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Application of Graph Theory on Dublin Airport Management

Singh Doliya, Laxman (2022) Application of Graph Theory on Dublin Airport Management. Masters thesis, Dublin, National College of Ireland.

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The airport management face operational challenges with the growth in the number of passengers, limited infrastructure, congestion in terminals, and increasing prices. Although significant progress has been made in the field of passenger analytics such as identifying crowds in the airport and predicting the inflow of passengers, but real-time management congestion in airports remains an ongoing problem. The deep learning methodologies combined with graph theory applications can be applied to tackle the problem of managing crowds in the airport. The cognitive abilities of deep learning methodologies identify the formation of crowds and graph theory is used to provide a solution to manage the crowds by converting the airport space into a two-dimensional graph. In this research paper, the research proposes a hybrid solution to manage the crowds using the CSRNet algorithm to identify the density of the crowds and then using the A* path-finding algorithm to provide the optimal path for the passengers to reach from source to destination. A simulation model verifies the effectiveness of the proposed solution. The proposed solution creates an intelligent solution that provides a dynamic path based on the density of the crowd within a given region. This research will enable the authorities of Dublin airport to better manage the crowd and thereby provide outstanding services to the passengers using the airport facilities.

Item Type: Thesis (Masters)
Basilio, Jorge
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Aviation Industry
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 May 2023 12:34
Last Modified: 26 May 2023 12:34

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