Airport Ground Movement Problem: Minimization of Delay and Pollution Emission

Adacher, Ludovica; Flamini, Marta; Romano, Elpidio

doi:10.1109/tits.2017.2788798

Cited by 32 publications

(12 citation statements)

References 33 publications

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“…He extended the problem from single paths to alternate paths and creatively added punctuality index into the objective function. Adacher et al [9] proposed a general approach to optimize the airport ground movement problem, with the goal of minimizing total routing taxi delay and the pollution emissions. Evertse and Visser [10] designed a realtime airport surface movement planning tool, which was capable of creating conflict-free timed taxiing trajectories for aircraft, while minimizing the total emission of pollutants, fuel usage, departure slot time deviations and taxi time.…”

Section: Literature Reviewmentioning

confidence: 99%

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

Jiang

Liu

et al. 2021

IEEE Access

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Section: Literature Reviewmentioning

confidence: 99%

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

Jiang

Liu

et al. 2021

IEEE Access

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“…Researchers have studied flight delays based on various views, such as aircraft taxiing delay [ 6 , 7 ], distributions of departure or arrival delays [ 1 , 2 ], and how delay propagated through airports and airlines [ 3 , 4 ].…”

Section: Related Workmentioning

confidence: 99%

“…Ludovica Adacher et.al. dealt with the problem of routing and scheduling aircraft ground movement operations to minimize the total routing taxiing delay [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

“…However, in our work, we build a graph sequence for each aircraft by day. And for each flight, according to the existing research [ 6 , 7 ] and the field experience of the data providing company, we used the time window, within half an hour before and after departure or arrive, to obtain neighbors with the same departure airport or the same arrival airport.…”

Section: Model: Sgdanmentioning

confidence: 99%

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SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

Mei

Liu

Pan

et al. 2020

Sensors

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There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.

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“…Alternative efforts including [13,17,20,36] formulated Ground Movement as a mixed-integer linear programming problem. A recent approach using pre-computed routes formulated Ground Movement as a job-shop scheduling problem [1].…”

Section: Related Workmentioning

confidence: 99%

A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

Brownlee

Woodward

Weiszer

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself.We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-firstserved ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches.

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Airport Ground Movement Problem: Minimization of Delay and Pollution Emission

Cited by 32 publications

References 33 publications

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

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