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Customer Satisfaction in Information Provision in Bus and Coach terminals

The actuality of this research lies in the significance of changing the mobility traditions toward public transport usage. It requires considering various aspects of the customer experience in public transport services. Transport terminals are an asset to an area as they may act as catalysts for more active use of public transport. However, poorly planned, and sited terminals may generate problems and passengers reduction.The research aims to gain insights into the effectiveness of information visualization in bus and coach terminals and contribute to enhancing the overall passenger experience during journeys. The author is reviewing existing literature on information visualization in transport terminals and studies, or best practices related to improving passenger experiences through effective information design.The author used both quantitative and qualitative methods as sources of data, such as user surveys, expert interviews, and observations for understanding how passengers interact with existing information displays. From analysis, the Riga International Coach terminal has a good information system but misses some visualization tools for information provision. Thus, recommendations were made on how to improve information provision to enhance customer satisfaction.

Author: Diane Aliou Yasmine

Supervisor: Irina Jackiva

Degree: Master

Year: 2024

Work Language: English

Unsupervised machine learning approach for hierarchical graph-based representation of natural language text collections.

Managing big data efficiently is important in various fields, much so when data consists of human-written documents. Recent advances in Natural Language Processing (NLP), particularly LLMs, allowed to solve many task in this domain, despite the high demand for labelled data, compute resources and specialized skills.To tackle these limitations, current study proposed a NLP pipeline to identify topic hierarchies in collections of scientific publications. The work focused on evaluation of available unsupervised machine learning methods and quality metrics in NLP, and development of visualization techniques to build a prototype of the pipeline.Proposed solution is based on the hARTM approach optimized for interpretability. It demonstrated the capacity to infer human-interpretable topic hierarchies from collections of scientific texts and construct meaningful hierarchy of topic-based document representations. The visualization approaches rely on MDS to present inter-document similarity and Sankey plots to show document cluster relatedness within topic hierarchy.Utility was demonstrated on two datasets, focusing on interpretability and meaning of the topic hierarchy and associated topic definitions. Potential application areas include personal education and scientific writing.

Author: Jevgenijs Bodrenko

Supervisor: Irina Jackiva

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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