Predeparture Uncertainty and Prediction Performance in Collaborative Routing Coordination Tools

Quality & Reliability Eng

2010

In many applications, decision making under uncertainty often involves two steps-prediction of a certain quality parameter or indicator of the system under study and the subsequent use of the prediction in choosing actions. The prediction process is severely challenged by highly dynamic environments that particularly involve sequential decision making, such as air traffic control at airports in which congestion prediction is critical for smooth departure operations. Taxi-out time of a flight is an excellent indicator of surface congestion and is a quality parameter used in the assessment of airport delays. The regression, queueing, and moving average models have been shown to perform poorly in predicting taxi-out times because they are slow in adapting to the changing airport dynamics. This paper presents an approximate dynamic programming approach (reinforcement learning, RL) to taxi-out time prediction. The taxi-out prediction performance was tested on flight data obtained from the Federal Aviation Administration's (FAA) Aviation System Performance Metrics (ASPM) database on Detroit International (DTW), Washington Reagan National (DCA), Boston (BOS), New York John F. Kennedy (JFK) and Tampa International (TPA) airports. For example, at the Boston airport (presented in detail) the prediction accuracy by RL model was 14% higher than the queueing model and 39% higher than a running-average model. In general, the RL model was 35-50% more accurate than the regression model for all of the above airports.

Section: Literature Review Of Prediction Models For Taxi‐out Timementioning

confidence: 99%

Improving quality of prediction in highly dynamic environments using approximate dynamic programming

Quality & Reliability Eng

2010

“…Many statistical models that consider the probability distribution of departure delays and aircraft takeoff time in order to predict taxi-time have evolved in recent years [9,10]. Other research that develops a departure planning tool for departure time prediction is available in [11][12][13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Taxi-out times with a reinforcement learning algorithm

2008 IEEE/AIAA 27th Digital Avionics Systems Conference

et al. 2008

Flight delays have a significant impact on the nation's economy. Taxi-out delays in particular constitute a significant portion of the block time of a flight. In the future, it can be expected that accurate predictions of 'wheels-off' time may be used in determining whether an aircraft can meet its allocated slot time, thereby fitting into an en-route traffic flow. Without an accurate taxi-out time prediction for departures, there is no way to effectively manage fuel consumption, emissions, or cost. Dynamically changing operations at the airport makes it difficult to accurately predict taxiout time. This paper describes a method for estimating average taxi-out times at the airport in 15 minute intervals of the day and at least 15 minutes in advance of aircraft scheduled gate push-back time. A probabilistic framework of stochastic dynamic programming with a learning-based solution strategy called Reinforcement Learning (RL) has been applied. Historic data from the Federal Aviation Administration's (FAA) Aviation System Performance Metrics (ASPM) database were used to train and test the algorithm. The algorithm was tested on John F. Kennedy International airport (JFK), one of the busiest, challenging, and difficult to predict airports in the United States that significantly influences operations across the entire National Airspace System (NAS). Due to the nature of departure operations at JFK the prediction accuracy of the algorithm for a given day was analyzed in two separate time periods (1) before 4:00 P.M and (2) after 4:00 P.M. On an average across 15 days, the predicted average taxi-out times matched the actual average taxi-out times within ±5 minutes for about 65 % of the time (for the period before 4:00 P.M) and 53 % of the time (for the period after 4:00 P.M). The prediction accuracy over the entire day within ±5 minutes range of accuracy was about 60 %. Further, application of the RL algorithm to estimate taxi-out times at airports with multidependent static surface surveillance data will likely improve the accuracy of prediction. The implications of these results for airline operations and network flow planning are discussed.

“…DEPARTS uses a near-real-time airport information management system to provide its key inputs, which it collects from the airport's surface movement advisor, and recommends optimal runway assignment, taxi clearance, and takeoff clearance times for individual departures. Other research develops a departure planning tool for departure time prediction (4)(5)(6)(7)(8).…”

Section: Literature Reviewmentioning

confidence: 99%

Airport Taxi-Out Prediction Using Approximate Dynamic Programming

Transportation Research Record: Journal of the Transportation R

2008

Flight delay is one of the pressing problems that have far-reaching effects on society and the nation's economy. A primary cause of flight delay in the National Airspace System is high taxi-out times (time between gate push-back and wheels-off) at major airports. Accurate prediction of taxi-out time is needed to make downstream schedule adjustments and for better departure planning, which could mitigate delays, emissions, and congestion on the ground. However, accurate prediction of taxi-out time is difficult because of uncertainties associated with the dynamically changing airport operation. A novel stochastic approximation scheme based on reinforcement learning (RL) is presented for predicting taxi-out times in the presence of weather and other departure-related uncertainties. The prediction problem is cast in a probabilistic framework of stochastic dynamic programming and solved by using approximate dynamic programming approaches (particularly RL). The strengths of the method are that it is nonparametric, unlike regression models with fixed parameters, it is highly adaptable to the dynamic airport environment since it is learning based, it is scalable, it is inexpensive since it does not need highly sophisticated surface management systems, and it can effectively handle uncertainties because of the probabilistic framework. Taxi-out prediction performance was tested on data obtained from the FAA Aviation System Performance Metrics database on Detroit International and Washington Reagan National Airports. Results show that the root-mean-square prediction error calculated 15 min before gate departure time is on average 2.9 min for about 80% of the predicted flights.