SCOPE: Smart Cooperative Parking Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The shortage of parking spaces in metropolitan cities has become a significant challenge, leading to wasted time, money, traffic congestion, and environmental pollution. While smart parking solutions offer potential relief, existing systems often struggle with integration and coordination issues in the complex smart city ecosystem. In response, this paper introduces SCOPE, a cooperative distributed system architecture and interaction model that facilitates the management of parking spaces in a smart city through coordination and autonomous interactions. The system leverages an overlay network, a hierarchical and spatial structure of coordination nodes, and an integration layer to organize traffic and communication among facilities. By incorporating a sharing economy business model, SCOPE maximizes parking resource usage, merges public and private parking resources, and provides economic opportunities for private parking owners. The evaluation results demonstrate that SCOPE significantly reduces search time, traffic, cost, and air pollution while improving driver satisfaction. This novel approach presents a comprehensive solution to the challenges of smart parking management in metropolitan cities, paving the way for more efficient, sustainable, and economically viable urban environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it