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

TL;DR

This paper introduces a congestion-aware path re-routing strategy for urban UAS traffic management that enhances safety and efficiency by dynamically avoiding congested airspace using rule-based local path adjustments and queuing models.

Contribution

It proposes a novel rule-based re-routing strategy utilizing nominal path and positional data, validated through queuing models and simulations for congested urban airspace.

Findings

The strategy reduces congestion on nominal paths by spreading traffic to alternative routes.

Queuing models effectively predict traffic spread under varying demand.

Simulations confirm improved safety and airspace utilization in congested scenarios.

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.