An improved incremental assignment model for parking variable message sign location problem
Bibliographic record
Abstract
Summary Operators and planners of parking guidance and information system (PGIS) often encounter the difficulty when and how to provide parking information to drivers in peak hours. The aim of this study was to assign the parking demands onto the urban network, find the positions suitable to locate the parking variable message sign (parking VMS), and try to solve the problem when to provide parking information to drivers. Parking VMS, as the most common forms of the information display terminal of PGIS, becomes critical in designing PGIS. This paper started from analyzing the relationship between the location and the performances of parking VMS. If a parking VMS is placed right along the link approaching to the divarication intersection of the subsequent “shortest path” and the previous one, the travel time declines abruptly, and the guiding compliance ratio becomes superior. Then, the network‐based parking choice model for the parking VMS was proposed. Network modification and incremental assignment were used to find the divarication intersections caused by the changes of space availability. In addition, MATLAB software package (MathWorks, Inc., Natick, MA, USA) was adopted to calculate the entire process mentioned. The proposed model and the algorithm were applied to a numerical example, where the location of parking VMS was obtained. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".