Disaggregate Analysis of Relationships between Commercial Vehicle Parking Citations, Parking Supply, and Parking Demand
Bibliographic record
Abstract
Over the past several decades, road space and curb space have become increasingly scarce in urban areas. Commercial vehicles are then forced to compete with passenger vehicles for this limited space; this situation leads to an increase in illegal commercial vehicle parking. Increased commercial vehicle parking causes increased congestion, reduced safety for other road users, and an increase in the cost of final goods and services. For appropriate policies to be selected to address this problem, the relationships between illegal commercial vehicle parking and the built environment must be better understood. This research aimed to quantify the relationships of illegal commercial vehicle parking, parking supply, and parking demand through the estimation of a distance-decay-weighted regression model. Data on commercial vehicle parking citations from 2012 for the City of Toronto, Ontario, Canada, are presented. This information was used together with employment data and a parking inventory to estimate the number of tickets issued by address. A distance-decay-weighted method was applied in an attempt to capture the spatial relationships between these variables. Results of this model showed that off-street parking facilities, such as loading bays, surface lots, and parking garages, were related to lower rates of illegal commercial vehicle parking, whereas higher business densities and the restriction of existing on-street parking spaces were related to increased illegal commercial vehicle parking. These results suggest that policies encouraging the creation of off-street loading facilities for commercial vehicles and making off-street parking more attractive for passenger vehicles may help reduce the incidence of illegal commercial vehicle parking.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".