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Publicação
An LLM-Based Conversational Agent for Multi-Operator Public Transport Information
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| 3.51 MB | Adobe PDF |
Autores
Resumo(s)
Regional public transport information landscapes play a key role in shaping passengers’ ability to plan and undertake journeys, yet they remain difficult to navigate. In many regions, passenger information is fragmented across multiple websites and applications, creating barriers to adoption and limiting the modal shift required to achieve CO2 emission targets. Although a growing body of evidence suggests an increasing interest in conversational systems for public transport, existing solutions have largely focused on narrow segments of the passenger journey. This study proposes a framework for developing modular passenger-facing agentic conversational systems, combining a retrieval-augmented generation (RAG) component for textual information with a ReAct-inspired agentic routing component for transit network information. The framework includes a reproducible methodology for tuning and evaluating both components and is validated through a real-world pilot deployment in the Oeste region of Portugal, where three public transport operators serve 388,000 residents across 203 bus routes. Results showed reliable component-level performance on unseen evaluation datasets. User-based evaluation further indicated fitness for use, with participants reporting high overall satisfaction. The findings thus establish a baseline for agentic conversational systems in multi-operator public transport settings, and the framework’s reliance on standardized data formats supports transferability to other regions.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Large Language Models AI Agents Conversational AI Multi-Agent System Public Transport Routing