Route-Level Transit Passenger Origin-Destination Trip Estimation from Automatic Passenger Counting Data: A Case Study in Edmonton
Bibliographic record
Abstract
Transit passenger origin-destination (OD) trip estimation is very important for transit planning, service management and operation analysis. The traditional method to conduct transit OD trip estimation requires on-board surveys to collect passenger on-off data, which are time-consuming, expensive and usually by-products of other comprehensive censuses which may take place in a very low frequency. The Automatic Data Collection (ADC) systems, including Automatic Vehicle Location (AVL) system, Automatic Passenger Counting (APC) system and Automatic Fare Collection (AFC) system, can collect passenger boarding and alighting counts frequently and have a much larger coverage than on-board surveys. In this thesis, data structure and methods of preprocessing APC data are discussed; route-level transit passenger OD trip estimation methods using APC data are reviewed and applied to the APC data of the Route 1 of the Edmonton Transit System (ETS). The analysis in this thesis shows those methods can produce similar results, but they have strengths and drawbacks. This thesis compares them and makes recommendations for practical applications. Besides, this thesis reviews and implements the stop grouping method to group similar stops along the Route 1 of ETS. The result stop group configuration synthesizes important flow patterns along the Route 1 which is more useful for transit agencies than stop-to-stop OD trip estimations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".