RT2text: Making Trajectory Summarization More Accessible through Text Generation

Trajectory summarization is vital for mobility analysis, extracting key movement patterns from large trajectory datasets. RT2text is a rule-based method that converts summarized trajectory into readable text. It uses regular expressions for data extraction, rule-based analysis, and Jinja2 templating for dynamic text generation, ensuring coherent descriptions of movement patterns. By incorporating Natural Language Generation (NLG), RT2text facilitates intuitive insights into mobility trends. We demonstrate RT2text’s effectiveness in turning structured summarized trajectory into meaningful narrative, enhancing data accessibility for analysis and decision-making. The code for this work is available at https://github.com/RepresentantativeMAT/RT2text.