arXiv subjects

Daniel DeLaurentis

Publications and source records attributed to Daniel DeLaurentis.

4 recordsLinked to original sources

arXiv2022

Fleet-Level Environmental Assessments for Feasibility of Aviation Emission Reduction Goals

The International Air Transport Association (IATA) is one of several organizations that have presented goals for future CO2 emissions from commercial aviation with the intent of alleviating the associated environmental impacts. These goals include attaining carbon-neutral growth in the year 2020 and total aviation CO2 emissions in 2050 equal to 50% of 2005 aviation CO2 emissions. This paper presents the use of a simulation-based approach to predict future CO2 emissions from commercial aviation based upon a set of scenarios developed as part of the Aircraft Technology Modeling and Assessment project within ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment. Results indicate that, in future scenarios with increasing demand for air travel, it is difficult to reduce CO2 emissions in 2050 to levels equal to or below 2005 levels, although neutral CO2 growth after 2020 may be possible.

physics.soc-ph↗

arXiv2022

Relational Dynamic Bayesian Network Modeling for Uncertainty Quantification and Propagation in Airline Disruption Management

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data sets. We employ the UTFM to assess two independent and separately disrupted flight legs from the airline route network. Assessment of a flight leg from Dallas to Houston, disrupted by air traffic control hold for bad weather at Dallas, revealed that proactive disruption management for turnaround in Dallas before schedule execution is impractical because of zero transition probability between turnaround and taxi-out.

cs.AI↗

arXiv2021

Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the airline operations control center. To that effect, we provide a discussion and demonstration of an agnostic and systematic paradigm for enabling expeditious simultaneously-integrated recovery of all problem dimensions during airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology. Results indicate that our paradigm for simultaneously-integrated recovery executes in polynomial time and is effective when all the flights in the airline route network are disrupted.

cs.AI↗

arXiv2021

Exploratory Data Analysis for Airline Disruption Management

Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions. To that effect, this paper applies macroscopic and microscopic techniques by way of basic statistics and machine learning, respectively, to analyze historical scheduling and operations data from a major airline in the United States. Macroscopic results reveal that majority of irregular operations in airline schedule that occurred over a one-year period stemmed from disruptions due to flight delays, while microscopic results validate different modeling assumptions about key drivers for airline disruption management like turnaround as a Gaussian process.

stat.AP↗