Nextgen a proactive approach

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Nextgen a proactive approach

Open source software and crowd sourced data for operational performance analysis Author: To date the access to operational data in support of research claims or observed performance benefits is limited.

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Though the majority of related data sets are established and curated by government authorities, open data access is still in its infancy. ICAO promotes a performance based approach. In that light there is stronger interest in performing regional and international benchmarking comparisons.

To advance the state of the art it is important to establish common methods and tools. This paper reports on the conceptualization of the benchmarking process as a data analytical workflow and the supporting developments of an open-source data-analytical toolbox.

This feasibility study demonstrates the use of the process and open data to provide a framework under which official results can be validated.

The benefits of this approach have been demonstrated throughout the on-going regional benchmarking projects. The toolbox developed offers scope to expand the functionality and make operational ANS performance analyses Nextgen a proactive approach to a wider audience interested in applying harmonized global standards.

Karthik Gopalakrishnan, Hamsa Balakrishnan MIT abstract In this paper, we compare the performance of dif-ferent approaches to predicting delays in air traffic networks.

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We consider three classes of models: We also discuss the importance of selecting the right predictor variables, or features, in order to improve the performance of these algorithms.

The MJLS model, however, is better at predicting the actual delay levels on different links, and has a mean prediction error of 4. MJLS is also better at predicting outbound delays at the 30 major airports, with a mean error of 6.

The effect of temporal factors, and the spatial distribution of current delays, in predicting future delays are also compared. The MJLS model, which is specifically designed to capture aggregate air traffic dynamics, leverages on these factors and outperforms the ANN in predicting the future spatial distribution of delays.

In this manner, a tradeoff between model simplicity and prediction accuracy is revealed. We estimate both the mean and 90th percentile responses for two performance indicators: Specifically, we apply the method of Propensity Scores to estimate counterfactuals and compute the increase in hourly average airborne delay which would have resulted without a Ground Delay Program, using an eleven month span of hourly weather, traffic, and delay data at JFK.

Our introduction also summarizes the concepts of causal inference required for our analysis.

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We also offer suggestions to improve and extend our initial application of casual inference. Technical details of propensity score modeling are further covered in an appendix. Three sources of inefficiency are explored: Historical flight records are projected onto a small set of nominal trajectories clustered from historical data, and compared against the history of the potential causal factors.

Statistical models reveal the estimated influence of the factors. In this case, convective weather was the most influential factor in seeming to cause flights to deviate from what would have been a less costly trajectory. Winds and miles-in-trail restrictions are also important for some origin-destination pairs, but less significant than convective weather.

A systematical empirical study of air traffic operation in Guangzhou terminal airspace is conducted by collecting synchronized flight and air-ground communication data. Empirical results identify synchronized free, smooth, semi-stable and congested phase states from both flow and controllers performance evolutions.

Meta-cognition is explained as one of critical underlying mechanisms that drive the phase transitions. These novel findings will provide theoretical basis for aggregated air traffic flow modeling, decision support system design and tactical flow management.

Efficient boarding procedures have to consider both operational constraints and the individual passenger behaviour. In contrast to the handling processes of fuelling, catering and cleaning, the boarding process is mainly driven by the passenger and not by airport or airline employees.

Models for evaluating boarding sequences mainly depend more on assumptions concerning the individual passenger processes in the aircraft than on reliable field measurements. This paper provides a comprehensive set of operational data including classification of boarding times, passenger arrival times, time to store hand luggage, and passenger interactions as a fundamental basis for the calibration of boarding simulation models.

The concept aims to integrate systems and processes to provide improved levels of turnaround predictability and take-off time accuracy that are of benefit to the ground operation locally and the Network Manager Operations Centre NMOC respectively.

The local assessment was driven primarily by information shared by the first 17 fully implemented CDM airports.

Nextgen a proactive approach

Local benefits that were confirmed as part of the study include the reduction in average taxi-out times and push-back delays, increased ground handling resource utilisation and more expeditious recovery from adverse conditions.Advanced Safety Management Systems (SMS) in Civil Aviation (Classroom, 5 days) A Safety Management System (SMS) is an essential part of .

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