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Khalid, Muhammad Adnan (2025) Real-Time Public Transport by WebSocket: A Hybrid Architecture for Efficient Data Delivery. Masters thesis, Dublin, National College of Ireland.
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Abstract
The research topic of this dissertation is the design, implementation, and testing of a hybrid architecture of real-time delivery of public transport data consisting of periodic API calls, Redis caching, and WebSocket notifications to balance responsiveness and the load on the public API. With the example of the GTFS data of the Irish National Transport Authority, the system includes predictive analytics based on AI to add additional scheduling forecasts of delays and personalised recommendations to the journey planning. The architecture polls APIs in the backend, caches results in an in-memory cache and streams changes to clients in a WebSocket connection, to avoid the non-scalability of the client-side polling implementation. Use of a React/Leaflet.js front end offers an intuitive user interface that is responsive, with live vehicle status and surrounding route details. Quantitative benchmarking, usability analysis, and comparisons proved that API calls were reduced to 99.93%, average latency to 320 ms, the hit rate in the cache amounted to 93%, and the ability to scale to more than 10,000 concurrent users. The delay forecasting accuracy of the predictive model was 87%. It is established that hybrid architectures provide significant advantages in the areas of scalability, efficiency, and user experience real-time transport systems. This study offers new knowledge to scholarly discourse on sustainable API utilization, and it offers a scheme of transporting authorities, smart city operators, and mobility services suppliers that are commercially realistic.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Nolan, Eamon UNSPECIFIED |
| Subjects: | 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 H Social Sciences > HE Transportation and Communications > Urban Transportation |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 01 Jul 2026 11:10 |
| Last Modified: | 01 Jul 2026 11:10 |
| URI: | https://norma.ncirl.ie/id/eprint/9431 |
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