Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions

This paper introduces the Text-to-TrajVis task, which aims to transform natural language questions into trajectory data visualizations, facilitating the development of natural language interfaces for trajectory visualization systems.As this is a novel task, there is currently no relevant dataset available in the community.To address this gap, we first devised a new visualization language called Trajectory Visualization Language (TVL) to facilitate querying trajectory data and generating visualizations.Building on this foundation, we further proposed a dataset construction method that integrates Large Language Models (LLMs) with human efforts to create high-quality data.Specifically, we devised a four-stage pipeline that begins with candidate extraction, proceeds through seed TVL generation and tree-based expansion, and concludes with LLM-driven question creation followed by human validation.This process results in the creation of the first large-scale Text-to-TrajVis dataset, named Tra-jVL 1 , which contains 9,608 (question, TVL) pairs.We propose a framework called TRCAT for progressively converting natural language questions into TVLs.The framework incorporates TVL-RAG Chain Module and Area-Time Standardization Module, significantly enhancing the accuracy of LLMs in TVL generation.Based on the TrajVL dataset, we conduct a comprehensive evaluation of TRCAT's performance across several mainstream LLMs (e.g., GPT, Qwen, LLaMA, and Gemma).Furthermore, we established a benchmarking system for this task, providing a foundation for future research in structured trajectory language generation.