Mobidot – Mobility Profiling from Smartphone Sensor Data: Confidently Know How People Travel #SWI2015

Our SWI problem formulation focuses on advancing the quality of our data derivation. Mobidot infers the route, role, objective, and mode of transportation from Smartphone data. Smartphones possess a variety of sensors, including GPS, mobile telephone (4G) and wi-fi signals, accelerometer-based mode, etc. that can be used to determine the motion and position of the user when coupled with geographic databases.

The continual monitoring and recording of data from Smartphone sensors and the comparison with online geographic databases strain battery usage, and hence a sensing strategy must be devised to optimize information gathering with minimal energy usage. The first objective of the SWI problem is to optimize data measurement quality against battery usage. Sub-objectives:

  1. Devise an optimal scheduling plan for sensing, for instance, regularly via a Smartphone in a `heartbeat’ operating model that detects changes in travel patterns. Detection of optional triggers, adjustment of sensing intervals depending on transportation mode inferred.
  2. Develop a method to filter data on the mobile site in such a way that the crucial information is contained, while the redundant information is thrown away, yet maintaining the performance of the trip analysis (route, mode, role, objective).
  3. Develop methods for inferring motion given sparse data (intermittent or incomplete).

The second objective of the SWI problem is to detect obvious errors in the automatically derived role, objective, and mode detection as already performed by the central software platform of Mobidot:

  1. Identify, based on the provided dataset including Smartphone sensor data (GPS, telephone, and accelerometer), (online) geographic databases, and the already inferred information by Mobidot, what the obvious cases are where the automatic inference is likely to be in error.
  2. Minimize false inferences (`ghost trips’). These are displacements that are detected by the system, but which are not really made by the end-user. A potential reason is GPS drift or a series of inaccurate locations.

Mobidot will provide all relevant and needed data to be able to develop data deduction improvements and test approaches and methods. This includes sample Smartphone multi-sensor data, sensor energy usage stats, and samples of resulting anonymized mobility profiles.


Download popular


Download scientific


Print this page


Mobidot – Mobility Profiling from Smartphone Sensor Data: Confidently Know How People Travel