FEATURES OF THE FUNCTIONING OF ROADSIDE PARKING SPACE
Bibliographic record
Abstract
The aim of the study is to investigate the natural quantitative characteristics of the functioning of a roadside (linear) parking area: the number of parking sessions, the occupancy of the parking space, the distribution of storage periods, the intensity of entry and exit of cars, the turnover of parking spaces, etc. The object of the study is the processes of functioning of a roadside parking lot located near an educational institution (school). The subject of the study is the features of the functioning of a parking space of this type. In contrast to well-known publications, the evolution of key indicators of roadside parking is considered, that is, their dependence on the time (quarter) of the year. Observations were carried out continuously for a week both in spring (April) and summer (July), autumn (October) and winter (February). The data for the study were obtained using the stationary measuring software and hardware complex «Azimuth DT», which monitors the movement of vehicles in the parking area continuously throughout the entire observation period. Digital processing of video recording data of cars entering and leaving the parking lot allowed us to establish that almost all the determined quantitative characteristics of the parking space are not constant and depend on the season, which must be taken into account when placing, planning and organizing the functioning of the parking area. It is noted that the specific generation of correspondence and the required number of parking spaces near the centers of mass gravity are sensitive to the characteristics of the urban area and, apparently, vary greatly across the regions of the country. The indicators obtained for urbanized areas of other countries are unique and are not applicable for practical use in Russia. It is advisable to extend the experience and methodology of studying the parking space to the study of the performance indicators of flat, including intercepting, and multi-level parking lots.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".