Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay

The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.

Download Full-text

Quantification and Forecasting of Cumulonimbus (Cb) Clouds Direction, Nebulosity and Occurrence With Autoregression Using 2018-2020 Dataset From Yaounde-nsimalen

10.21203/rs.3.rs-1174494/v1 ◽

2022 ◽

Author(s):

Mbucksek Blaise Ringnwi ◽

DAÏKA Augustin ◽

TSEDEPNOU Rodrigue ◽

Bon Firmin André ◽

Kossoumna Libaa Natali

Keyword(s):

Traffic Management ◽

Situational Awareness ◽

Air Traffic Management ◽

Future Research ◽

Sarima Model ◽

Augmented Dickey Fuller ◽

Data Points ◽

Auto Regression ◽

Autoregressive Neural Network

Abstract This works reports the quantification and forecasting of Cumulonimbus (Cb) clouds direction, nebulosity and occurrence with auto regression using 2018-2020 dataset from Yaoundé –Nsimalen of Cameroon. Data collected for October 2018-2020 consisted of 2232 hourly observations. Codes were written to automatically align, multi-find and replace data points in excel to facilitate treating big datasets. The approach included quantification of direction generating time series from data and determination of model orders using the correlogram. The coefficients of the SARIMA model were determined using Yule-Walker equations in matrix form, the Augmented Dickey Fuller test (ADF) was used to check for stationarity assumption, Portmanteau test to check for white noise in residuals and Shapiro-Wilk test to check normality assumptions. After writing several algorithms to test different models, an Autoregressive Neural Network (ANN) was fitted and used to predict the parameters over window of 2 weeks. Autocorrelation Function (ACF) shows no correlation between residuals, with p ≤ 0.05, fitting the stationarity assumption. Average performance is 80%. A stationary wavelike occurrence of the direction has been observed, with East as the most frequented sector. Forecast of Cb parameters is important in effective air traffic management, creating situational awareness and could serve as reference for future research. The method of decomposition could be made applicable in future research to quantify/forecast cloud directions.

Download Full-text

Safety Culture and Safety-Relevant Behavior in Air Traffic Management

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000068 ◽

2015 ◽

Vol 5 (1) ◽

pp. 3-17 ◽

Cited By ~ 5

Author(s):

Michaela Schwarz ◽

K. Wolfgang Kallus

Keyword(s):

Safety Culture ◽

Traffic Management ◽

Service Providers ◽

Five Factor Model ◽

Principal Component ◽

Air Traffic Management ◽

Air Traffic ◽

Psychometric Validation ◽

Safety Attitudes ◽

Culture Development

Since 2010, air navigation service providers have been mandated to implement a positive and proactive safety culture based on shared beliefs, assumptions, and values regarding safety. This mandate raised the need to develop and validate a concept and tools to assess the level of safety culture in organizations. An initial set of 40 safety culture questions based on eight themes underwent psychometric validation. Principal component analysis was applied to data from 282 air traffic management staff, producing a five-factor model of informed culture, reporting and learning culture, just culture, and flexible culture, as well as management’s safety attitudes. This five-factor solution was validated across two different occupational groups and assessment dates (construct validity). Criterion validity was partly achieved by predicting safety-relevant behavior on the job through three out of five safety culture scores. Results indicated a nonlinear relationship with safety culture scales. Overall the proposed concept proved reliable and valid with respect to safety culture development, providing a robust foundation for managers, safety experts, and operational and safety researchers to measure and further improve the level of safety culture within the air traffic management context.

Download Full-text