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