{"id":158444,"date":"2009-11-04T00:00:00","date_gmt":"2009-11-04T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/trajectory-simplification-method-for-location-based-social-networking-services\/"},"modified":"2018-10-16T20:20:42","modified_gmt":"2018-10-17T03:20:42","slug":"trajectory-simplification-method-for-location-based-social-networking-services","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/trajectory-simplification-method-for-location-based-social-networking-services\/","title":{"rendered":"Trajectory Simplification Method for Location-Based Social Networking Services"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The increasing availabilities of GPS-enabled devices have given rise to the location-based social networking services (LBSN), in which users can record their travel experiences with GPS trajectories and share these trajectories among each other on Web communities. Usually, GPS-enabled devices record far denser points than necessary in the scenarios of GPS-trajectory-sharing. Meanwhile, these redundant points will decrease the performance of LBSN systems and even cause the Web browser crashed. Existing line simplification algorithms only focus on maintaining the shape information of a GPS trajectory while ignoring the corresponding semantic meanings a trajectory implies. In the LBSN, people want to obtain reference knowledge from other users\u2019 travel routes and try to follow a specific travel route that interests them. Therefore, the places where a user stayed, took photos, or changed moving direction greatly, etc, would be more significant than other points in presenting semantic meanings of a trajectory. In this paper, we propose a trajectory simplification algorithm (TS), which considers both the shape skeleton and the semantic meanings of a GPS trajectory. The heading change degree of a GPS point and the distance between this point and its adjacent neighbors are used to weight the importance of the point. We evaluated our approach using a new metric called normalized perpendicular distance. As a result, our method outperforms the DP (Douglas-Peuker) algorithm, which is regarded as the best one for line simplification so far.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The increasing availabilities of GPS-enabled devices have given rise to the location-based social networking services (LBSN), in which users can record their travel experiences with GPS trajectories and share these trajectories among each other on Web communities. Usually, GPS-enabled devices record far denser points than necessary in the scenarios of GPS-trajectory-sharing. Meanwhile, these redundant points [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-158444","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"SIGSPATIAL GIS workshop on location-based social 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Chen","user_id":0,"rest_url":false},{"type":"user_nicename","value":"yuzheng","user_id":35088,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yuzheng"},{"type":"user_nicename","value":"xingx","user_id":34906,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=xingx"},{"type":"text","value":"Kai Jiang","user_id":0,"rest_url":false},{"type":"text","value":"Chunping Li","user_id":0,"rest_url":false},{"type":"text","value":"Nenghai 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