Spatio-Temporal Feature Engineering and Selection-Based Flight Arrival Delay Prediction Using Deep Feedforward Regression Network
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".