Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach

• URL: http://arxiv.org/abs/2111.09139v1
• Date: Wed, 17 Nov 2021 14:23:54 GMT
• Title: Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach
• Authors: Erik Vargo, Alex Tien, Arian Jafari
• Abstract summary: This paper proposes a novel approach to predict and determine whether the average taxi-out time at an airport will exceed a pre-defined threshold within the next hour of operations. A computer vision-based model is proposed that incorporates airport surface data in such a way that adaptation-specific information is inferred implicitly and automatically by Artificial Intelligence (AI)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper proposes a novel approach to predict and determine whether the average taxi- out time at an airport will exceed a pre-defined threshold within the next hour of operations. Prior work in this domain has focused exclusively on predicting taxi-out times on a flight-by-flight basis, which requires significant efforts and data on modeling taxiing activities from gates to runways. Learning directly from surface radar information with minimal processing, a computer vision-based model is proposed that incorporates airport surface data in such a way that adaptation-specific information (e.g., runway configuration, the state of aircraft in the taxiing process) is inferred implicitly and automatically by Artificial Intelligence (AI).

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