Model-based Methodology for Identifying Capacity Limitations in Airports: Mexico City Airport Case

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Amsterdam University of Applied Sciences

Model-based Methodology for Identifying Capacity Limitations in Airports:

Mexico City Airport Case

Mujica Mota, Miguel; Flores, Idalia DOI

10.11128/sne.29.on.10462 Publication date

2019

Document Version Final published version Published in

Simulation Notes Europe

Link to publication

Citation for published version (APA):

Mujica Mota, M., & Flores, I. (2019). Model-based Methodology for Identifying Capacity Limitations in Airports: Mexico City Airport Case. Simulation Notes Europe, 29(1), 13-25.

https://doi.org/10.11128/sne.29.on.10462

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S N E OV E R V I E W N O T E

Model-based Methodology for Identifying Capacity Limitations in Airports:

Mexico City Airport Case

Miguel Mujica Mota^1*, Idalia Flores²

1Amsterdam University of Applied Sciences, Aviation Academy, Weesperzijde 190,1097DZ. The Netherlands; *m.mujica.mota@hva.nl

2National Autonomous University of Mexico, Mexico City, Mexico

Abstract. The current study presents a methodology for analysing and identifying the limitations in capacity of an airport, the methodology has been implemented in the case of Mexico City Airport which is a congested airport in Mexico. The methodology allows identifying what room is left for absorbing more traffic and what options are available while a new infrastructure is in place. The methodology revealed, that there is still room for absorbing more traffic under certain conditions and start- ing from that, actions can be taken in order to increase the capacity or reducing congestion in the airport.

Introduction

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Mujica Mota and Flores Model-based Methodology for Identifying Capacity Limitations

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Figure 1: Methodological approach for simulating airport systems.

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Figure 2: Conceptual approach of the simulation model.

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3 Case Study: Mexico City International Airport

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Figure 3: Schematic view of the airport.

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Table 2: Performance parameters of RWY.

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Figure 6: Evolution of ATMs during the day.

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Figure 7: Evolution of ATMs during the day 10% more traffic.

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Figure 9: Evolution of A/C queuing in T1.

Figure 10: Evolution of A/C queuing in T2.

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Figure 12: Evolution of gates utilization.

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