A Combined Statistical Approach And Ground Movement Model

A combined statistical approach and ground movement model for improving taxi time estimations at airports (Journal of the Operational Research Society)

Airlines and airports face several key challenges in the near future. Firstly, the number of flights is predicted to increase in the next few years (SESAR, 2006). Secondly, there is an increasing focus upon environmental considerations, and this is likely to increase in importance. Thirdly, the use of computerised tools is enabling increased aircraft utilisation, reduced idle times, and increased passenger connection options, leading to ever more complex and interlinked flight schedules. The on-time performance of flights at each airport and the earlier visibility of any delays (allowing corrective measures to be put into place) are becoming increasingly important, since many downstream flights can be affected by delays to single aircraft. Consequently, the operations at busy hub airports are experiencing an increased focus of attention, and this is likely to increase in the face of future challenges.

Total taxi times from stand/gate to runway are needed if advance predictions of take-off times are required, for use by en-route controllers (or decision support systems to help them) or for improving arrival time predictions for the destination airports, allowing the effects of any predicted delays to be mitigated. Taxi times are already needed by several existing search algorithms for take-off time prediction and take-off sequencing (Atkin et al, 2007; Eurocontrol, 2010) and for allocating appropriate stand holds to aircraft to absorb ground delay at the gate/ stand, decreasing the fuel burn and environmental effects (Burgain et al, 2009; Atkin et al, 2010a). Although the effects have been less well studied, taxi times are also useful for arrivals, being necessary for predicting stand/ gate arrival times, to ensure that adequate resources are available at the correct time (Eurocontrol, 2010). Taxi time predictions will become even more important if the effi- ciency of stand resource utilisation is to be improved in future. Current common practice is to use standard mean taxi times for each taxi source/destination pairs. A better understanding of the influencing factors, and a model to estimate such taxi times to a higher level of accuracy, would have positive effects for both the published approaches and the systems which are currently in use. The importance of the ground movement problem was explained in Atkin et al (2010b), highlighting how it links several other airport operations such as runway sequencing and gate assignment. Improved ground movement can increase on-time performance at airports, so ground movement simulations and optimisers are extremely useful. These usually explicitly model the interaction between aircraft (modelling delays due to other aircraft and any necessary re-routing on longer paths to avoid conflicts) and, thus, require predictions for taxi times which do not already include these elements (Gotteland and Durand, 2003; Smeltink et al, 2004; Balakrishnan and Jung, 2007; Roling and Visser, 2008; Lesire, 2010). The use of historic data would be preferable for calibrating models. However, such recorded data usually includes significant delays due to the interactions between aircraft. There are obvious benefits from being able to quantify the effects of this interaction and the model which is considered in this paper aims to provide this facility.

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