• Aircraft taxi time prediction: Comparisons and insights

      Ravizza, Stefan; Chen, Jun; Atkin, Jason A. D.; Stewart, Paul; Burke, Edmund K.; University of Lincoln (Elsevier, 2014-01)
      The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1 min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.
    • Toward a more realistic, cost-effective, and greener ground movement through active routing: A multiobjective shortest path approach

      Chen, Jun; Atkin, Jason A. D.; Locatelli, Giorgio; Weiszer, Michal; Ravizza, Stefan; Stewart, Paul; Burke, Edmund K.; University of Derby (IEEE, 2016-10-31)
      This paper draws upon earlier work, which devel- oped a multiobjective speed profile generation framework for unimpeded taxiing aircraft. Here, we deal with how to seamlessly integrate such efficient speed profiles into a holistic decision- making framework. The availability of a set of nondominated unimpeded speed profiles for each taxiway segment, with respect to conflicting objectives, has the potential to significantly impact upon airport ground movement research. More specifically, the routing and scheduling function that was previously based on distance, emphasizing time efficiency, could now be based on richer information embedded within speed profiles, such as the taxiing times along segments, the corresponding fuel consumption, and the associated economic implications. The economic implica- tions are exploited over a day of operation, to take into account cost differences between busier and quieter times of the airport. Therefore, a more cost-effective and tailored decision can be made, respecting the environmental impact. Preliminary results based on the proposed approach show a 9%–50% reduction in time and fuel respectively for two international airports: Zurich and Manchester. The study also suggests that, if the average power setting during the acceleration phase could be lifted from the level suggested by the International Civil Aviation Organization, ground operations may simultaneously improve both time and fuel efficiency. The work described in this paper aims to open up the possibility to move away from the conventional distance-based routing and scheduling to a more comprehensive framework, capturing the multifaceted needs of all stakeholders involved in airport ground operations.
    • The trade-off between taxi time and fuel consumption in airport ground movement

      Ravizza, Stefan; Chen, Jun; Atkin, Jason A. D.; Burke, Edmund K.; Stewart, Paul; University of Lincoln (2013-02-13)
      Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport's surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.