Comparison of various temporal air traffic flow management models in critical scenarios

Dalmau, Ramon; Gawinowski, Gilles; Anoraud, Camille

doi:10.1016/j.jairtraman.2022.102284

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…In this part, we compare the proposed method with an optimisation method to show the difference between the proposed method's optimisation performance and the DCB instance's mathematical boundary for readers' reference. We refer to the state-ofthe-art DCB research (Dalmau et al, 2022) to build an integer linear programming (ILP) model based on this study's DCB instance. Eq.…”

Section: Comparison With An Optimisation Methodsmentioning

confidence: 99%

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

CHEN

2022

SSRN Journal

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Section: Comparison With An Optimisation Methodsmentioning

confidence: 99%

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

CHEN

2022

SSRN Journal

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“…Several studies [18][19][20][21][22] have attempted to predict and analyze delay trends in the airline industry, allowing airlines and airports to implement the necessary adjustments to their operations and reduce the overall impact of delays on passenger experience. Traditional statistical models, such as time series analysis and regression, have been used to predict flight delays [23].…”

Section: Delay Predictionmentioning

confidence: 99%

Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model

Yhdego,

Wei,

Erlebacher

et al. 2023

Mathematics

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Some delay patterns are correlated to historical performance and can reflect the trend of delays in future flights. A typical example is the delay from an earlier inbound flight causing delayed departure of a connecting and downstream outbound flight. Specifically, if an arriving aircraft arrives late, the connecting airline may decide to wait for connecting passengers. Due to the consistent flow of passengers to various destinations during a travel season, similar delay patterns could occur in future days/weeks. Airlines may analyze such trends days or weeks before flights to anticipate future delays and redistribute resources with different priorities to serve those outbound flights that are likely to be affected by feeder delays. In this study, we use a hybrid recurrent neural network (RNN) model to estimate delays and project their impacts on downstream flights. The proposed model integrates a gated recurrent unit (GRU) model to capture the historical trend and a dense layer to capture the short-term dependency between arrival and departure delays, and, then, integrates information from both branches using a second GRU model. We trained and tuned the model with data from nine airports in North, Central, and South America. The proposed model outperformed alternate approaches with traditional structures in the testing phase. Most of the predicted delay of the proposed model were within the predefined 95% confidence interval. Finally, to provide operational benefits to airline managers, our analysis measured the future impact of a potentially delayed inbound feeder, (PDIF) in a case study, by means of identifying the outbound flights which might be affected based on their available connection times (ACTs). From an economic perspective, the proposed algorithm offers potential cost savings for airlines to prevent or minimize the impact of delays.

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“…Post et al evaluated the operating conditions leading to the increased probability of an airport ATFM delay through Bayesian networks, and the results showed that the predicted arrival congestion index and the actual arrival congestion index were the indicators that had the highest impact on airport ATFM delays [7]. Ramon et al proposed three indicators to predict the trend of ATFM delays from the perspective of ATFM delay evolution trends, which were as follows: the expectation of an actual ATFM delay, the probability distribution of an ATFM delay, and the trend of ATFM delays [8]. Sergi et al categorized the causes of ATFM delays into those of airport traffic, airport capacity, network capacity, and operating slots.…”

Section: Introductionmentioning

confidence: 99%

Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm

Zhao,

Yuan,

Chen

2024

Aerospace

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Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.

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Comparison of various temporal air traffic flow management models in critical scenarios

Cited by 9 publications

References 18 publications

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model

Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm

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