Rail-AI-Leader: Technical Report

Tobaras, Christopher (2024) Rail-AI-Leader: Technical Report. Undergraduate thesis, Dublin, National College of Ireland.

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Abstract

The aim of this technical report is to document and the requirements and development of a signaling tool that can be used enhance safety, optimize the flow of traffic and further research the idea of automating railway traffic. The report explores the technology used throughout the development process, which includes traversal algorithms, GUI interfaces, and scheduling of threads. The architecture of the program is also discussed, which consists of a Model-View-Controller style interface, displays a track layout which follows a transactional rules-based approach for the generation, and movement of trains.

The report describes the functional and other requirements of the application, following with the implementation of the key features which meet these requirements. The graphical interface, pathfinding algorithm and transactional model will be described in detail, utilizing screenshots and snippets of code to demonstrate the functionality. The report discusses the challenges encountered and resolved throughout the duration of development and demonstrates the usage of technologies such as GitHub, JavaFX, and IntelliJ to meet the requirements set forth for the program.

After the development section, the app is then tested and evaluated to see if the final product meets the requirements. The report afterwards will discuss features that could be considered if further development was possible. The report concludes that the application could present a viable need for automating railway traffic, however, needs further time to implement more features that would improve the performance, flexibility, and impact of the application.

Item Type:	Thesis (Undergraduate)
Supervisors:	Name Email Clifford, William UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TF Railroad engineering and operation Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Bachelor of Science (Honours) in Computing
Depositing User:	Ciara O'Brien
Date Deposited:	27 May 2025 14:12
Last Modified:	27 May 2025 14:12
URI:	https://norma.ncirl.ie/id/eprint/7676

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