Vision Paper: Using Volunteered Geographic Information to Improve Mobility Prediction

Fine-grained real-time movement prediction is becoming increasingly important, with smartphones and vehicles constantly tracking our position and trying to guess our next location to timely provide us with recommendations, traffic forecasts, or driver assistance. Depending on the tracking accuracy, the recorded locations are first mapped to street segments, using a mobility model to choose the most likely road in case of ambiguities. The main prediction procedure uses a similar movement model (possibly incorporating additional user-specific data) to assess likely future travel choices. While the exact street topology is not essential on a very high level (e.g., when predicting the "next place" someone is going to be), it becomes more and more important if we try to predict the exact position of a person or vehicle. Similarly, different data sources (such as points of interest, land use zones, or building footprints) should be used for predictions at different levels of accuracy. In this paper, we assess current research trends concerning various types of volunteered geographical information (VGI), how this data can be used in different models to compute mobility predictions, and we present our vision for an integrated system that is able to use crowdsourced geographic data to perform mobility prediction at different levels.