  Navigation is one of the most widely used applications of the Location Based
Services (LBS) which have become part of our digitally informed daily lives.
Navigation services, however, have generally been designed for drivers rather
than other users such as pedestrians or wheelchair users. For these users the
directed networks of streets and roads do not limit their movements, but their
movements may have other limitations, including lower speed of movement, and
being more dependent on weather and the pavement surface conditions. This paper
proposes and implements a novel path finding algorithm for open areas, i.e.
areas with no network of pathways such as grasslands and parks where the
conventional graph-based algorithms fail to calculate a practically traversable
path. The new method provides multimodality, a higher level of performance,
efficiency, and user satisfaction in comparison with currently available
solutions. The proposed algorithm creates a new graph in the open area, which
can consider the obstacles and barriers and calculate the path based on the
factors that are important for wheelchair users. Factors, including slope,
width, and surface condition of the routes, are recognised by mining the actual
trajectories of wheelchairs users using trajectory mining and machine learning
techniques. Unlike raster-based techniques, a graph-based open area path
finding algorithm allows the routing to be fully compatible with current
transportation routing services, and enables a full multimodal routing service.
The implementations and tests show at least a 76.4% similarity between the
proposed algorithm outputs and actual wheelchair users trajectories.
