  Getting insights on human mobility patterns and being able to reproduce them
accurately is of the utmost importance in a wide range of applications from
public health, to transport and urban planning. Still the relationship between
the effort individuals will invest in a trip and its purpose importance is not
taken into account in the individual mobility models that can be found in the
recent literature. Here, we address this issue by introducing a model
hypothesizing a relation between the importance of a trip and the distance
traveled. In most practical cases, quantifying such importance is undoable. We
overcome this difficulty by focusing on shopping trips (for which we have
empirical data) and by taking the price of items as a proxy. Our model is able
to reproduce the long-tailed distribution in travel distances empirically
observed and to explain the scaling relationship between distance traveled and
item value found in the data.
