{"id":243509,"date":"1970-01-01T00:00:00","date_gmt":"2016-06-27T09:45:06","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=243509"},"modified":"2016-06-29T17:39:37","modified_gmt":"2016-06-30T00:39:37","slug":"gas-consumption-pollution-emission","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/gas-consumption-pollution-emission\/","title":{"rendered":"Gas Consumption and Pollution Emission"},"content":{"rendered":"<p>In this paper, we instantly infer the gas consumption and pollution emission of vehicles traveling on a city\u2019s road network in current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge cannot only be used to suggest cost-efficient driving routes but also identify the road segments where gas has been wasted significantly. In the meantime, the instant estimation of the pollution emission from vehicles can enable pollution alerts, and, in the long run, help diagnose the root cause of air pollution.<\/p>\n<p>Refer to the publication about this technology at KDD 2011.<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/inferring-gas-consumption-and-pollution-emission-of-vehicles-throughout-a-city\/\">Inferring Gas Consumption and Pollution Emission of Vehicles throughout a City<\/a><\/p>\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-243509-1\" width=\"854\" height=\"480\" preload=\"metadata\" controls=\"controls\"><source type=\"video\/mp4\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/06\/Gas_consumption_KDD2011_yuzheng.mp4?_=1\" \/><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/06\/Gas_consumption_KDD2011_yuzheng.mp4\">https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/06\/Gas_consumption_KDD2011_yuzheng.mp4<\/a><\/video><\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we instantly infer the gas consumption and pollution emission of vehicles traveling on a city\u2019s road network in current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge cannot only be used to suggest cost-efficient driving routes but also identify the road segments where gas has been [&hellip;]<\/p>\n","protected":false},"featured_media":243515,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-243509","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"http:\/\/New%20(YouTube)%20URL","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/243509"}],"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":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/243509\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/243515"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=243509"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=243509"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=243509"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=243509"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=243509"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=243509"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=243509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}