  Route choice in multimodal networks shows a considerable variation between
different individuals as well as the current situational context.
Personalization of recommendation algorithms are already common in many areas,
e.g., online retail. However, most online routing applications still provide
shortest distance or shortest travel-time routes only, neglecting individual
preferences as well as the current situation. Both aspects are of particular
importance in a multimodal setting as attractivity of some transportation modes
such as biking crucially depends on personal characteristics and exogenous
factors like the weather. This paper introduces the FAVourite rOUte
Recommendation (FAVOUR) approach to provide personalized, situation-aware route
proposals based on three steps: first, at the initialization stage, the user
provides limited information (home location, work place, mobility options,
sociodemographics) used to select one out of a small number of initial
profiles. Second, based on this information, a stated preference survey is
designed in order to sharpen the profile. In this step a mass preference prior
is used to encode the prior knowledge on preferences from the class identified
in step one. And third, subsequently the profile is continuously updated during
usage of the routing services. The last two steps use Bayesian learning
techniques in order to incorporate information from all contributing
individuals. The FAVOUR approach is presented in detail and tested on a small
number of survey participants. The experimental results on this real-world
dataset show that FAVOUR generates better-quality recommendations w.r.t.
alternative learning algorithms from the literature. In particular the
definition of the mass preference prior for initialization of step two is shown
to provide better predictions than a number of alternatives from the
literature.
