{"id":156224,"date":"2008-09-21T00:00:00","date_gmt":"2008-09-21T07:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/understanding-mobility-based-on-gps-data\/"},"modified":"2018-10-16T20:18:29","modified_gmt":"2018-10-17T03:18:29","slug":"understanding-mobility-based-on-gps-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/understanding-mobility-based-on-gps-data\/","title":{"rendered":"Understanding Mobility Based on GPS Data"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Both recognizing human behavior and understanding a user\u2019s mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user\u2019s mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people\u2019s motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.<\/p>\n<\/div>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/gps-trajectories-with-transportation-mode-labels\/\">Released trajectory data with transportation labels<\/a>.<\/p>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Both recognizing human behavior and understanding a user\u2019s mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user\u2019s mobility with informative knowledge and provide pervasive computing systems with more context information. In [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","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-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-156224","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 10th ACM conference on Ubiquitous Computing (Ubicomp 2008)","msr_affiliation":"","msr_published_date":"2008-09-21","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"208063","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"Ubicomp270-yuzheng.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Ubicomp270-yuzheng.pdf","id":208063,"label_id":0},{"type":"file","title":"Understanding%20user%20mobility%20based%20GPS%20data.pptx","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Understanding20user20mobility20based20GPS20data.pptx","id":208064,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":208064,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Understanding20user20mobility20based20GPS20data.pptx"},{"id":208063,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Ubicomp270-yuzheng.pdf"}],"msr-author-ordering":[{"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":"user_nicename","value":"wyma","user_id":34861,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=wyma"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[170845,170213],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170845,"post_title":"Computing with Spatial Trajectories","post_name":"computing-with-spatial-trajectories","post_type":"msr-project","post_date":"2011-11-08 23:36:50","post_modified":"2017-06-06 09:31:37","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/computing-with-spatial-trajectories\/","post_excerpt":"Editor: Yu Zheng,\u00a0Xiaofang Zhou Foreword by Jiawei Han Editorial board: Ralf Hartmut G\u00fcting, Hans-Peter Kriegel, Hanan Samet [Order it on Amazon] [Buy it from Springer] [Preview this book (Outline and Preface)] &nbsp; With the rapid development of wireless communication and mobile computing technologies and global positioning and navigational systems, spatial trajectory data has been mounting up, calling for systematic research and development of new computing technologies for storage, preprocessing, retrieving, and mining of trajectory data&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170845"}]}},{"ID":170213,"post_title":"GeoLife: Building Social Networks Using Human Location History","post_name":"geolife-building-social-networks-using-human-location-history","post_type":"msr-project","post_date":"2009-02-06 23:21:46","post_modified":"2023-01-23 06:59:05","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/geolife-building-social-networks-using-human-location-history\/","post_excerpt":"GeoLife is a location-based social-networking service, which enables users to share life experiences and build connections among each other using human location history. Dr. Yu Zheng started this project in 2007 with his team. Application Scenarios GeoLife enables user to share travel experience using GPS trajectories. By mining multiple users\u2019 location histories, GeoLife can discover the top most interesting locations, classical travel sequences and travel experts in a given geospatial region, hence\u00a0enable a generic travel&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170213"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/156224"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/156224\/revisions"}],"predecessor-version":[{"id":433068,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/156224\/revisions\/433068"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=156224"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=156224"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=156224"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=156224"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=156224"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=156224"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=156224"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=156224"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=156224"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=156224"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=156224"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=156224"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=156224"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=156224"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=156224"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=156224"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}