Behavioral Trait Extraction and Clustering from Transit User Text Using Large Language Models

Abstract

This paper presents a text based frame-work for identifying and analyzing behavioral traits in public transit users using natural language data. Instead of relying on traditional surveys, the approach extracts behavior patterns directly from user generated text, such as reviews or social media posts. Each sample is represented by a hybrid embedding system, which includes meaning for user language and their nuances and behavioral characteristics like urgency, safety sensitivity, and cost awareness is inferred from the system. The representations are clustered and distinct traveler personas are revealed, depending on their approach to time, comfort, safety, and cost. The results show that language based modeling is capable of detecting behavioral differences, nuances and producing relevant traveler profile information without the need for any explicit user data. This demonstrates the potential for behavior analysis to provide a framework for understanding travel behavior at the population level, as well as at the individual level. Furthermore, the results have important implications for the development of behavior based trip planning and personal travel assistance services that respond to the underlying mindsets of users, helping to create more human centric transportation systems.