Predicting Transport Mode in the Rabat Region: A Machine Learning Approach
Bibliographic record
Abstract
The continuous increase in population density and the increase in average travel of people by different modes of transportation, whether public or private, play a crucial part in how the city's urban area develops.The latter contribute significantly to the emergence of many problems, including road congestion, loss of time, and pollution in urban areas, noise, and other issues.Machine Learning has extensively beneficial aspects of proposing models in this context.Therefore, we proposed four algorithms of the machine learning techniques that have been implemented to analyze and classify the displacement of an individual's database to support urban decisions, K-Nearest Neighbors (KNN), Artificial Neural Network-multilayer perceptron Neural (net-RBF), Bayesian Belief Network (BNN) and Support Vector Machine (SVM).We will compare their learning metrics using train/test and cross-validation.The obtained results show that net-RBF offers the best accuracy (92.77%),SVM classifier (89.87%),BBN classifier (87.33%), and KNN classifier (86.53%).
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".