{"id":161563,"date":"2011-09-17T00:00:00","date_gmt":"2011-09-17T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/where-to-find-my-next-passenger\/"},"modified":"2018-10-16T22:09:09","modified_gmt":"2018-10-17T05:09:09","slug":"where-to-find-my-next-passenger","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/where-to-find-my-next-passenger\/","title":{"rendered":"Where to Find My Next Passenger?"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers\u2019 mobility patterns and 2) taxi drivers\u2019 pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations (and the routes to these locations), towards which they are more likely to pick up passengers quickly (during the routes or at the parking places) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we propose a parking place detection algorithm and learn the above knowledge (represented by probabilities) from trajectories. Then, we feed the knowledge into a probabilistic model which estimates the profit of a parking place for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using trajectories generated by 12,000 taxis in 110 days.<\/p>\n<\/div>\n<p><span id=\"d2df8c5c-c264-4469-bbce-5da546881354\" class=\"ImageBlock fn\"><img decoding=\"async\" id=\"Imaged2df8c5c-c264-4469-bbce-5da546881354\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/urbancomputing-taxirecommender.jpg\" alt=\"\" \/><span id=\"ImageCaptiond2df8c5c-c264-4469-bbce-5da546881354\" class=\"ImageCaptionCoreCss ImageCaption\"><\/span><\/span><\/p>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers\u2019 mobility patterns and 2) taxi drivers\u2019 pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations (and the routes to these locations), towards which they are more [&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":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 13th ACM International Conference on Ubiquitous Computing (Ubicomp 2011)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the 13th ACM International Conference on Ubiquitous Computing (Ubicomp 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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":170558,"post_title":"T-Drive: Driving Directions based on Taxi Traces","post_name":"t-drive-driving-directions-based-on-taxi-traces","post_type":"msr-project","post_date":"2010-09-16 23:38:30","post_modified":"2017-06-19 13:28:23","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/t-drive-driving-directions-based-on-taxi-traces\/","post_excerpt":"T-drive is a smart driving direction services based on GPS trajectories of a large number of taxis. It helps user find out the practically fastest path to a destination at a given departure time. A\u00a0prototype has been built based on a\u00a0real-world trajectory dataset generated by 30,000 taxis in Beijing in\u00a0a period of 3 monthes. The service is available (within Microsoft corpnet), which\u00a0provides\u00a0a user\u00a0with the practically fastest path with less online computation and according to your&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170558"}]}},{"ID":170824,"post_title":"Urban Computing","post_name":"urban-computing","post_type":"msr-project","post_date":"2016-07-03 10:26:01","post_modified":"2018-04-07 17:32:40","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/urban-computing\/","post_excerpt":"Concept\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (\u4e2d\u6587\u4e3b\u9875) Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170824"}]}},{"ID":248075,"post_title":"Trajectory Data Mining","post_name":"trajectory-data-mining","post_type":"msr-project","post_date":"2016-07-04 00:24:38","post_modified":"2023-07-05 08:23:26","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/trajectory-data-mining\/","post_excerpt":"The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. 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