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.