California sets ambitious climate goals that demand a sharp reduction in the per-capita vehicle miles traveled (VMT). Traditionally, researchers have relied on three types of VMT data collection methods – travel surveys, passively collected data, and simulated data – to estimate VMT or understand factors affecting VMT. However, each of these methods has disadvantages for obtaining a reliable VMT dataset. Although travel surveys are an inexpensive way to collect VMT data with rich traveler attributes, they often suffer from bias and errors in reporting or recalling. Passively collected VMT data, such as traffic count data, can provide precise VMT data, yet they often lack information about “who” and “why” of travel. Lastly, simulated VMT data is useful when making counterfactual testing, but they are after all not real data. In this study, to fulfill the gap between current data collection strategies, the authors introduce a WhatsApp-based VMT data collection framework. The framework deployed on the Azure cloud platform automatically lets study participants submit photos of their odometer, communicates with them for administrative events, manages submitted images and other information of the participants, and displays that data on the web UI for the study administrator. With a pilot study with 77 participants, the authors successfully collected 173 photos of the odometer. Although only 3 photos out of 173 were unusable, demonstrating the capability of the framework, a couple of potential improvements, such as an interface that aligns the photo timestamps over the days or better recruitment approaches, should be addressed in future work.
