{"id":186918,"date":"2011-10-20T00:00:00","date_gmt":"2011-10-20T05:13:54","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/urban-computing-with-taxicabs-2\/"},"modified":"2016-08-16T06:38:39","modified_gmt":"2016-08-16T13:38:39","slug":"urban-computing-with-taxicabs-2","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/urban-computing-with-taxicabs-2\/","title":{"rendered":"Urban Computing with Taxicabs"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This video showcases three application scenarios that have been enabled in the urban computing project. 1) Finding smart driving direction based on taxi trajectories; 2) A passenger-cabbie recommender system; 3) Glean the flawed urban planning in terms of people&#8217;s city-wide mobility patterns learned from taxi trajectories.<\/p>\n<p>Contact:<br \/>\nYu Zheng, Researcher at Microsoft Research Asia,<br \/>\nyuzheng@microsoft.com<\/p>\n<\/div>\n<div style=\"width: 854px;\" class=\"wp-video\"><!--[if lt IE 9]><script>document.createElement('video');<\/script><![endif]-->\n<video class=\"wp-video-shortcode\" id=\"video-186918-1\" width=\"854\" height=\"480\" preload=\"metadata\" controls=\"controls\"><source type=\"video\/mp4\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/10\/urban_planning_Ubicomp2011_yuzheng-2.mp4?_=1\" \/><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/10\/urban_planning_Ubicomp2011_yuzheng-2.mp4\">https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/10\/urban_planning_Ubicomp2011_yuzheng-2.mp4<\/a><\/video><\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This video showcases three application scenarios that have been enabled in the urban computing project. 1) Finding smart driving direction based on taxi trajectories; 2) A passenger-cabbie recommender system; 3) Glean the flawed urban planning in terms of people&#8217;s city-wide mobility patterns learned from taxi trajectories. Contact: Yu Zheng, Researcher at Microsoft Research Asia, yuzheng@microsoft.com<\/p>\n","protected":false},"featured_media":196442,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13563],"msr-video-type":[],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-186918","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/h7boo6KfpVs","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186918"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186918\/revisions"}],"predecessor-version":[{"id":243434,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186918\/revisions\/243434"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/196442"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=186918"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=186918"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=186918"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=186918"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=186918"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=186918"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}