详情

原文传递 Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques.

题名：	Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques.
作者：	Lee, H.
关键词：	Airports, Taxiing, Time measurement, Performance prediction, Prediction analysis techniques, Machine learning, Traffic control, Real time operation, Operations research
摘要：	Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.
总页数：	Lee, H.
报告类型：	科技报告