A combined statistical approach and ground movement model for improving taxi time estimations at airports

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. Improved on-stand time predictions (for improved resource allocation at the stands) and take-off time predictions (for improved airport-airspace coordination) both require more accurate taxi time predictions, as do the increasingly sophisticated ground movement models which are being developed. Calibrating such models requires historic data showing how long aircraft will actually take to move around the airport, but recorded data usually includes significant delays due to contention between aircraft. This research was motivated by the need to both predict taxi times and to quantify and eliminate the effects of airport load from historic taxi time data, since delays and re-routing are usually explicitly considered in ground movement models. A prediction model is presented here that combines both airport layout and historic taxi time information within a multiple linear regression analysis, identifying the most relevant factors affecting the variability of taxi times for both arrivals and departures. The promising results for two different European hub airports are compared against previous results for US airports.

[1]  Lance Sherry,et al.  Application of Reinforcement Learning Algorithms for Predicting Taxi-out Times , 2009 .

[2]  Hamsa Balakrishnan,et al.  A Framework for Coordinated Surface Operations Planning at Dallas-Fort Worth International Airport , 2007 .

[3]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[4]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[5]  Stefan Ravizza,et al.  Exploration of the ordering for a sequential airport ground movement algorithm , 2011 .

[6]  Charles Lesire.  Iterative Planning of Airport Ground Movements , 2010 .

[7]  Edmund K. Burke,et al.  The Airport Ground Movement Problem: Past and Current Research and Future Directions , 2010 .

[8]  Nicolas Durand,et al.  Genetic algorithms applied to airport ground traffic optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[9]  John-Paul Clarke,et al.  Collaborative Virtual Queue: Benefit Analysis of a Collaborative Decision Making Concept Applied to Congested Airport Departure Operations , 2009 .

[10]  John-Paul Clarke,et al.  Queuing Model for Taxi-Out Time Estimation , 2002 .

[11]  Paul Roling,et al.  Optimal Airport Surface Traffic Planning Using Mixed Integer Linear Programming , 2003 .

[12]  Edmund K. Burke,et al.  TSAT allocation at London Heathrow: the relationship between slot compliance, throughput and equity , 2010, Public Transp..

[13]  P. R. Waal,et al.  An Optimisation Model for Airport Taxi Scheduling , 2004 .

[14]  Hamsa Balakrishnan,et al.  Queuing Models of Airport Departure Processes for Emissions Reduction , 2009 .

[15]  M. Ball,et al.  Estimating Flight Departure Delay Distributions—A Statistical Approach With Long-Term Trend and Short-Term Pattern , 2008 .

[16]  D. M. Allen.  Mean Square Error of Prediction as a Criterion for Selecting Variables , 1971 .

[17]  Edmund K. Burke,et al.  A Statistical Approach for Taxi Time Estimation at London Heathrow Airport , 2011 .

[18]  J. Brian Gray,et al.  Introduction to Linear Regression Analysis , 2002, Technometrics.

[19]  Katy Griffin,et al.  Quantitative Analysis of Uncertainty in Airport Surface Operations , 2009 .

[20]  Edmund K. Burke,et al.  A more realistic approach for airport ground movement optimisation with stand holding , 2013, Journal of Scheduling.

[21]  Edmund K. Burke,et al.  Hybrid Metaheuristics to Aid Runway Scheduling at London Heathrow Airport , 2007, Transp. Sci..