TransitTalk: Large language model-based digital assistants for enhancing transit customer experience and staff performance

Document Type

Journal Article

Publication Date

2026

Subject Area

technology - intelligent transport systems, planning - signage/information

Keywords

customer service, intelligent transit system, large language modelc, public transit system

Abstract

Large-language models (LLMs) have transformed natural-language understanding, generation, and reasoning, yet their potential within public-transit operations remains largely untapped. This study introduces TransitTalk, a retrieval-augmented framework that positions an LLM as an intelligent intermediary between natural-language requests and the heterogeneous databases that underpin modern transit networks. The framework first interprets rider or staff intent, then issues structured queries to relevant data sources, and finally produces context-aware conversational responses. Chain-of-thought prompting and vector-database retrieval enable the LLM to operate simultaneously as a dialogue agent, domain expert, and bespoke formatter, thereby bridging the gap between technical data schemas and user-friendly information delivery. The paper demonstrates the practicality of the framework through three fully implemented prototypes. Tweet Writer automatically drafts clear, multi-platform service-alert messages; Trip Advisor generates personalized, constraint-aware itineraries; and Policy Navigator delivers concise explanations of fares, accessibility provisions, and service regulations. Collectively, these applications illustrate how an LLM layer can streamline communications, reduce staff workload, and enhance the passenger information experience without disrupting existing data pipelines. All source code are openly available.

Rights

Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.

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