  Despite a tremendous amount of work in the literature and in the commercial
sectors, current approaches to multi-modal trip planning still fail to
consistently generate plans that users deem optimal in practice. We believe
that this is due to the fact that current planners fail to capture the true
preferences of users, e.g., their preferences depend on aspects that are not
modeled. An example of this could be a preference not to walk through an unsafe
area at night. We present a novel multi-modal trip planner that allows users to
upload auxiliary geographic data (e.g., crime rates) and to specify temporal
constraints and preferences over these data in combination with typical metrics
such as time and cost. Concretely, our planner supports the modes walking,
biking, driving, public transit, and taxi, uses linear temporal logic to
capture temporal constraints, and preferential cost functions to represent
preferences. We show by examples that this allows the expression of very
interesting preferences and constraints that, naturally, lead to quite diverse
optimal plans.
