  Route choice is often modelled as a two-step procedure in which travellers
choose their routes from small sets of promising candidates. Many methods
developed to identify such choice sets rely on assumptions about the mechanisms
behind the route choice and require corresponding data sets. Furthermore,
existing approaches often involve considerable complexity or perform many
repeated shortest path queries. This makes it difficult to apply these methods
in comprehensive models with numerous origin-destination pairs. In this paper,
we address these issues by developing an algorithm that efficiently identifies
locally optimal routes. Such paths arise from travellers acting rationally on
local scales, whereas unknown factors may affect the routes on larger scales.
Though methods identifying locally optimal routes are available already, these
algorithms rely on approximations and return only few, heuristically chosen
paths for specific origin-destination pairs. This conflicts with the demands of
route choice models, where an exhaustive search for many origins and
destinations would be necessary. We therefore extend existing algorithms to
return (almost) all admissible paths between a large number of
origin-destination pairs. We test our algorithm on a road network modelling the
Canadian province British Columbia and analyze the distribution of locally
optimal paths in the province.
