Influencing transportation demand can significantly reduce CO2 emissions. Individual user mobility models are key to influencing demand at personal and structural levels. Constructing such models is a challenging task that depends on a number of interdependent steps. Progress on this task is ham- strung by the lack of high quality public datasets. We introduce MobilityNet: the first step towards a common ground for multi-modal mobility research. MobilityNet solves the holistic evaluation, pri- vacy preservation and fine grained ground truth problems through the use of artificial trips, control phones, and repeated travel. It currently includes 1080 hours of data from both Android and iOS, representing 16 different travel contexts and 4 different sensing configurations.