An Interval Type-2 Fuzzy Logic-Based Map Matching Algorithm for Airport Ground Movements

Airports and their related operations have become the major bottlenecks to entire air traffic management system, raising predictability, safety, environmental concerns. One of underpinning techniques for digital sustainable transport is airport ground movement optimization. Currently, real data made freely available majority aircraft at many airports. However, recorded not accurate enough due measurement errors general uncertainties. In this article, we aim develop a new interval type-2 fuzzy logic-based map matching algorithm, which can match each raw point correct segment. To aim, first specifically design set Sugeno rules associated rule weights, as well model output, based on preliminary experiments sensitivity tests. Then, membership functions are fine-tuned by particle swarm optimization algorithm. Moreover, an extra checking step using further integrated improve accuracy. Using real-world Hong Kong airport, compared developed algorithm with other known algorithms. Experimental results show that designed potential handle uncertainties, effectively The proposed demonstrated be robust achieve best accuracy over 96% without compromising run time.