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Study on electric towing tractor quantity based on cellular automata

Published online by Cambridge University Press:  31 October 2024

Y. Liu*
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
X. Cheng
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
D. Tang
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
J. Zhang
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
X. Luo
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
*
Corresponding author: Y. Liu; Email: yx-liu@cauc.edu.cn
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Abstract

Aircraft ground taxiing contributes significantly to carbon emissions and engine wear. The electric towing tractor (ETT) addresses these issues by towing the aircraft to the runway end, thereby minimising ground taxiing. As the complexity of ETT towing operations increases, both the towing distance and time increase significantly, and the original method for estimating the number of ETTs is no longer applicable. Due to the substantial acquisition cost of ETT and the need to reduce waste while ensuring operational efficiency, this paper introduces for the first time an ETT quantity estimation model that combines simulation and vehicle scheduling models. The simulation model simulates the impact of ETT on apron operations, taxiing on taxiways and takeoffs and landings on runways. Key timing points for ETT usage by each aircraft are identified through simulation, forming the basis for determining the minimum number of vehicles required for airport operations using a hard-time window vehicle scheduling model. To ensure the validity of the model, simulation model verification is conducted. Furthermore, the study explores the influence of vehicle speed and airport scale on the required number of ETTs. The results demonstrate the effective representation of real-airport operations by the simulation model. ETT speed, airport runway and taxiway configurations, takeoff and landing frequencies and imbalances during peak periods all impact the required quantity of ETTs. A comprehensive approach considering these factors is necessary to determine the optimal number of ETTs.

Information

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society
Figure 0

Figure 1. Illustration of aircraft stand area.

Figure 1

Table 1. The amount of fuel saved by airports

Figure 2

Figure 2. Lict resolution rules.

Figure 3

Table 2. The runway take-off and landing interval time [15, 16] 0

Figure 4

Figure 3. ETT service process.

Figure 5

Figure 4. The hard time window model.

Figure 6

Figure 5. Comparison of taxiing time.

Figure 7

Figure 6. The cumulative distribution function of relative errors.

Figure 8

Figure 7. Variation of ETT quantity with time.

Figure 9

Figure 8. Variation of ETT quantity with speed for different airports.

Figure 10

Figure 9. Relationship between ETT quantity variation and departure flight distribution.

Figure 11

Table 3. The amount of fuel saved by airports