Real-Time Prediction of Bus Inter-Stop Travel Time Using Deep Learning Approach
Bibliographic record
Abstract
Urban bus systems are becoming increasingly important as urbanization and traffic volumes increase. Travel time is an important component of this system. Providing accurate information about the future is essential for improving user satisfaction and optimizing the use of existing facilities. With the implementation of automatic vehicle location (AVL) systems for monitoring bus locations, it is possible to access bus traffic data, which is helpful for forecasting. Analyzing AVL data from Tehran, Iran, this study compares a statistical approach to a deep learning approach for predicting inter-stop travel time. According to the results, deep learning outperforms the statistical model in travel time prediction. Additionally, the sensitivity analysis shows that arc lengths and directions are the most significant factors in travel time predictions. The developed models can predict travel times in transit applications with reasonable accuracy. Developing countries with similar public transportation systems and mobility characteristics can use the findings to improve bus services.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".