Context aware parking occupancy forecasting in urban environment for sustainable smart parking system
Bibliographic record
Abstract
The increasing urbanization and car ownership rates are placing a significant strain on urban parking infrastructure, leading to congestion, pollution, and driver frustration.While smart parking systems, leveraging sensors, communication networks, and data analytics, offer a promising solution, existing systems face challenges such as limited accuracy, coverage, and integration.This paper examines the potential of context-aware parking occupancy forecasting to overcome these limitations.By incorporating external factors like traffic flow, weather, and events into forecasting models, this approach aims to improve prediction accuracy and optimize parking resource management.We discuss the current state of smart parking, its challenges, and the benefits of contextaware forecasting.This research contributes to the development of more effective and efficient smart parking solutions for creating sustainable and livable urban environments.The study leverages context-aware forecasting models such as LSTM and ARIMA to address challenges in parking occupancy prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".