DDM: A Direction-Distance Model for We-Map Assistance Wayfinding

Objectives: Existing models/ algorithms for the users of wayfinding focus on the landmark weight or the shortest route in we-map, which leads to the important information (e.g. direction and distance) was ignored and did not meet the certain requirements (e.g. the contents in maps is enough totally when decision-making using maps, but meanwhile, there is no redundant information to disturb their decision). To address this issue, a direction-distance model (DDM) for we-map assistance wayfinding is proposed, aiming at providing more detailed information to help the users to make decisions regarding wayfinding. Methods: First, the features of we-map were clarified, aiming at discussing representative relations between the characteristics of the we-map and aid wayfinding. According to the discussion results, it was compared with landmarks and direction-distance viewed as the core objects of we-map, and lastly the direction and distance relations were determined as core objects of we-map. Then, an example was discussed to explain how to meet a specific feature of we-map (i.e. the objects in we-map are totally enough when decision-making using maps, but meanwhile, there is no redundant information to disturb their decision). The specific reason why the traditional direction model cannot be utilized the we-maps, is the traditional direction model has redundant information disturbing users' decisions when wayfinding using maps. Last, a quantification model is built by calculating the azimuth of the path data, and the qualification model is developed to describe the results of the quantification model based on the eight-direction cone model. the method for calculation of the path distance is designed to obtain distance information, and the length function for the road segments and segments are defined combined with the Euclidean distance to compute path distance based on the sum function. Results: The experimental results show that, (1) proposed model can avoid the calculation redundancy, (2) the time complexity is superior compared with the existing spatial direction models, (3) spatial direction relations were described qualitatively and quantitively, as well as (4) the missing information of the direction and distance was added and provided during the dynamic process of the users' wayfinding. Conclusions: A direction-distance model was proposed to solve the problem of important information being ignored and certain requirements not being met. This model can provide more detailed direction and distance information for users when wayfinding using maps, but there is no other redundant information to disturb their decision.