Alpha-Fair Routing in Urban Air Mobility with Risk-Aware Constraints

TL;DR

This paper presents a novel routing algorithm for urban air mobility that ensures fair demand distribution among communities while managing stochastic capacity constraints using risk measures.

Contribution

The paper introduces a convex optimization-based routing algorithm that enforces alpha-fairness and risk-aware capacity constraints in urban air mobility networks.

Findings

The algorithm guarantees fair demand distribution among communities.

It effectively manages stochastic capacity constraints with risk measures.

Case study demonstrates improved fairness over demand-maximizing methods.

Abstract

In the vision of urban air mobility, air transport systems serve the demands of urban communities by routing flight traffic in networks formed by vertiports and flight corridors. We develop a routing algorithm to ensure that the air traffic flow fairly serves the demand of multiple communities subject to stochastic network capacity constraints. This algorithm guarantees that the flight traffic volume allocated to different communities satisfies the \emph{alpha-fairness conditions}, a commonly used family of fairness conditions in resource allocation. It further ensures robust satisfaction of stochastic network capacity constraints by bounding the coherent risk measures of capacity violation. We prove that implementing the proposed algorithm is equivalent to solving a convex optimization problem. We demonstrate the proposed algorithm using a case study based on the city of Austin. Compared with one that maximizes the total served demands, the proposed algorithm promotes even distributions of served demands for different communities.