Unmanned aerial vehicles or drones are widely used or proposed to carry out various tasks in low-altitude airspace. To safely integrate drone traffic into congested airspace, the current concept of operations for drone traffic management will reserve a static traffic volume for the whole planned trajectory, which is safe but inefficient. In this paper, we propose a dynamic traffic volume reservation method for the drone traffic management system based on a multiscale A* algorithm. The planning airspace is represented as a multiresolution grid world, where the resolution will be coarse for the area on the far side. Therefore, each drone only needs to reserve a temporary traffic volume along the finest flight path in its local area, which helps release the airspace back to others. Moreover, the multiscale A* can run nearly in real-time due to a much smaller search space, which enables dynamically rolling planning to consider updated information. To handle the infeasible corner cases of the multiscale algorithm, a hybrid strategy is further developed, which can maintain a similar optimal level to the classic A* algorithm while still running nearly in real-time. The presented numerical results support the advantages of the proposed approach.
more »
« less
A Graph-Analytic Approach to Dynamic Airspace Configuration
The current National Airspace System (NAS) is
reaching capacity due to increased air traffic, and is based
on outdated pre-tactical planning. This study proposes a more
dynamic airspace configuration (DAC) approach that could
increase throughput and accommodate fluctuating traffic, ideal
for emergencies. The proposed approach constructs the airspace
as a constraints-embedded graph, compresses its dimensions, and
applies a spectral clustering-enabled adaptive algorithm to generate
collaborative airport groups and evenly distribute workloads
among them. Under various traffic conditions, our experiments
demonstrate a 50% reduction in workload imbalances. This
research could ultimately form the basis for a recommendation
system for optimized airspace configuration. Code available at
https://github.com/KeFenge2022/GraphDAC.git
more »
« less
- Award ID(s):
- 2142514
- PAR ID:
- 10467134
- Editor(s):
- Cristina Ceballos
- Publisher / Repository:
- IEEE Computer Society Conference Publishing Services (CPS) http://www.computer.org/cps
- Date Published:
- ISSN:
- 2835-5776
- ISBN:
- 979-8-3503-3458-6
- Page Range / eLocation ID:
- 235 - 241
- Subject(s) / Keyword(s):
- Dynamic Airspace Configuration, Graph Analytic, Singular Value Decomposition, Antoencoder
- Format(s):
- Medium: X
- Location:
- Seattle, Washington
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Computer simulations are widely used to design and evaluate air traffic systems. A fast time simulation capability is essential to effectively explore the consequences of decisions in airspace design, air traffic management, and operations. A parallel simulation approach is proposed to accelerate fast time simulation of air traffic networks that exploits both temporal and spatial parallelisms. A time-parallel algorithm is first described that simulates different time intervals concurrently and uses a fix up computation that exploits the scheduled nature of commercial air traffic to address the problem of dependencies between time segments. The time-parallel algorithm is then extended with a space-parallel simulation approach using Time Warp to simulate each time segment in parallel thereby increasing the amount of parallelism that can be exploited. The time and space-parallel algorithms are evaluated using a simulation of the U.S. National Airspace System (NAS). Experimental data is presented demonstrating that this approach can achieve greater acceleration than what can be achieved by exploiting time-parallel or space-parallel simulation techniques alone.
-
Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos’ workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller’s control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at Link.more » « less
-
Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM.more » « less
-
The present research examines a pattern-based measure of communications based on Closed Loop Communications (CLC) and non-content verbal metrics to predict Loss of Separation (LOS) in the National Airspace System (NAS). This study analyzes the transcripts from six retired Air Traffic Controllers (ATC) who participated in three simulated trials of various workloads in a TRACON arrival radar simulation. Results indicated a statistically significant model for predicting LOS based on CLC deviations (CLCD), word count in transmission, words per second, and traffic density. However, more research is required to evaluate the significance of each communication variable to predict LOS.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Proceeding:
- https://doi.org/10.1109/IRI58017.2023.00048
