personal automobile in order to attract more choice riders. Transit agencies can only becompetitive if they can provide services that are reliable (short wait time and less variation),have a short access time at both ends of the trip, and offer run times comparable to thepersonal automobile. This report uses AVL and APC data, in addition to a disaggregate dataobtained from a travel behaviour survey, to select stops and estimate run times for a limitedstop(or express) service along a heavily used bus transit corridor, route 67 Saint‐Michel. A runtimemodel is established at the trip level and incorporates variables on rainfall, snowfall andaccumulated snow as well as separating passenger activity by door, among other operatingvariables. The climatic variables had a significant impact on increasing runtimes. Passengeractivity through the back door decreased bus travel times. Three different scenarios aredeveloped based on theory and practice to select stops to be served by the new limited route.A range of travel time savings for each scenario is then estimated. A fourth selection of stops isthen developed based on the first three scenarios. A limited‐stop service is recommendedbased on selecting stops that serve both route directions, major activity points and keep anaverage spacing of 800 to 1,600 meters. Running times ranges for this scenario are estimatedby varying the run time model by isolating passenger activity and actual stops made at stopsserved by the new service. Implementing an express service would yield substantial timesavings for both the limited route and a parallel regular route in the order of 10 to 20 percentfor the limited service. The STM will be implementing a limited‐stop service, route 467, startingon March 30, 2009 based on the analysis presented in this paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".