Simplified model of local transit services
Bibliographic record
Abstract
This paper discusses the development of a simplified model to efficiently represent local public transportation services in a large-scale travel demand model. The California Statewide Travel Demand Model (CSTDM) is a comprehensive model system designed and developed for use in transportation policy analysis and travel demand forecasting, including representation of both long and short distance transportation covering the entire state of California. A novel hybrid system is used to represent the full range of rail and bus transit services that are available. Rail services – including all long-distance rail, commuter rail and light rail services – are represented in the standard manner, using explicit node and link networks; the relevant in-vehicle and out-ofvehicle service characteristics for journeys are determined as standard skims of these networks. On-street bus services are not represented using explicit networks; rather, the relevant in-vehicle and out-of-vehicle service characteristics are determined using functions of other transportation network variables, land use descriptors and relevant policy indicators. These functions are simplified econometric models estimated using observations of transit service obtained from Google Transit Data Feeds. The network and simplified components are integrated in order to allow transit paths with both rail and on-street bus components to be considered by the various travel choice models included in the modelling framework. This hybrid system provides a suitable representation of transit for an area of such size. This facilitates consideration of transit service policies, while obviating the need for extensive transit coding, a daunting task for a large area.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".