Electrification of Airport Operations:Electric Powered Tow-Truck Utilizationin Taxiing Operations
Bibliographic record
Abstract
ABSTRACT Civil aviation has steadily increased over the past decades and plays an essential role in connecting people and countries across the world. According to the International Civil Aviation Organization (ICAO, 2018), passenger traffic has grown with an average of 5.4% between 1995 and 2015. ICAO estimates the demand for aviation to continue increasing by an annual rate of 4.3% until 2035 and 4.1% until 2045. Among several crucial objectives of air transportation system problems, the minimization of fuel consumption has a profound impact on both the economic viability of airline companies and the impact of air-transportation in the environment. Although aviation is not currently the leading cause of global warming, industry development, and the increase in air transportation will make it a significant factor for global warming over the coming decades. Predicting the impact of aviation on economic and environmental systems requires investigations at different stages of air transport operations. One of the strategies to reduce the fuel consumption of aviation is to optimize the fuel burn during airplane ground movement (taxiing) in airports. The main reason is that aircraft ground movement is a significant source of fuel consumption and emissions at an airport (e.g., it is estimated that aircraft burn about 7% of their fuel during this stage of the flight). Among different ways of taxiing operation in an airport, electrification of ground transportation has proven to be one of the most efficient ways which have many advantages such as reducing fuel consumption and emission of greenhouse gases with low maintenance cost. However, it should be noted that electric-powered vehicles can be a beneficial and efficient way of taxiing in airports if the electricity is clean. Clean electricity is produced from IV renewable and non-emitting sources such as wind, sun, and water. Using electric-powered vehicles in airports might not be the optimal option if the electricity is produced by burning fossil fuels like coal. Nowadays, in many provinces of Canada, the produced electricity is clean, and the government is determined to have 90% clean electricity across Canada by 2030. The presented study discusses the scheduling of aircraft towing tractors at the airport in order to minimize the fuel consumption and environmental emission of airplane engines and towing tractors. In this study, we developed a Mixed Integer-Linear Programming (MILP) model to schedule electric-powered towing vehicles (pushback Tugs) to provide taxiing services to aircraft. The proposed MILP solution enables aircraft to request a towing vehicle when it is available or perform traditional taxiing operations by using aircraft engines to minimize operating costs, which includes delay/earliness costs, fuel consumption cost, and towing cost. We concluded that the hybrid system for taxiing operation which includes both traditional engine powered solutions and the proposed electric-powered towing vehicle approaches, is the optimal solution. Through sensitivity analysis, the proposed taxiing operations planning model determines the optimum number of towing vehicles in an airport.
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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 | 0.000 | 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) | 0.002 | 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".