Modeling and Predicting Parking Demand on a University Campus Using Spatio-Temporal Graph Convolutional Networks
Bibliographic record
Abstract
This paper introduces a novel approach for modeling and predicting parking demand in a university campus setting using Spatio-Temporal Graph Convolutional Networks (ST-GCNs). The framework models the campus parking system as a weighted undirected graph, where nodes represent parking lots and edges capture spatial relationships. Temporal patterns are integrated through LSTM layers, enabling the prediction of demand based on historical data. The model incorporates spatial and temporal dependencies to improve prediction accuracy and adaptability in dynamic environments. A study conducted on the Université de Moncton campus demonstrates the effectiveness of the proposed approach, offering actionable insights for efficient parking resource management by predicting the number of occupied spots across different parking lots in the campus. This study highlights the potential of ST-GCNs for parking demand prediction in structured environments such as university campuses and based on historical data. By leveraging spatio-temporal dependencies, our approach enhances forecasting accuracy, which could inform more effective urban planning strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".