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| Content Provider | ACM Digital Library |
|---|---|
| Author | Li, Yaguang Demiryurek, Ugur Shahabi, Cyrus Zheng, Bolong Zeng, Kai Su, Han |
| Abstract | In this paper, we study a route summarization framework for Personalized Navigation dubbed PerNav - with which the goal is to generate more intuitive and customized turn-by-turn directions based on user generated content. The turn-by-turn directions provided in the existing navigation applications are exclusively derived from underlying road network topology information i.e., the connectivity of nodes 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 about the geographic space- is often verbose and redundant for the drivers who have knowledge about the geographical areas. PerNav utilizes 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. Then this extracted information is used to obtain cognitive turn-by-turn directions customized for each user. |
| Starting Page | 2125 |
| Ending Page | 2128 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450335317 |
| DOI | 10.1145/2882903.2899384 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Route summarization Personalized navigation Trajectory |
| Content Type | Text |
| Resource Type | Article |
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