Movement Data

Every day millions of individual mobility decisions are made, but the data about actual personal mobility behavior is missing. Different technologies try to find the full travel chains, usually based on user’s location and speed, but it rarely brings any real value in understanding the actual personal mobility profile.

We at Moprim are experts collecting location data using smartphones, but furthermore we try to solve the missing piece for fully indexing user’s daily movement and getting the full travel chain: mode of transport. Transport mode discovery (TMD, also known as transport mode detection in some research) is a set of technologies trying to solve the mode of transport, for example if the user is walking, in a car, metro or bus or just happily doing nothing (we call it as stationary).

We approach the problem by collecting the sensor data coming from the accelerometer and gyroscope on the smartphones and trying to understand the nature of the movement. We have collected a vast amount of ground truth data from different kind of modalities, and we create engines (we call them Moprim TMD SDK’s), that can be used in smartphones to recognize the modality when it happens.

Lots of our R&D is focused on building clever data collection platforms, and machine learning algorithms able to recognize vast amount of features in a way that they can be used in restricted environment, where power consumption and other limitation apply (this is the user’s smartphone).

Our target is to able to index all the people’s movement in the world, and use that data for the better wellbeing of people and the big data coming from the people to make their cities smarter for them.