Optimization of Dynamic Parking Guidance Information for Special Events
Bibliographic record
Abstract
Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".