Spatio-Temporal Feature Engineering and Selection-Based Flight Arrival Delay Prediction Using Deep Feedforward Regression Network
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
Flight delays continue to pose a substantial concern in the aviation sector, impacting both operational efficiency and passenger satisfaction. Existing systems, while attempting to predict delays, often lack accurate predictive capabilities due to poor modeling setups, insufficient feature engineering, and inadequate feature selection processes, leading to suboptimal predictions and ineffective decision-making. Precisely forecasting flight arrival delays is essential for improving airline scheduling and resource allocation. The aim of our research is to create a superior prediction model that surpasses current modeling approaches. This study aims to forecast airline arrival delays by examining data from five prominent U.S. states in 2023—California (CA), Texas (TX), Florida (FL), New York (NY), and Georgia (GA). Our proposed modeling approach involves feature engineering to identify significant variables, followed by a novel feature selection algorithm (CFS) designed to retain only the most relevant features. Delay forecasts were generated using our proposed Deep Feed Forward Regression Network (DFFRN), a five-layer deep learning approach designed to enhance predictive accuracy by incorporating extensively selected features. The findings indicate that the DFFRN model substantially outperformed conventional models documented in the literature. The DFFRN had the highest R2 score (99.916%), indicating exceptional predictive efficacy, highlighting the efficacy of the DFFRN model for predicting flight delays and establishing it as a significant asset for improving decision-making and minimizing operational delays in the aviation sector.
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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