Using LiDAR Data for Measuring Transit Stop Coverage
Bibliographic record
Abstract
A public transit system consists of various components in which all must be considered together in order to develop an efficient and sustainable transit network. Providing convenient access to public transit enhances the service performance, reliability and will also result in higher public usage. Conventionally transit stop locations and spacing are determined based on aggregate measures of population density on a zonal basis using simple buffer analysis. This method has been criticized as inaccurate, as the population is rarely uniformly distributed over zones. In this research, transit stop access coverage is estimated using building geometric information accurately extracted from the LiDAR data collected in the City of Fredericton, Canada. LiDAR data is mostly used for flood hazard studies but can also be used for other purposes such as 3D building modeling. Through this approach building floorspace information and therefore a much more accurate measurement of transit stop coverage based on the building floorspace is obtained at disaggregate spatial level and compared with the conventional buffer-density approach through a real example in the City of Fredericton. Overall, it is found that this approach can provide transit planners with much more improved building and population distribution information at a very precise spatial level, in order to set the transit stops at their optimal locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".