Aude Marzuoli

Data visualization techniques for airspace flow modeling

With the predicted growth of air traffic, traffic flow managers need new tools to access information to support their decision making processes. Recent progress with information visualization tools enables users to explore large data sets and extract decisive knowledge. Their advantages for air traffic applications are presented in this paper. They can provide high level information to aggregate trajectories. With constant feedback due to human perception, a flow model of the airspace, reflecting its intrinsic structure, is elaborated and can be used for further research.

Data-Based Modeling and Optimization of En Route Traffic

Air traffic management aims at ensuring safe and efficient movement of aircraft in the airspace. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision-making is essential. These tools should aid in accommodating the air traffic throughput increase while limiting controller workload and ensuring high safety levels. The objective of this paper is to present a methodology to model and simulate traffic in a given portion of the airspace from data under nominal and perturbed conditions. A new framework for en route traffic flow management and airspace health monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale three-dimensional flow network provides valuable insight on airspace complexity. A linear programming formulation for optimizing en route air traffic is proposed. It takes into account a controller task load model based on flow geometry, in order to estimate airspa...

Air traffic optimization on data-driven network flow model

This paper presents a new framework for Traffic Flow Management and Airspace Health Monitoring based on data-driven approach for air traffic flows modeling using historical data. The large-scale 3-dimensional flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation of the Traffic Flow Management Problem is proposed, taking into account estimations of controller workload based on flow geometry. Preliminary results for the problem are discussed, pointing out clues for further research.

Optimal navigation policy for an autonomous agent operating in adversarial environments

We consider an autonomous vehicle navigation problem, whereby a traveler aims at traversing an environment in which an adversary tries to set an ambush. Optimal strategies are computed as random path distributions, a realization of which is the path chosen by the traveler. Theoretical optimal policies are derived under assumptions from the Minimal Cut-Maximal Flow literature. Numerical approaches to compute such optimal strategies are proposed. These numerical approaches, which borrow from randomized path planning techniques, can be implemented for high-dimensional configuration spaces. The methodology developed allows for the application of ambush games on complex environments for realistic applications regarding vehicle routing in adversarial settings.

Multimodality in a Metroplex Environment: A case study in the San Francisco Bay Area

The present paper focuses on the crisis management following the Asiana Crash at San Francisco Internation Airport in July 2013. The crash led to a large number of domestic and international flight diversions to many airports, such as Oakland, San Jose, Los Angeles, but also Denver, Seattle, Calgary, for instance. Thousands of passengers found themselves struggling to reach their original destination. Passenger reaccommodation varied greatly from airline to airline and airport to airport. The contributions of this paper are twofold. First a passenger-centric reaccommodation scheme is developed to balance costs and delays, for each diversion airport. Second, assuming better information sharing and collaborative decision making, we show that there was enough capacity at the neighboring airports, Oakland and San Jose, to accommodate most of the diverted flights and reoptimize the allocation of flight diversions to the Bay Area airports.

Large-scale data-based collaborative air traffic optimization for congestion management

In a context of air transportation growth, it becomes essential to better manage the rising congestion levels. The present paper presents a large-scale destination-aggregated multicommodity flow model at the National Airspace system level, supported by a data-based network synthesis. It encomprises a departure queuing model to optimize the routing and delays of flights in the NAS. The flows are aggregated according to their destination to ensure a more compact linear optimization formulation without losing accuracy in the analysis. This model determines the nationwide impact of local constraints from historical data. It could serve as the basis to understand the propagation of congestion in the NAS, mitigate its effects by linking optimization results to operational constraints and actions, and support nationwide collaborative management of the airspace resources.

Resilience of the national airspace system: A case study of the fire at the Chicago ARTCC

As air traffic grows, improving the resilience of an increasingly congested airspace is becoming a primary concern to ensure its sustainability and avoid the propagation of localized disruptions. The fire set at Chicago Air Traffic Control facility in September 2014 is a striking and worrisome case where one ill-intentioned individual halted traffic in a facility which caused ripple effects across the National Airspace. This paper analyzes the particular role that the Chicago center plays in the National airspace. Then it provides a detailed analysis of the local and global impact of the fire, through the day of the event and for the next two weeks while the facility remained closed for repairs. Finally, it draws the lessons learnt from this event as well as other recent infrastructure failures.

Resilience of the national airspace system structure: A data-driven network approach

In the context of air transportation growth, it has become essential to better manage the rising congestion levels and their potential ripple effects throughout the entire airspace. The present paper aims at examining the resilience of the National Airspace System. Through a data-based network model, the main choke points of the system are identified and their importance is quantified for better monitoring of congestion growth and propagation. The study relies on data-mining and network science techniques to analyze sector-level traffic patterns. The dynamic aspects of time and traffic load are examined and the notion of core subnetwork is discussed. Finally, the robustness of the network is studied under different attack strategies, highlighting potential vulnerabilities.

Analysis of airspace degradation and optimization of en-route traffic under degraded conditions

Weather is known to play an important role in the management of air traffic. Using an existing traffic flow optimization environment, the impacts of different weather perturbations on the airspace are analyzed. Simulations to optimize traffic flows using estimates of controller taskload in the degraded environment are run and their results are compared with ETMS data. They highlight the discrepancies between nominal and perturbed conditions, and point out further promising research topics.