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 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 optimisation. 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 paper, 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 optimisation 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.Output Status: Forthcomin

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 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 optimisation. 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 paper, 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 optimisation 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

A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process

Position-controlled growth of GaN nanowires and nanotubes on diamond by molecular beam epitaxy

In this work the position-controlled growth of GaN nanowires (NWs) on diamond by means of molecular beam epitaxy is investigated. In terms of growth, diamond can be seen as a model substrate, providing information of systematic relevance also for other substrates. Thin Ti masks are structured by electron beam lithography which allows the fabrication of perfectly homogeneous GaN NW arrays with different diameters and distances. While the wurtzite NWs are found to be Ga-polar, N-polar nucleation leads to the formation of tripod structures with a zinc-blende core which can be efficiently suppressed above a substrate temperature of 870 °C. A variation of the III/V flux ratio reveals that both axial and radial growth rates are N-limited despite the globally N-rich growth conditions, which is explained by the different diffusion behavior of Ga and N atoms. Furthermore, it is shown that the hole arrangement has no effect on the selectivity but can be used to force a transition from nanowire to nanotube growth by employing a highly competitive growth regime