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

Abstract

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.