A Collaborative Reservation Mechanism of Multiple Parking Lots Based on Dynamic Vehicle Path Planning
Why this work is in the frame
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Bibliographic record
Abstract
With the development of wireless communication and artificial intelligence technology, online parking reservation system can effectively save drivers’ searching time for vacant spaces. However, in the environment with multiple candidate parking lots around the destination, how to coordinate and maximize parking space resources to reduce the travel time is still a practical issue for urban drivers. In order to solve this problem, a collaborative reservation mechanism based on dynamic vehicle path planning is proposed in this paper. By the aid of the dedicated backbone network with a clear division of work responsibilities, the information of traffic and parking lots is collected in real time, based on which the travel time prediction and empty spaces evaluation are executed separately, and then the optimal decision of path planning and parking lot selection can be made and adjusted dynamically by a step-by-step acknowledgement mechanism. The simulation results show that, based on collaborative working and overall planning, our proposed reservation mechanism can effectively raise the utilization rate of the parking lots resources and significantly reduce the travel time for drivers under different traffic environments. Compared to current mechanisms, the collaborative parking reservation mechanism reveals higher feasibility and applicability. It can assist in design and operation of urban traffic management and space resource utilization.
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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 | 0.001 |
| 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 | 0.000 |
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 it