Deriving Parking Use from Household Travel Survey Data
Bibliographic record
Abstract
Parking is a critical element in sustainable transportation policies as it directly affects mode choice, land use, street safety, etc. Still, it does not receive as much attention as it should in the research agenda compared to other topics. One of the reasons for the lack of research and quantitative assessment is the complexity, time and burden associated with the gathering, processing and analysis of data on parking supply and demand. Actually, parking studies tend to be often limited to small areas and focused on specific issues. This research proposes a method to derive zonal parking use from the systematic processing of car trips observed from household travel surveys. Three consecutive large-scale surveys from the Montreal area are processed to illustrate the methodology. It relies on a concept called the vehicle accumulation profile (VAP) which serves to derive parking use throughout a typical weekday and each day of the week. The method allows preserving all the attributes of the car driver, making it possible to analyze parking patterns by type of parking, population segment, etc. Processing several Origin-Destination surveys allows conducting longitudinal parking analysis. This paper illustrates the usability of such a concept and confirms that household surveys can be systematically processed to provide typical parking usage, as is done for travel behaviors. The analysis reveals an increasing share of not moving vehicles, during a typical weekday, across the area from 1998 to 2008, as well as an overall increase in assumed parking capacities and parking duration. Trends on parking duration and assumed capacity (derived from the survey) are also discussed.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".