Cognitive work analysis in the conceptual design of first-of-a-kind systems – designing urban air traffic management

Figures & data

Table 1. Initial work domain analysis.

Level	A to B	Inside area
S	Drone needs to fly from A to B	Drone(s) needs to operate in specific area
FP	Some known overarching objectives, e.g. safety, efficiency
AF	Generic efficiency metrics, e.g. regarding path lengths, safety metrics (e.g. infringements)	Generic volume capacity metrics
F	Monitor current drone position, versus other airspace objects	Monitor current drone position, versus 3d boundary (airspace constraints) locations
PF	E.g. LIDAR detection capabilities	E.g. GPS accuracy (limitation)
O	e.g. drones, buildings, ‘inside area’ also has a ‘virtual boundary’ object

Figure 1. (a) the collection of ideas, (b) a service concept example.

Figure 2. Daily traffic pattern variations.

Figure 3: UTM building blocks; (a) volume, (b) layer, (c) tube, (d) point. Background generated from GSD-Ortofoto25 and GSD-Höjddata, grid 2+ ©Lantmäteriet.

Figure 4. (a) low-density traffic, and (b) high-density traffic. Background generated from GSD-Ortofoto25 and GSD-Höjddata, grid 2+ ©Lantmäteriet.

Figure 5. Concept 2, exclusion zone for ATM integration. (a) top: geofenced area, (b) bottom: depth profile.

Figure 6. Airport geofence. Geofenced area in light transparent colour. Background generated from GSD-Ortofoto25 and GSD-Höjddata, grid 2+ © Lantmäteriet.

Figure 7. (a) Concept 3 and 4, (b) concept 5

Figure 8. Layered traffic. Background generated from GSD-Ortofoto25 and GSD-Höjddata, grid 2+ © Lantmäteriet.

Figure 9. Random point-to-point traffic pattern. Background generated from GSD-Ortofoto25 and GSD-Höjddata, grid 2+ © Lantmäteriet.

Figure 10. Fan-out (delivery from warehouse) traffic pattern (two warehouses). Background generated from GSD-Ortofoto25 and GSD-Höjddata, grid 2+ © Lantmäteriet.

Table 2. Workshop 1–2 WDA.

Level	Traffic management
S	WS1 Drone traffic in cities: commercial or recurring operations; peer-initiated operations; high priority irregular operations WS2 Contingency management
FP	WS2 Safety
AF	WS1 Service-dependent priority and value measures
F	WS1 Changing airspace design to match traffic variations; WS2 monitor conditions versus drone performance limits;
PF	WS1: overarching traffic patterns (variations over the day, varying priorities, varying planning horizons) WS2: landing sites (houses or roads), drone reliability within performance limits, drones with various performance

Table 3. Building blocks WDA.

Level	Volumes	Layers	Line	Point
F	Encapsulate; monitor (borders and capacity)	Encapsulate; monitor (borders and capacity, vertical passages)	Encapsulate trajectory; separation of trajectories, line segments,	Encapsulate (potentially moving) points; monitoring and separating points
PF	Perimeter constraints of virtual 3D volume	Altitude constraints of virtual layer	Virtual intersections, segments	(constraints regarding) movement of points
O	3D shape, location, size	layer, stack of layers	Virtual object (line or tube), network, location, size	shape of encapsulation volume

Table 4. Concepts WDA.

Level	C1	C2	C3	C4	C5
S	Low traffic intensity	Different traffic types, congested geofence corners	High traffic intensity	Different kinds of traffic that should not be mixed; manage traffic to reduce congestion.	drones with poor detect-and-avoid capacity; need to manage privacy/noise; crossing traffic in (fan pattern)
FP	Manage airspace with five (2-5) drones	Manage shared airspace with airports, efficiently and safely	Capacity monitoring, safety	Separate of different kinds of traffic; manage congestion	Privacy, noise
AF			Capacity estimation	Airspace efficiency estimation
F	Point-based supervisory control (monitoring)	Volume-based supervisory control	Grid-capacity-based supervisory control	supervisory control based on airspace efficiency indicator and layering	Tube separation-based supervisory control; hub-to-hub traffic pattern, fan traffic pattern;
PF	Human supervisory function, limited to about five drones	congested corners and long trajectory lines when using large geofence	(extensive) use of detect-and-avoid in the air traffic, with a too high traffic density	detect-and-avoid in the simulated traffic	traffic interference with crossing traffic in fan-out pattern and start/landing sites
O	Points representing drones, as interface objects	Points representing drones, lines representing trajectories, 3d objects representing geofences, as interface objects	Heat map as interface object	Different colours, 3D as interface objects representing traffic at different altitudes	Lines representing tubes, as interface objects