Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Abstract

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs' trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

Research on the Development of Modern Design Through Data Mining Technology

“…With applying mathematical models like kernel density estimation, the algorithm calculates the intensity of movement at various points in the logistics network, highlighting areas with the highest traffic or activities. Density-based clustering further groups together similar movement patterns, identifying frequently used paths and common transportation routes [13]. One significant advantage of this approach is its ability to reveal hidden patterns and trends in logistics data.…”