PaRE: A System for Personalized Route Guidance

The turn-by-turn directions provided in existing navigation applications are exclusively derived from underlying road network topology information, i.e., the connectivity of edges to each other. Therefore, the turn-by-turn directions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation that ignores human cognition of the geographic space is often verbose and redundant for the drivers who have knowledge about the geographical areas. In this paper, we study a Personalized RoutE Guidance System dubbed PaRE with which the goal is to generate more customized and intuitive directions based on user generated content. PaRE utilizes a wealth of user generated historical trajectory data to extract namely “landmarks” (e.g., point of interests or intersections) and frequently visited routes between them from the road network. The extracted information is used to obtain cognitive customized directions for each user. We formalize this task as a problem of finding the optimal partition for a given route that maximizes the familiarity while minimizing the number of segments in the partition, and propose two efficient algorithms to solve it. For empirical study, we apply our solution to both real and synthetic trajectory datasets to evaluate the performance and effectiveness of PaRE .