Accessibility Analysis of Unmanned Aerial Vehicles Near Airports with a Four-Dimensional Airspace Management Concept

The demand for UAS operations is increasing in recent years, as well as the traffic volume of civil aviation. The operations of both traffic modules are based on the utilization of airspace resources. Currently Unmanned Aircraft Systems (UAS) are not allowed to operate close to airports, where potential conflicts between UAS and manned aircraft may happen. This rule is constraining the application of UAS. Therefore, it is necessary to study the current airspace utilization pattern near airports, before the boundary determination of UAS operation to allow the UAS accessing airspace safely without conflicts. In this paper, a data-driven analysis on historical trajectories at Changi Airport in Singapore was carried out. Trajectory data have been clustered to recognize the utilization patterns of airspace. Discussions on the boundary of UAS operation were presented based on both current airspace utilization patterns and the estimated capacity. The airspace utilization was further modeled in an urban airspace management framework, and quantifiable area for potential UAS operation was analyzed. As this is the first research study to present the initial concept, more operational and other factors should be considered in the future study for the generation of potential boundary of UAS operations.

[1]  Marcus Johnson,et al.  UAS Traffic Management (UTM) Concept of Operations to Safely Enable Low Altitude Flight Operations , 2016 .

[2]  Frank Rehm,et al.  Clustering of Flight Tracks , 2010 .

[3]  Kin Huat Low,et al.  Preliminary Concept of Adaptive Urban Airspace Management for Unmanned Aircraft Operations , 2018 .

[4]  Mayara Condé Rocha Murça,et al.  Identification, Characterization, and Prediction of Traffic Flow Patterns in Multi-Airport Systems , 2019, IEEE Transactions on Intelligent Transportation Systems.

[5]  Arnab Majumdar,et al.  A framework for the optimization of terminal airspace operations in Multi-Airport Systems , 2018 .

[6]  Kin Huat Low,et al.  Evolutionary Optimization-based Mission Planning for UAS Traffic Management (UTM) , 2019, 2019 International Conference on Unmanned Aircraft Systems (ICUAS).

[7]  Arnab Majumdar,et al.  Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty , 2017 .

[8]  Kin Huat Low,et al.  Three-dimensional (3D) Monte-Carlo modeling for UAS collision risk management in restricted airport airspace , 2020 .

[9]  Anand Singh Jalal,et al.  A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases , 2010 .

[10]  Lishuai Li,et al.  Characterizing air traffic networks via large-scale aircraft tracking data: A comparison between China and the US networks , 2018 .

[11]  Christine M. Belcastro,et al.  Preliminary Risk Assessment for Small Unmanned Aircraft Systems , 2017 .

[12]  Kin Huat Low,et al.  Collision risk management for non-cooperative UAS traffic in airport-restricted airspace with alert zones based on probabilistic conflict map , 2019 .

[13]  Daniel Delahaye,et al.  A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area , 2018, Transportation Research Part C: Emerging Technologies.