Congestion-Aware Path Re-routing Strategy for Dense Urban Airspace

TL;DR

This work tackles congestion in dense urban UAS traffic by introducing a distributed, rule-based re-routing strategy that generates local parallel paths around preplanned nominal routes. A zone-based grid and a heading-reference graph drive myopic, congestion-aware decisions, enforced by priority rules, descend logic, and conflict-resolution maneuvers, with special handling for static obstacles and exogenous traffic streams. To evaluate performance, the authors develop discrete-time queuing models—MMRP and MMBP—for Stream{0} and additional streams, enabling analytical estimation of traffic spread and congestion probabilities that guide airspace reservation. Simulations demonstrate that, under varying arrival rates, weather, and obstacle configurations, the traffic self-organizes into parallel corridors with bounded occupancy ($M$ per zone) and a controllable spread modulated by $M$ and $\eta$, validating the theoretical models. The findings indicate that the approach can scale to growing UAS demand while maintaining safety, through offline UTM parameterization and decentralized in-flight execution, with potential extensions to more complex exogenous traffic scenarios and probabilistic congestion definitions.

Abstract

Existing UAS Traffic Management (UTM) frameworks designate preplanned flight paths to uncrewed aircraft systems (UAS), enabling the UAS to deliver payloads. However, with increasing delivery demand between the source-destination pairs in the urban airspace, UAS will likely experience considerable congestion on the nominal paths. We propose a rule-based congestion mitigation strategy that improves UAS safety and airspace utilization in congested traffic streams. The strategy relies on nominal path information from the UTM and positional information of other UAS in the vicinity. Following the strategy, UAS opts for alternative local paths in the unoccupied airspace surrounding the nominal path and avoids congested regions. The strategy results in UAS traffic exploring and spreading to alternative adjacent routes on encountering congestion. The paper presents queuing models to estimate the expected traffic spread for varying stochastic delivery demand at the source, thus helping to reserve the airspace around the nominal path beforehand to accommodate any foreseen congestion. Simulations are presented to validate the queuing results in the presence of static obstacles and intersecting UAS streams.

Figures (26)

• Figure 1: a) Cell tessellation of workspace containing the UAS dispatch center (source) and the customer location (destination). b) Several feasible paths may exist between source and destination ($\mathsf{S}$-$\mathsf{1}$-$\mathsf{2}$-$\mathsf{4}$-$\mathsf{D}$, $\mathsf{S}$-$\mathsf{1}$-$\mathsf{3}$-$\mathsf{4}$-$\mathsf{D}$, $\mathsf{S}$-$\mathsf{5}$-$\mathsf{4}$-$\mathsf{D}$). Here, the path shown by the bold line is the nominal path given by the UTM. In the event of congestion on this path, the figure illustrates the local parallel paths that are generated. The expected traffic spread or the expected path spread is the average number of parallel paths on which the UAS traffic would be diverted depending on the request rate $\Lambda$.
• Figure 2: a) Node and zone definition. b) Connected grid and stream-level indexing of zones about each nominal segment.
• Figure 3: In the figure, with respect to the UAS $i \in \{1,2,3,4\}$, the set $\{\ j'\ \vert\ j \in \{1,2,3,4\} \backslash \{i\}\ \}$ are the $L$ slot look-ahead predicted positions of other UAS. When $M = 2$, from the perspective of UAS 2 present in zone $Z$, zone $Z'$ will not be congested in the next $L$ timeslots as only one predicted position (that of UAS 1) exists in $Z'$. From the perspective of UAS 3 present in zone $Z$, the zone $Z'$ will be congested as two predicted positions (UAS 1 and 2) lie in zone $Z'$.
• Figure 4: Zone neighborhood of three arbitrary zones $Z = (X,Y)$ in the grid, whose $X<0$, $X=0$, and $X>0$, respectively. The congestion occurrences in respective upstream zones $Z_{\mathrm{UP}}$ and $Z_{\mathrm{ID}}$ influence the decisions of UAS present in $Z$.
• Figure 5: a) Heading reference directed graph for UAS present in zone $Z$ in an arbitrary level $Y$. b) The $L = 5$ slot look-ahead predicted positions of UAS, which are restricted on the heading reference graph. If $M = 2$, then by Definition \ref{['def: 2']}, in the perspective of UAS present at node $\widetilde{Z}$, both zones $Z_{\mathrm{UP}}$ and $Z_{\mathrm{ID}}$ are congested (due to UAS 3, 4, 5, and 7). UAS present on $\beta\rightarrow\gamma$ path (UAS 1 and 2) do not contribute to congestion in $Z_{\mathrm{UP}}$ and $Z_{\mathrm{ID}}$.

Theorems & Definitions (5)

• Definition 1
• Definition 2
• Definition 3
• Theorem 1
• proof