{"id":168704,"date":"2015-11-01T00:00:00","date_gmt":"2015-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/traffic-prediction-in-a-bike-sharing-system\/"},"modified":"2018-10-16T20:47:34","modified_gmt":"2018-10-17T03:47:34","slug":"traffic-prediction-in-a-bike-sharing-system","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/traffic-prediction-in-a-bike-sharing-system\/","title":{"rendered":"Traffic Prediction in a Bike-Sharing System"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Bike-sharing systems are widely deployed in many major cities, providing a conven\u00adient tra\u00adn\u00ads\u00ad\u00adpor\u00adtation mode for citizens\u2019 comm\u00adutes. As the rents\/returns of bikes at different stations during different periods are unbalanced, the bikes in a system need to be rebalanced all the time. Real-time monitoring cannot tackle this problem well as it is too late to reallocate bikes after an unbalance has occurred. In this paper, we propose a hierarchical prediction model to predict the check-out\/in of each station cluster in a future period so that reallocation can be executed in advance. We propose a bipartite clustering algorithm to cluster stations into groups, based on which the hierarchical prediction of check-out\/in can be done. After the entire traffic of the whole city is obtained by a Gradient Boosting Regression Tree (GBRT), a multi-similarity-based infer\u00aden\u00adce model is proposed to predict the check-out propor\u00adtion across clusters and the inter-cluster transition matrix. Thus, the check-out across clusters can be calculated easily. Then, each cluster\u2019s check-in is inferred from the check-out, inter-cluster transition and trip duration. We evaluate our models on two bike-sharing systems in New York (NY) and Washington (WA), respectively, to confirm our models\u2019 advanta\u00adges beyond baseline approaches.<\/p>\n<\/div>\n<p>(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Codes.zip\">Code<\/a>)(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Data.zip\">Data<\/a>)(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/11\/traffic-prediction-in-a-bike-sharing-system-yuzheng.pptx\">PPT<\/a>)<\/p>\n<p><span id=\"9e5fb397-3fe9-4bfc-b7bb-c1233e73ce24\" class=\"ImageBlock fn\"><img decoding=\"async\" id=\"Image9e5fb397-3fe9-4bfc-b7bb-c1233e73ce24\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/03\/\/urbancomputing-flyer-bike.png\" alt=\"\" \/><span id=\"ImageCaption9e5fb397-3fe9-4bfc-b7bb-c1233e73ce24\" class=\"ImageCaptionCoreCss ImageCaption\"><\/span><\/span><\/p>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bike-sharing systems are widely deployed in many major cities, providing a conven\u00adient tra\u00adn\u00ads\u00ad\u00adpor\u00adtation mode for citizens\u2019 comm\u00adutes. As the rents\/returns of bikes at different stations during different periods are unbalanced, the bikes in a system need to be rebalanced all the time. Real-time monitoring cannot tackle this problem well as it is too late to [&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":"ACM SIGSPATIAL 2015","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems","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":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version can be found at http:\/\/dl.acm.org.","msr_conference_name":"Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems","msr_doi":"10.1145\/2820783.2820837","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Yexin Li, Huichu Zhang, Lei Chen","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2015-11-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2015,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13563],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168704","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"ACM SIGSPATIAL 2015","msr_edition":"Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems","msr_affiliation":"","msr_published_date":"2015-11-01","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":"204128","msr_publicationurl":"","msr_doi":"10.1145\/2820783.2820837","msr_publication_uploader":[{"type":"file","title":"traffic%20prediction%20in%20a%20bike%20sharing%20system.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/traffic20prediction20in20a20bike20sharing20system.pdf","id":204128,"label_id":0},{"type":"file","title":"traffic prediction in a bike-sharing system-yuzheng","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/11\/traffic-prediction-in-a-bike-sharing-system-yuzheng.pptx","id":247070,"label_id":0},{"type":"file","title":"Codes.zip","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Codes.zip","id":204130,"label_id":0},{"type":"file","title":"Data.zip","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Data.zip","id":204129,"label_id":0},{"type":"doi","title":"10.1145\/2820783.2820837","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":247070,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/11\/traffic-prediction-in-a-bike-sharing-system-yuzheng.pptx"},{"id":204130,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Codes.zip"},{"id":204129,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Data.zip"},{"id":204128,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/traffic20prediction20in20a20bike20sharing20system.pdf"}],"msr-author-ordering":[{"type":"text","value":"Yexin Li","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":"text","value":"Huichu Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Lei Chen","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[170824],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"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"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168704","targetHints":{"allow":["GET"]}}],"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":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168704\/revisions"}],"predecessor-version":[{"id":530392,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168704\/revisions\/530392"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=168704"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=168704"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=168704"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=168704"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=168704"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=168704"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=168704"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=168704"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=168704"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=168704"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=168704"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=168704"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=168704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}