Estimation Vehicular Waiting Time at Traffic Build-Up Queues
Why this work is in the frame
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Bibliographic record
Abstract
Due to the high growth of social economic activities and the increased need for mobility in recent days, transportation problems like congestion, accidents, and pollution have been increased. However, improving the reliability of delay estimates and real-time dissemination of information remains a challenge. An advanced border-crossing system corresponding to the changes of cross-border circumstances becomes an urgent matter. An automated system for queue end monitoring has been proposed using image processing based transformed domain and empirical mode decomposition (EMD) feature extraction systems. The performance of feedforward backpropagation algorithm artificial neural networks (ANNs) was evaluated and tested, based on a selected set of features. The experimental results showed that the use of discrete wavelet transform (DWT) based Daubechies with decomposition of level 2 has accomplished the target with a processing time 2 sec and 3 epochs of training network only with best validation performance of (2.1053e-007) for vehicle recognition. Also the use of EMD as a feature extractor has accomplished the target of vehicle recognition with a best validation performance of (about 3.42e-09) and a processing time of 1 sec at epoch 3 of training network only with a minimal percentage of error for the recognition of each vehicle in the appropriate queue with the aid of the new concept of road side unit (RSU).
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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 | 0.000 |
| 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 | 0.000 |
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