{"id":333626,"date":"2016-12-08T11:18:27","date_gmt":"2016-12-08T19:18:27","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=333626"},"modified":"2018-10-16T20:00:17","modified_gmt":"2018-10-17T03:00:17","slug":"will-turn-predicting-turn-proportions-intersections-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/will-turn-predicting-turn-proportions-intersections-2\/","title":{"rendered":"Where Will They Turn: Predicting Turn Proportions At Intersections"},"content":{"rendered":"

Predicting a driver\u2019s route would be useful for warning a driver of upcoming road hazards, informing about traffic situations, and serving relevant advertising. There are many clues to a driver\u2019s future route, including their past behavior and likely destination. Another clue is the driver\u2019s turn choices at a sequence of intersections. Strung together, the turn choices form a route. This paper develops a basic algorithm, and variations, to predict the aggregate turn behavior of drivers at intersections. Given an intersection with a few different turn options, including the option to continue straight ahead, our goal is to infer the proportion of drivers who will take each option. For ground truth, we use raw turn counts gathered for government traffic studies by our local municipality at 40 different intersections. Our basic turn prediction algorithm is based on the assumption that drivers tend to choose roads that offer them more destination options. This matches our intuition that turning onto a short, dead end road is relatively rare compared to turning onto a highway \u201con\u201d ramp. The best performing algorithm predicts turn proportions with a median error of 0.142.<\/p>\n","protected":false},"excerpt":{"rendered":"

Predicting a driver\u2019s route would be useful for warning a driver of upcoming road hazards, informing about traffic situations, and serving relevant advertising. There are many clues to a driver\u2019s future route, including their past behavior and likely destination. Another clue is the driver\u2019s turn choices at a sequence of intersections. Strung together, the turn […]<\/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":"Personal and Ubiquitous Computing","msr_chapter":"","msr_edition":"Personal and Ubiquitous Computing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"7","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"591-599","msr_page_range_start":"591","msr_page_range_end":"599","msr_series":"","msr_volume":"14","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"Best presentation award at Fourth International Symposium on Location and Context-Awareness (LoCA 2009), May 7-8, 2009, Tokyo, Japan","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":"2009-05-07","msr_highlight_text":"","msr_notes":"Best presentation award at Fourth International Symposium on Location and Context-Awareness (LoCA 2009), May 7-8, 2009, Tokyo, Japan","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"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":[13563],"msr-publication-type":[193721],"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-333626","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Personal and Ubiquitous Computing","msr_affiliation":"","msr_published_date":"2009-05-07","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"Personal and Ubiquitous Computing","msr_pages_string":"591-599","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"14","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"7","msr_organization":"","msr_how_published":"","msr_notes":"Best presentation award at Fourth International Symposium on Location and Context-Awareness (LoCA 2009), May 7-8, 2009, Tokyo, Japan","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":"333632","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"Where Will They Turn: Predicting Turn Proportions At Intersections","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/12\/Turn-Proportions-final-web-1.pdf","id":333632,"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":[],"msr-author-ordering":[{"type":"user_nicename","value":"jckrumm","user_id":32203,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jckrumm"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144633],"msr_project":[],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inbook","related_content":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/333626","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":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/333626\/revisions"}],"predecessor-version":[{"id":517996,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/333626\/revisions\/517996"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=333626"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=333626"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=333626"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=333626"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=333626"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=333626"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=333626"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=333626"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=333626"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=333626"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=333626"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=333626"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=333626"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}