Integration of weather information into the dispatcher pre-flight route selection process

Dorneich, Michael C.; Olofinboba, Olu; Pratt, Steve; Feyereisen, Thea

doi:10.1109/dasc.2002.1052913

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Prior to departure, they typically examine multiple information sources and integrate the sources to create an initial flight plan. More specifically, the pre-flight planning process is illustrated as follows [18]: 1) enter all necessary information (e.g., origin and destination) into a following reasons: First, the request may be denied by the ARTCC because pilots do not have the same knowledge as the ARTCC. Second, the request may subsequently be deferred by the ARTCC because pilots today are not typically allowed to monitor for optimization opportunities with weather information that is continuously updated.…”

Section: Ground-based Flight Planning Frameworkmentioning

confidence: 99%

Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Kim

Justin

Mavris

et al. 2022

Journal of Aerospace Information Systems

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Date approved: April 23, 2021 So, whether you eat or drink, or whatever you do, do all to the glory of God. Corinthians 10:31For the glory of God ACKNOWLEDGMENTSAs I am now rounding the last corner before the end of my academic journey, I would like to take a few moments to sincerely thank all of those who have supported me in numerous different ways. Honestly, as there are so many people to list, this section has been one of the hardest parts of this dissertation for me. This journey would have not been possible without the dedication and support of my advisor, committee members, colleagues, friends, mentors, and family. First of all, I would like to express my sincere gratitude to my advisor, Prof. Dimitri Mavris. He has been an excellent mentor since I joined the Aerospace Systems Design Laboratory (ASDL) as a Ph.D. student in Fall 2015. I am grateful for the opportunities he has provided me -for teaching me the process of research, for recruiting me as a graduate research/teaching assistant, for allowing me to have summer internship experiences, for making me participate in various research projects, and, most importantly, for trusting me to complete this dissertation. I would not have succeeded in completing my academic goals without his continuous guidance and support. I could not have asked for a more supportive advisor in my life. I have been truly blessed to work with the best advisor. Thanks Doc for your guidance, patience, encouragement, and support. I will never forget what you have done for me.I would also like to express my profound appreciation to my committee members: Prof.

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Section: Ground-based Flight Planning Frameworkmentioning

confidence: 99%

Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Kim

Justin

Mavris

et al. 2022

Journal of Aerospace Information Systems

View full text Add to dashboard Cite

show abstract

“…Prior to departure time, they typically examine multiple information sources and integrate the sources to create an initial flight plan. More specifically, an initial flight plan is created based on the following procedures [3]: 1) enter all information (e.g., origin-destination) into a system, 2) find a recommended route from a list of company routes based on the information, 3) see if the recommended route penetrates areas of convective weather, 4) try to pick another route from a list of company routes if the recommended route penetrates any area, 5) create a new route based on multiple information sources if none of the company routes are available, and 6) release the flight plan. Figure 1 shows an example of the flight plan document.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning

Kim

Zhang

Briceño

et al. 2021

AIAA Scitech 2021 Forum

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Many research groups have been committed to developing numerical models for weather forecasts. The models are currently used to predict weather patterns and trends in the aviation industry. In particular, pilots receive wind information predicted by the models and use the forecast to not only calculate how much fuel is needed for a flight but also optimize flight routes by seeking favorable winds. One potential issue is that the models provide relatively coarse wind information in both space and time, which potentially leads to inaccurate calculation of fuel consumption. This research aims to yield a continuous wind prediction model by combining a supervised learning algorithm with the Inverse Distance Weighting technique. Specifically, this research compares three different supervised learning algorithms that include Gaussian Process, Multi-Layer Perceptron, and Support Vector Machine to identify the most appropriate algorithm. The selected algorithm is then compared to a linear interpolation method that is widely used in current flight planning systems for obtaining continuous wind information. A case study is performed with the real Delta Airlines flight 1944 to evaluate the proposed methodology. The results show that 1) the Support Vector Machine provides a better wind prediction compared to the other models, 2) the supervised learning-based regression method performs better than the linear interpolation method in wind predictions, and 3) there are 16 seconds of difference between the real flight (12,117 seconds) and the simulated flight (12,101 seconds) for the cruise portion, indicating that the proposed methodology generates valid results as long as input wind data is provided accurately.

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An experimental evaluation of weather avoidance using route optimization as a decision aid

Dorneich

Olofinboba

Pratt

et al.

IEEE International Conference on Systems, Man and Cybernetics

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Integration of weather information into the dispatcher pre-flight route selection process

Cited by 3 publications

References 1 publication

Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning

An experimental evaluation of weather avoidance using route optimization as a decision aid

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