Real-time flight scheduling for optimal air traffic flow management

Inherent uncertainties of the air transportation system can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, higher workload for Air Traffic Controllers (ATCos), etc. Current Air Traffic Flow Management (ATFM) models r...

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Main Author: Gammana Guruge, Nadeesha Sandamali
Other Authors: Su Rong
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/140363
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-140363
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Engineering::Aeronautical engineering::Air navigation
spellingShingle Engineering::Electrical and electronic engineering
Engineering::Aeronautical engineering::Air navigation
Gammana Guruge, Nadeesha Sandamali
Real-time flight scheduling for optimal air traffic flow management
description Inherent uncertainties of the air transportation system can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, higher workload for Air Traffic Controllers (ATCos), etc. Current Air Traffic Flow Management (ATFM) models rarely consider uncertainty in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, the thesis proposes several frameworks for en-route ATFM, while scrutinizing different uncertainty components present in air transportation system. Generally, uncertainty exists in two major forms, i.e., capacity uncertainty and demand uncertainty, which result in the stochastic behavior of the Air Traffic Network (ATN). Demand uncertainty is one critical form, which has an adverse effect on ATFM. It is mainly due to the deviation in departure time and aircraft speed from their scheduled values, and can further lead to an unexpected arrival of aircraft at certain routes at unscheduled times, causing unanticipated demands for those routes. The uncertainty of demand imposes several difficulties in air transportation systems, including higher workloads for air traffic controllers, higher delays, travel costs, as well as safety risks. Thus, in the initial part of the thesis, we propose a preliminary flight routing and scheduling scheme, while considering both departure and speed uncertainties present in ATN. Following robust optimization, we ensure that capacity violations are eliminated from the system while maintaining the required in-trail separation between aircraft even under the uncertainty of demand. Similar to the demand, the uncertainty of capacity is also another critical form of uncertainty present in air transportation system, and it is primarily due to unexpected weather situations, emergency conditions, controller workload variations, etc. Thus, in this thesis, we further propose a framework for en-route ATFM, while scrutinizing uncertainties in en-route capacity and demand and their imbalance, via a chance constraint-based probabilistic approach. The proposed framework plays a key role in ensuring the safety of the overall air transportation system in terms of maintaining the safety separation between flights and constraining the capacity of the sectors as well. Moreover, a flight level assignment and a speed optimization scheme are proposed based on the Base of Aircraft Data (BADA) of the European Organization for the Safety of Air Navigation (EUROCONTROL) with the objective of minimizing the fuel consumption. The model further minimizes the overall expected delay of the network using the control actions of ground holding, speed control, rerouting, and flight cancellations. At the implementation stage, two phases of ATFM, i.e., the pre-tactical phase and tactical phase, are considered, in which the former focuses on generating optimal trajectories, and the latter focuses on real-time updates of flight plans. However, due to the flight-by-flight structure, the above models scale poorly for large-scale systems. To overcome this drawback, especially anticipating the high volume of air traffic in the future, we finally present a two-stage architecture, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage decomposes them into individual flight plans. Due to the flow-based modeling structure at the first stage, it enhances the scalability while taking into account constraints of the link, sector and airport capacities and also the waypoint flow rate. The second stage ensures that the flights are safely separated with the required in-trail separation, thus, enhances the safety of the overall system. Acknowledging the importance of computational complexity along with the optimality, several solution approaches are proposed for above optimization problems, which are primarily base on the concept of maximum independent set, in which the overall problem is decomposed into a set of subproblems based on their independence, and further optimized parallelly resembling a greedy approach, yet with a tighter closeness to the global optimality. Furthermore, each ATFM framework is evaluated in terms of flight delays and possible conflicts with simplified and realistic case studies.
