Privacy-preserving federated learning for transportation mode prediction based on personal mobility data

Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios, such as pandemic control and precaution, product recommendation, customized user profile analysis, traffic management in smart cities, etc. However, utilizing such personal mobility data faces many challenges since users' private information, such as home/work addresses, can be unintentionally leaked. In this work, we build an FL system for transportation mode prediction based on personal mobility data. Utilizing FL-based training scheme, all user's data are kept in local without uploading to central nodes, providing high privacy preserving capability. At the same time, we could train accurate DNN models that is close to the centralized training performance. The resulted transportation mode prediction system serves as a prototype on user's traffic mode classification, which could potentially benefit the transportation data analysis and help make wise decisions to manage public transportation resources.