Characterization and prediction of air traffic delays

Rebollo, Juan Luis Moreno; Balakrishnan, Hamsa

doi:10.1016/j.trc.2014.04.007

Cited by 238 publications

(156 citation statements)

References 14 publications

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“…Their findings revealed that the New York area is responsible for 15% of injected delays and 9% of propagated delays in the NAS. Rebollo and Balakrishnan used random forests and regression models to predict air traffic delays [20]. The authors examined their models using the 100 most frequently delayed links in the NAS.…”

Section: Literature Reviewmentioning

confidence: 99%

Airline delay prediction by machine learning algorithms

Khaksar

Sheikholeslami

2017

Scientia Iranica

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Flight planning, as one of the challenging issue in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through proposed approaches that are based on machine learning algorithms. Parameters that enable the effective estimation of delay are identified, after which Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method are applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a U.S. flight dataset and then refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks are visibility, wind, and departure time, whereas those affecting delay in Iranian airline flights are fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurance and magnitude in both the whole-network US and Iranian. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.

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Section: Literature Reviewmentioning

confidence: 99%

Airline delay prediction by machine learning algorithms

Khaksar

Sheikholeslami

2017

Scientia Iranica

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“…6). This was designed so that the most important predictor had an importance of 100 (Rebollo, Balakrishnan 2014).…”

Section: Modelingmentioning

confidence: 99%

Prediction of traffic sign vandalism that obstructs critical messages to drivers

Khalilikhah

Heaslip

2017

Transport

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Abstract.A critical deficiency in any one or a combination of three transportation system characteristics: the driver, roadway, or vehicle can contribute to an elevated crash risk for the motoring public. Traffic signs often convey critical information to drivers. However, traffic signs are only effective when clearly visible and legible. Traffic sign vandalism that is exclusively the results of humans causes both sign legibility and visibility to deteriorate. Transportation agencies spend a significant amount of money to repair or replace vandalized signs. This study was conducted to identify which traffic signs are more vulnerable to vandalism. To do this, a mobile-based vehicle collected data of over 97000 traffic signs managed by the Utah Department of Transportation (UDoT), US. The vandalized signs were identified by a trained operator through inspection of daytime digital images taken of each individual sign. Location data obtained from online sources combined with the traffic sign data were imported into ArcGIS to acquire localized conditions for each individual sign. According to the chi-square test results, the association between vandalism and traffic sign attributes and localized conditions, including background color, size, mount height, exposure, land cover, and road type was evident. After employing the random forests model, the most important factors in making signs vulnerable to vandalism were identified.

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“…Many candidate scores can be used to compare two networks; their suitability can vary depending on the application being considered [17], [12]. For example, one can compare the edge weights of two graphs, that is, use the Euclidean distance between the vectors of edge weights as a measure of graph similarity [14]. However, such a representation requires O(n 2 ) parameters to represent a graph, where n is the number of nodes.…”

Section: Features For Comparing Networkmentioning

confidence: 99%

“…In contrast to previous work on delay propagation in air traffic networks [9], [10], our primary focus is not to cluster network nodes (airports) on the basis of their similarities, but instead, to cluster the networks themselves (i.e., the system state at different times) on the basis of their similarities [11], [12], [13]. In earlier work, the NAS delay state was characterized using the edge weights of the network; however, the connectivity and network structure of the system were not explicitly considered [14].…”

Section: Introductionmentioning

confidence: 99%

Clusters and communities in air traffic delay networks

Gopalakrishnan

Balakrishnan

Jordan

2016

2016 American Control Conference (ACC)

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Abstract-The air transportation system is a network of many interacting, capacity-constrained elements. When the demand for airport and airspace resources exceed the available capacities of these resources, delays occur. The state of the air transportation system at any time can be represented as a weighted directed graph in which the nodes correspond to airports, and the weight on each arc is the delay experienced by departures on that origin-destination pair. Over the course of any day, the state of the system progresses through a timeseries, where the state at any time-step is the weighted directed graph described above.This paper presents algorithms for the clustering of air traffic delay network data from the US National Airspace System, in order to identify characteristic delay states (i.e., weighted directed graphs) as well as characteristic types-ofdays (i.e., sequences of such weighted directed graphs) that are experienced by the air transportation system. The similarity of delay states during clustering are evaluated on the basis of not only the in-and out-degrees of the nodes (the total inbound and outbound delays), but also network-theoretic properties such as the eigenvector centralities, and the hub and authority scores of different nodes. Finally, the paper looks at community detection, that is, the grouping of nodes (airports) based on their similarities within a system delay state. The type of day is found to have an impact on the observed community structures.

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Characterization and prediction of air traffic delays

Cited by 238 publications

References 14 publications

Airline delay prediction by machine learning algorithms

Airline delay prediction by machine learning algorithms

Prediction of traffic sign vandalism that obstructs critical messages to drivers

Clusters and communities in air traffic delay networks

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