Predictive ridesharing based on personal mobility patterns

For digital mobility assistants it is advantageous to know users' mobility habits to be able to infer the most probable departure time and next destination. Different approaches are known to face this challenge, but most of them either have a very static feature model and limited extensibility capabilities or they are very complex and require exponential amount of training data for every added feature. This paper introduces a flexible and extendible mobility model - to represent a user's movement and habits - using a Variable-order Markov Model (VOMM) based on users' mobility patterns enriched with different temporal context information. Since this model uses a tree like data structure, it is possible to find patterns of different lengths in the same training data. Spatio-temporal next location prediction is based on the Prediction by Partial Matching (PPM) algorithm. We examine several classification and regression based machine learning algorithms for probability fusion of next location candidates and possible departure times to obtain the most accurate joint probability for the predicted location. The resulting prediction accuracy is between 60% and 81%.