Personalized landmark adaptive visualization method for pedestrian navigation maps: Considering user familiarity

Landmark‐based pedestrian navigation can assist pedestrians in navigating successfully. Previous studies have investigated the factors affecting the cognitive efficiency of landmark visualization in terms of both the visual salience of landmarks and the personal characteristics of users. However, empirical studies and applications that consider the influence of spatial familiarity on landmark representation are limited. In this article, we propose a personalized landmark adaptive visualization method for pedestrian navigation maps considering user familiarity. We first explore the influence of spatial familiarity on landmark salience and symbols using cognitive experiments. The results showed that unfamiliar people preferred strong visual salience landmarks and image‐based symbols, while familiar people preferred strong semantic salience landmarks and text‐based symbols. Based on these results, a mathematical model of landmark salience for selecting personalized landmarks is proposed, and association rules between landmark salience and symbols are mined. Finally, the framework of a landmark visualization method is proposed based on the rules. To verify the effectiveness of the proposed method, a prototype system is developed, and a comparative experiment is conducted with a Baidu map. Experimental results showed that the proposed method has direct practical implications for the development of pedestrian navigation systems, depending on different target users.