Parking Demand vs Supply: An Optimization-Based Approach at a University Campus
Bibliographic record
Abstract
Parking management has always been a major concern for universities and other activity centers. Nowadays, many universities are suffering from a lack of campus parking capacity. To tackle this problem, it is necessary to take parking lots assignment into consideration, regarding intercampus users’ needs. These users have different ages, physical characteristics, expectations, and administrative positions that should be considered before any parking assignment. Here, a new method is proposed to optimize parking lots management for those universities where staff (academic and administrative), in contrast to students, are allowed to park inside the campus area. For this purpose, first, the probability of using a specific parking lot by each group is determined. For staff, this is done based on their choices, revealed by the relative frequency of using parking lots. This probability for students can be calculated using a fuzzy inference system model. To develop the model, a survey is conducted to extract students’ preferences, regarding parking spaces assignment inside the campus area. Afterward, an integer linear programming model with the objective function of maximizing parking probability is employed, considering several related constraints. The proposed model is applied to Shahid Bahonar University of Kerman (SBUK), Iran, as the case study. According to the results, it can be concluded that the proposed method can help to reduce wandering time of finding an appropriate parking space for both staff and students. In addition, the proposed application can help increase the satisfaction level of staff and students with regard to parking management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".