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The rapid deployment of fleets of small, uncrewed aircraft (drones) in the immediate aftermath of a natural disaster to search impacted regions for people in need of rescue is one of the most vital applications of advanced air mobility. Effective drone-based search operations require that the drone fleets operate out of bases that are appropriately located in advance of the disaster. Using a case study based in the Iwate prefecture of Japan, we develop optimization formulations to strategically locate drone bases. It is important to be capable of responding quickly to the locations most likely to require search, while covering as large an area as possible. We evaluate the disparities in the level of access afforded to different areas. Finally, we extend our optimization formulation to account for the probability of the base locations themselves being impacted by the disaster, and the possibility of base relocation. 

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.