Optimizing public transport and encouraging soft mobility can help reduce greenhouse gas emissions. It implies to understand users' decision-making patterns regarding transports. The BMDec project focuses on home/work mobility, and aims to provide decision-support tools for transport stakeholders. This paper presents two experimental contributions in mobility data collection and machine learning for the prediction of transport modes used.