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dc.contributor.authorChow, Hong Weien_US
dc.contributor.authorLim, Zhi Junen_US
dc.contributor.authorAlam, Sameeren_US
dc.identifier.citationChow, H. W., Lim, Z. J. & Alam, S. (2021). Data-driven runway occupancy time prediction using decision trees. 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC).
dc.description.abstractWith an increasing amount of flights, the demand for runways at airports increases as well. Innovative mechanisms are required to maximise the use of a runway such that the demand can be met. Such mechanisms include the prediction of Runway Occupancy Time (ROT), so that the Air Traffic Controllers (ATCs) are able to gauge how much time a particular flight needs on the runway. This allows them to prepare the next flight for the runway and effectively reduce the buffer times between flights, thus increasing the overall efficiency of the runway. The objective of this paper is to develop an explainable machine learning model to predict Runway Occupancy Time. The Decision Tree Regressor was chosen for this study and its performance was compared to other more complicated machine learning models. The Decision Tree Regressor, unlike the other machine learning algorithms, provides explicit rules on how the predictions of the ROT is derived. An example of a generated rule for runway 02L of Singapore Changi Airport is that if an aircraft is a medium aircraft from airline XXX, arriving between 2100 and 2159 hours UTC, with an approach speed of more than 83.344 m/s at the final approach fix, and with the trailing aircraft traveling slower, the predicted ROT will be 42.6 seconds. Results show that the Decision Tree Regressor has the least runtime out of all the models at 0.28 minutes during training and its prediction capabilities are also on par with the rest of the machine learning models. The Root Mean Square Error for the Decision Tree Regressor is 5.96 seconds, which is only 0.20 seconds away from the best performing machine learning model. This, coupled with the rules that the Decision Tree Regressor can provide, makes it easier for end-users to to accept the prediction results without compromising on the accuracy. Permutation importance was also applied to the decision tree, providing an insight into what affects the ROT the most.en_US
dc.description.sponsorshipCivil Aviation Authority of Singapore (CAAS)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
dc.subjectEngineering::Aeronautical engineering::Aviationen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleData-driven runway occupancy time prediction using decision treesen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.conference2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC)en_US
dc.contributor.researchAir Traffic Management Research Instituteen_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsRunway Occupancy Timeen_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsDecision Treeen_US
dc.citation.conferencelocationSan Antonio, Texas, USAen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and the Civil Aviation Authority of Singapore.en_US
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