Impact of on-ground taxiing with electric powered tow-trucks on congestion, cost, and carbon emissions at Montreal-Trudeau international airport
Bibliographic record
Abstract
Everyday millions of litres of jet fuel are burnt during the aircraft’s on-ground taxiing operations, releasing tons of greenhouse gas emissions in the atmosphere. Aircraft manufacturers and researchers believe that replacing the current aircraft taxiing operation with more efficient on-ground taxiing operations could meet future market requirements. Multiple factors such as safety, airport throughput, energy efficiency, air emissions, and total cost need to be considered when designing airport taxi operations. This research reports on the performance of utilizing electric tow-trucks during on-ground taxiing operations. It builds on previous studies to assess the impact of the initial investment of implementing these alternative taxi system on congestion, cost and carbon emission on the on-ground taxi operations. We developed a Discrete Event Simulation model to schedule electric powered tow-trucks to provide taxiing services to aircrafts. The simulation enables aircrafts to request an available tow-truck or use aircraft engines to perform taxiing operations. The performance measurements of the taxiing operations were based on total fuel consumption, emission, traffic delays and total cost of implementing the operational strategy. Montreal-Pierre Elliot Trudeau International Airport was selected as a case study. Based on the presented methodology, the result exhibits that utilizing electric-powered tow-trucks to perform all on-ground taxiing operations is the best practical solution to meet the future market requirements. The conducted investigation indicates that this approach provides both economic and environmental benefits to the aviation industry. Three extensive sets of numerical analysis have been conducted to provide better insights into the problem. In each part of these analysis, different determinant factors such as the total cost, fuel consumption, delay and emissions have been used to compare the obtained results of the proposed approach with the current situation at the airport. After analyzing the results, an environmentally friendly and economically efficient approach is offered.
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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 | 0.000 | 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 | 0.001 |
| 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".