Team 1

Abstract

In the airline industry of the US, there exists a fundamental disparity between the desires of the consumer and the services provided by airlines. Consumers are generally interested in gaining time-sensitive access to a breadth of travel locations and paying low fares, while airlines are concerned with maximizing profit. In its current state, major sectors of the U.S. population are not adequately serviced by the airline industry. Additionally, these problems lie against the backdrop of a warming climate, with the global airline industry contributing around two percent of greenhouse gas emissions. The intersection of network science, optimization, and machine learning provides a powerful framework for addressing these issues. With publicly-available airline transportation data and access to high-performance computation tools, we can create a model to account for accessibility, economic, and environmental factors. In the design of such a model, our senior design team will gain greater insight into the current priorities of the industry and propose alternative solutions to rewrite them.

Built With

• python