Factors influencing charter flight departure delay

Fernandes, Nuno De Oliveira; Moro, Sérgio; Costa, Carlos J.; Aparício, Manuela

doi:10.1016/j.rtbm.2019.100413

Cited by 10 publications

(5 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Table 4 , we can see that SGDAN achieves the best performance. Comparing with Reference [ 5 ], SGDAN’s results far exceed it. It is due to the fact that SGDAN not only takes into account the delayed propagation in the flight sequences, but also captures the spatial factors which impact flights.…”

Section: Methodsmentioning

confidence: 54%

“…Based on the existing paper [ 5 ] and the experience from the data-providing company, that is, Umetrip, we extracted some detailed features. Before giving the definition of the final features, we first give some definitions of different attributes.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the methods [ 15 , 16 ], we also compared other methods for the same task. Zhongbin Li et al and Young Jin Kim et al use long short term memory (LSTM) network to predict flight delay [ 41 , 42 ] and Nuno Fernandes et al extracts a series of features and uses machine learning, that is, Neural Network (NN), Support Vector Machines (SVM) and Random Forest (RF) to predict [ 5 ]. These models were evaluated with accuracy in the paper.…”

Section: Methodsmentioning

confidence: 99%

“…The existing research mainly focuses on flight delay or no delay [ 5 ], the distribution of delays [ 1 , 2 ], and the delay propagation through airlines and airports [ 3 , 4 ]. But in many cases, the factors affecting flight delay are not independent.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Mei

Liu

Pan

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 54%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mei

Liu

Pan

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Different types of multimodal transport networks have been modeled in several empirical studies, as shown in Table 1 [39]. Note that air travel can be modeled as a heterogeneous mode of transport, as different air travel companies constitute different layers of a network [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing robustness in multimodal transportation systems: a case study in Lisbon

Aparício

Arsénio

Henriques

2022

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Introduction Worldwide public transport systems are exposed to disruptions caused by malfunctions, accidents, maintenance, reduced fleet, and disasters, compromising mobility. Transport networks’ multimodal planning and management can be explored to increase their robustness against these events. In this context, this research paper proposes and empirically compares methods to assess the robustness of a multimodal transport network, looking at aspects regarding the single-mode and multimodal network topology. Materials and Methods We hypothesize that the appropriate multilayered and traffic sensitive modeling of a multimodal transport network can help characterize robustness and further unravel vulnerabilities related to the integration of different transport modes. Using metric-based targeting, we evaluate how the network decreases performance when simulating failures on stations and pathways using different scenarios. The following six extraction strategies for nodes and edges were used in the simulation: Random removal; Initial Degree removal; Initial Betweenness removal; Recalculate Degree removal; Recalculate Betweenness removal; and Multimodal Hubs removal. Lisbon’s public transport is used as a case study and is modeled as a multiplex network integrating eight different modes of transport. Proposing a novel normalized version of assessing the impact of failures, we were able to compare side by side the robustness of each modality layer, regardless of their size. Lastly, we simulate cascading events such as the breakdown of an entire transportation line. Conclusions Using different ways to induce failures in the network, we observe that to leave all nodes completely disconnected, we would need to remove about half the network nodes, highlighting the robustness of the Lisbon public transport network. Comparing different failure scenarios, methods that rely on recalculating network metrics yield a higher impact on the network robustness assessment. The impact of different events is quantified, showing that failures in stations are generally more dangerous than in pathways and offering views on the consequences of deactivating particular network modules. Overall, the results of this study allow decision-makers to gain further understanding of the topological vulnerabilities of a transportation network.

show abstract

A decision tree model for the prediction of the stay time of ships in Brazilian ports

Abreu

Maciel

Alves

et al. 2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Factors influencing charter flight departure delay

Cited by 10 publications

References 62 publications

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

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

Assessing robustness in multimodal transportation systems: a case study in Lisbon

A decision tree model for the prediction of the stay time of ships in Brazilian ports

Contact Info

Product

Resources

About