author2 Su Rong
author_facet Su Rong
Gammana Guruge, Nadeesha Sandamali
format Thesis-Doctor of Philosophy
author Gammana Guruge, Nadeesha Sandamali
author_sort Gammana Guruge, Nadeesha Sandamali
title Real-time flight scheduling for optimal air traffic flow management
title_short Real-time flight scheduling for optimal air traffic flow management
title_full Real-time flight scheduling for optimal air traffic flow management
title_fullStr Real-time flight scheduling for optimal air traffic flow management
title_full_unstemmed Real-time flight scheduling for optimal air traffic flow management
title_sort real-time flight scheduling for optimal air traffic flow management
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140363
_version_ 1772825260559695872
spelling sg-ntu-dr.10356-1403632023-07-04T17:20:33Z Real-time flight scheduling for optimal air traffic flow management Gammana Guruge, Nadeesha Sandamali Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Engineering::Electrical and electronic engineering Engineering::Aeronautical engineering::Air navigation Inherent uncertainties of the air transportation system can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, higher workload for Air Traffic Controllers (ATCos), etc. Current Air Traffic Flow Management (ATFM) models rarely consider uncertainty in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, the thesis proposes several frameworks for en-route ATFM, while scrutinizing different uncertainty components present in air transportation system. Generally, uncertainty exists in two major forms, i.e., capacity uncertainty and demand uncertainty, which result in the stochastic behavior of the Air Traffic Network (ATN). Demand uncertainty is one critical form, which has an adverse effect on ATFM. It is mainly due to the deviation in departure time and aircraft speed from their scheduled values, and can further lead to an unexpected arrival of aircraft at certain routes at unscheduled times, causing unanticipated demands for those routes. The uncertainty of demand imposes several difficulties in air transportation systems, including higher workloads for air traffic controllers, higher delays, travel costs, as well as safety risks. Thus, in the initial part of the thesis, we propose a preliminary flight routing and scheduling scheme, while considering both departure and speed uncertainties present in ATN. Following robust optimization, we ensure that capacity violations are eliminated from the system while maintaining the required in-trail separation between aircraft even under the uncertainty of demand. Similar to the demand, the uncertainty of capacity is also another critical form of uncertainty present in air transportation system, and it is primarily due to unexpected weather situations, emergency conditions, controller workload variations, etc. Thus, in this thesis, we further propose a framework for en-route ATFM, while scrutinizing uncertainties in en-route capacity and demand and their imbalance, via a chance constraint-based probabilistic approach. The proposed framework plays a key role in ensuring the safety of the overall air transportation system in terms of maintaining the safety separation between flights and constraining the capacity of the sectors as well. Moreover, a flight level assignment and a speed optimization scheme are proposed based on the Base of Aircraft Data (BADA) of the European Organization for the Safety of Air Navigation (EUROCONTROL) with the objective of minimizing the fuel consumption. The model further minimizes the overall expected delay of the network using the control actions of ground holding, speed control, rerouting, and flight cancellations. At the implementation stage, two phases of ATFM, i.e., the pre-tactical phase and tactical phase, are considered, in which the former focuses on generating optimal trajectories, and the latter focuses on real-time updates of flight plans. However, due to the flight-by-flight structure, the above models scale poorly for large-scale systems. To overcome this drawback, especially anticipating the high volume of air traffic in the future, we finally present a two-stage architecture, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage decomposes them into individual flight plans. Due to the flow-based modeling structure at the first stage, it enhances the scalability while taking into account constraints of the link, sector and airport capacities and also the waypoint flow rate. The second stage ensures that the flights are safely separated with the required in-trail separation, thus, enhances the safety of the overall system. Acknowledging the importance of computational complexity along with the optimality, several solution approaches are proposed for above optimization problems, which are primarily base on the concept of maximum independent set, in which the overall problem is decomposed into a set of subproblems based on their independence, and further optimized parallelly resembling a greedy approach, yet with a tighter closeness to the global optimality. Furthermore, each ATFM framework is evaluated in terms of flight delays and possible conflicts with simplified and realistic case studies. Doctor of Philosophy 2020-05-28T05:37:33Z 2020-05-28T05:37:33Z 2020 Thesis-Doctor of Philosophy Gammana Guruge, N. S. (2020). Real-time flight scheduling for optimal air traffic flow management. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/140363 10.32657/10356/140363 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University