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Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infrastructure as a collection of contiguous regions (or sectors) with constraints on the number of vehicles that simultaneously enter, stay, or exit each region. Vehicles request access to airspace with trajectories spanning multiple regions at different times. We use the graph structure of our airspace model to formulate the allocation problem as a path allocation problem on a time-extended graph. To ensure that the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of “air-credits” (an artificial currency) and anonymously charges prices for traversing the edges of the time-extended graph. We seek to compute a competitive equilibrium that ensures that: (i) capacity constraints are satisfied, (ii) a strictly positive resource price implies that the sector capacity is fully utilized, and (iii) the allocation is integral and optimal for each AAM vehicle given current prices, without requiring access to individual vehicle utilities. However, a competitive equilibrium with integral allocations may not always exist. We provide sufficient conditions for the existence and computation of a fractional-competitive equilibrium, where allocations can be fractional. Building on these theoretical insights, we propose a distributed, iterative, two-step algorithm that: (1) computes a fractional competitive equilibrium, and (2) derives an integral allocation from this equilibrium. We validate the effectiveness of our approach in allocating trajectories for the emerging urban air mobility service of drone delivery. 

Congestion pricing has been long-believed to effectively regulate traffic in urban city centers. Practical implementations of such policies have been hindered by concerns that they would disproportionately and adversely affect low-income groups. This paper analyzes the impacts of two price increases to the congestion charge on different income groups in Central London, making use of the synthetic control method to leverage empirical data from the UK National Travel Survey. We estimate that the highest income earners contributed to more of the revenue than the lowest income earners, making the scheme progressive in the scale of its equity impact. Although high-income travelers appeared to drop more charge-eligible trips as the price increased, their total trips to Central London did not decrease, suggesting that they were able to substitute with non-chargeable modes of travel. Low-income travelers saw large declines across both chargeable and non-chargeable modes, revealing a much lower rate of substitution. The low-income group responded more to the 2011 price increase than the 2014 one, demonstrating the diminishing ability of subsequent price increases to regulate demand. 

The air transportation system connects the world through the transport of goods and people. However, operational inefficiencies such as flight delays and cancellations are prevalent, resulting in economic and environmental impacts. In the first part of this article, we review recent advances in using network analysis techniques to model the interdependencies observed in the air transportation system and to understand the role of airports in connecting populations, serving air traffic demand, and spreading delays. In the second part, we present some of our recent work on using operational data to build dynamical system models of air traffic delay networks. We show that Markov jump linear system models capture many of the salient characteristics of these networked systems. We illustrate how these models can be validated and then used to analyze system properties such as stability and to design optimal control strategies that limit the propagation of disruptions in air traffic networks. 

Understanding the characteristics of air-traffic delays and disruptions is critical for developing ways to mitigate their significant economic and environmental impacts. Conventional delay-performance metrics reflect only the magnitude of incurred flight delays at airports; in this work, we show that it is also important to characterize the spatial distribution of delays across a network of airports. We analyze graph-supported signals, leveraging techniques from spectral theory and graph-signal processing to compute analytical and simulation-driven bounds for identifying outliers in spatial distribution. We then apply these methods to the case of airport-delay networks and demonstrate the applicability of our methods by analyzing U.S. airport delays from 2008 through 2017. We also perform an airline-specific analysis, deriving insights into the delay dynamics of individual airline subnetworks. Through our analysis, we highlight key differences in delay dynamics between different types of disruptions, ranging from nor’easters and hurricanes to airport outages. We also examine delay interactions between airline subnetworks and the system-wide network and compile an inventory of outlier days that could guide future aviation operations and research. In doing so, we demonstrate how our approach can provide operational insights in an air-transportation setting. Our analysis provides a complementary metric to conventional aviation-delay benchmarks and aids airlines, traffic-flow managers, and transportation-system planners in quantifying off-nominal system performance.