Abstract
Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10–20% over the original QPPTW.
Highlights
The aviation industry is experiencing sustained and long-term growth
This is because Quickest Path Problem with Time Windows (QPPTW) allocates the quickest routes assuming fixed taxi times, and without uncertainty added to the simulator, the routes taken by the aircraft will match these
An adaptive Mamdani fuzzy rule based system has been developed as the first attempt for accurate estimation of taxiing times and their associated uncertainty
Summary
The aviation industry is experiencing sustained and long-term growth. It is estimated that air traffic within the European Union will reach 1.5× 2012 levels by 2035 (EUROCONTROL, 2013). The Quickest Path Problem with Time Windows (QPPTW) algorithm (Ravizza et al, 2013) is extended to use these fuzzy times, generating multiple routes for different levels of uncertainty This allows the decision support system to find a route assignment that is robust in a range of situations, yet still uses a minimal time to complete the movement. The major contributions of this paper are: an adaptive Mamdani fuzzy rule based system (FRBS) from Ravizza et al (2014) is improved and extended to estimate taxi times and their uncertainties; and Fuzzy-QPPTW, an algorithm to allocate taxi routes to aircraft that are robust to taxi time uncertainty, is proposed.
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