Geographic information system‐system dynamics procedure for bus rapid transit ridership estimation
Bibliographic record
Abstract
SUMMARY This paper presents a two‐step procedure for estimating the total daily ridership (TDR) of a new bus rapid transit (BRT) route that runs along a corridor without a competing regular bus service. The first step of the procedure uses the Geographic Information System‐Business Analyst desktop to analyze and extract the total population, employed population, housing units within ¼ mile and ¼ to ½ mile from the BRT stations in the base year, and their respective annual growth rates. These values are then used as inputs into the second step. The second step of the procedure uses a simulation model constructed by the system dynamics approach. This simulation model, which embeds the known relationships between the demographic variables and proposed BRT system's infrastructure and operational features, initially estimates the TDR of the base year. Simulation is then performed to estimate the TDR from the base year until a future year defined by the user. The two‐step procedure has been validated with actual demographic and ridership data from the Las Vegas MAX BRT line and the Los Angeles Orange Line, respectively. The procedure has also been applied to the proposed BRT route along Mesa St. in El Paso, Texas, as a case study. The two‐step procedure offers a new and relatively simple approach that complements the three known BRT ridership estimation methods currently acceptable by the U.S. Federal Transit Administration. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".