From trajectory to behavior: Capturing individual travel details using an applet-based GPS tracking system

This is the authors' abstract. We don't add key points for this paper.

Household travel survey data form the foundation of travel behavior modeling and transportation planning, yet traditional interview-based methods face significant challenges related to high labor costs and data quality limitations. Smartphone-based travel surveys have emerged as promising alternatives, but barriers to participation persist despite technological advances. This paper presents an innovative applet-based GPS tracking system designed to minimize participant burden through four integrated modules: (i) data collection via a freely-installed mini-program embedded within a widely-used social media platform, (ii) automated trip extraction using cloud-based algorithms, (iii) intuitive user interfaces for trip validation, and (iv) a comprehensive survey supervision platform. We evaluate this system through comparative analysis across three survey phases conducted in an urban district: an app-based pilot study, a traditional interview-based survey, and our applet-based field implementation. Results indicate that smartphone-based methods match interview-based methods in capturing trip chains, while significantly outperforming them in detecting multi-modal trip details. The applet-based survey approach also achieved notably lower recruitment rejection rates compared to the app-based method, demonstrating greater effectiveness in participant engagement. These findings underscore the feasibility and advantages of lightweight, participant-friendly smartphone-based travel survey methods, providing valuable insights for transportation research and planning practices.