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

Abstract

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.

A more realistic approach for airport ground movement optimisation with stand holding

“…In previous ground movement research, average taxi speeds have been assumed to be influenced only by the aircraft type, however, taxi time research has shown that the aircraft type is not the most influencing factor upon taxi speeds (Ravizza et al 2012;Idris et al 2002;Rappaport et al 2009). Comparisons between ground movement tool results and the status quo at airports have previously been hard to analyse, due to the need for accurate taxi speed data.…”

“…An approach to more accurately predict taxi times for aircraft or, equivalently, their average speeds, was proposed in Ravizza et al (2012). Multiple linear regression was used to estimate a more accurate taxi speed prediction function and identify the factors which were most related to taxi speeds, such as total distance travelled, total turning angle and the number of other aircraft of different types which were moving around the airport at the time.…”

“…These predictions could then be used in a more advanced ground movement decision support system, such as the one described in this paper, which would itself model the effects of the interaction between aircraft (so these should not already be included in the taxi speed data). recently introduced an alternative fuzzy rule-based system (FRBS) approach to estimate taxi times at airports, using the factors which were identified in Ravizza et al (2012). Results from the FRBS were found to outperform the multiple linear regression approach when applied to the same airport, thus it was adopted and extended for this research.…”

“…The work by Ravizza et al (2012) was extended to include a pushback duration and the multiple linear regression approach indicated that this factor was significant for Zurich. The resulting taxi time prediction functions by were therefore further enhanced for this work adding a predicted pushback duration to the taxi time for the first edge for departures where the gate requires it, before being utilised to predict the taxi times.…”

Analysis of evolution of commercial air traffic CO2 emissions in the European Union

“…to control the demand. S Ravizza et al (2013) and Lordan, Sallan, and Valenzuela-Arroyo (2016) in their study highlight the necessity of more accurate airport taxi time estimation to reduce delays and environmental impacts of airport operations. According to CODA (Central Office for Delay Analysis) (Eurocontrol 2016a), delay causes are categorized to airline (e.g.…”