Bibliographic record
Abstract
Cities are faced with many challenges, in particular in relation to the mobility of people and the structure of land-use. Parking management, which makes the link between the fields of urban planning and transportation, is one of the crucial ways to meet these challenges. However, parking studies are a poorly covered area in transportation research. The main barrier to study parking is parking data availability. In the Greater Montreal Area, data from origin-destination (OD) surveys are helpful in understanding typical travel behavior. These surveys have been conducted for forty years and provide useful data to describe and model various spatial-temporal features of daily mobility. This research illustrates the use of OD survey data to develop indicators on parking spaces and use in a given area. This study confirms that the systematic processing of car driver trips from travel surveys allows developing vehicle accumulation profiles for various zones and, from these, derive theoretical parking capacities. This research provides an assessment of the quality of the estimation by comparing the estimations from OD survey to other sources of data, namely geographical data and field surveys. The paper shows that parking capacity is subject to high variability and highlights that its assessment is quite complex and must take into account regulation data that modulates the availability of the raw parking capacity according to different days and hours of the day.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".