ITripGeneration Rates for LightRail Tmnsit ParkancH?ide lots
Bibliographic record
Abstract
11 park-and-ride lots in conjunction with its Light Rail Transit (LRT) system. The lots contain more than 7,000 stalls and provide free, paved parking for LRT patrons at suburban stations. Calgary’s LRT station facilities perform three main functions: ■ Transit terminal points where feeder bus systems interface with the LRT system, ■ Kiss-and-ride and ■ Park-and-ride locations The park-and-ride lots must be sized to ensure an adequate supply of parking and designed for the efficient operation of bus and auto traffic. In order to provide data for planning and operational studies of future park-and-ride lots, the City of Calgary Transportation Department undertook a study to examine the parking and traffic generation characteristics of the existing lots. (See note at end of article.) Overview of Park-and-Ride in Calgary Figure 1 show the current status of parkand-ride facilities provided at Calgary’s LRT stations. City of Calgary census figures for 1991 indicate that the city’s population was 708,600 with 86,700 downtown workers and 284,170 working throughout the remainder of the city. Since about a quarter of the employment in Calgary is in the central business district (CBD), downtown Calgary serves as a focus for the LRT system and as such the system is well suited to the needs of the downtown commuter. Calgary’s three rail transit lines serve different markets. The South Line serves a high population of downtown workers and also is the longest line (12.9 kilometers (km)) and the oldest, opening in 1981. The Northeast Line (9.8 km) serves a lower population of downtown workers in addition to running through a light industrial area. The Northwest Line serves the University of Calgary and the Southern Alberta Institute of Technology as well as serving a large base of downtown workers. The Anderson, Whitehorn and Brentwood stations are terminus stations on the
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".