{"id":869,"date":"2018-06-04T10:00:52","date_gmt":"2018-06-04T09:00:52","guid":{"rendered":"http:\/\/www.fpz.unizg.hr\/zits\/?p=869"},"modified":"2018-06-05T10:28:19","modified_gmt":"2018-06-05T09:28:19","slug":"poziv-na-predavanje-estimating-motorized-travel-mode-choice-using-classifiers-an-application-for-high-dimensional-multicollinear-data","status":"publish","type":"post","link":"https:\/\/www.fpz.unizg.hr\/zits\/?p=869","title":{"rendered":"Poziv na predavanje: \u201eEstimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data\u201c"},"content":{"rendered":"<p>Fakultet prometnih znanosti u suradnji sa Znanstvenim centrom izvrsnosti za znanost u podatcima i kooperativne sustave te pripadnim projektom Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) i udrugom ITS Hrvatska pozivaju Vas na predavanje:<\/p>\n<p><strong>&#8220;Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data&#8221;<\/strong><\/p>\n<p>koje \u0107e odr\u017eati profesor Andr\u00e9 Luiz Cunha, University of S\u00e3o Paulo (USP), S\u00e3o Carlos School of Engineering (EESC), Brazil u <strong>\u010detvrtak, 07. lipnja 2018.<\/strong> godine u dvorani<strong> D1<\/strong> na Fakultetu prometnih znanosti Sveu\u010dili\u0161te u Zagrebu, Vukeli\u0107eva 4 s po\u010detkom u <strong>13.00 h<\/strong>. Predavanje \u0107e se prenositi i preko weba preko poveznice <a href=\"https:\/\/connect.carnet.hr\/fpz_predavanje\/\">https:\/\/connect.carnet.hr\/fpz_predavanje\/<\/a>.<\/p>\n<p>Predavanje \u0107e biti na engleskom jeziku. Predvi\u0111eno trajanje s raspravom je 90 minuta te je otvoreno za sve zainteresirane, a posebice studente.<\/p>\n<p>Vi\u0161e o predavanju i predava\u010du pro\u010ditajte u nastavku obavijesti.<\/p>\n<p><strong>Abstract<\/strong>: Studies in the field of discrete choice analysis are crucial for transportation planning. Generally, travel demand models are based on the maximization of the random utility and straightforward mathematical functions, such as logit models. These assumptions lead to a continuous model that presents constraints concerning fitting the data. Artificial Neural Networks (ANN) and Classification Trees (CT) are classification techniques that can be applied to discrete choice models. These techniques can overcome some disadvantages of traditional modeling, especially the drawback of not being able to model high-dimensional multicollinear data. We will present the outcomes of the performance comparison of estimating motorized travel mode choice through ANN and CT with a binary logit in a multicollinear study case (aggregated and disaggregated covariates): Origin-Destination Survey carried out in S\u00e3o Paulo Metropolitan Area, Brazil in 2007. Classification techniques have shown a good ability to forecast, as well as to recognize travel behavior patterns.<\/p>\n<p><strong>CV<\/strong>: Andre Luiz Cunha is a PhD. Professor at the University of S\u00e3o Paulo (USP), S\u00e3o Carlos School of Engineering (EESC), in Brazil. He holds a degree in Civil Engineering from Federal University of Mato Grosso do Sul (UFMS) in 2004. He received the M.Sc. in Transport Engineering from USP in 2007 and the Ph.D. in Transportation Engineering from USP in 2013. Since 2014, he has been actively contributing to teaching and research in programming, database and data analysis at Department of Transport Engineering, USP-EESC. His research interest is in Intelligent Transportation Systems (ITS) with focus in use of sensors and development of devices to collect traffic data in field, computer vision, anomalies detection, pattern recognition, and traffic simulation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fakultet prometnih znanosti u suradnji sa Znanstvenim centrom izvrsnosti za znanost u podatcima i kooperativne sustave te pripadnim projektom Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) i udrugom ITS Hrvatska pozivaju Vas na predavanje: &#8220;Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data&#8221; koje \u0107e odr\u017eati&#8230;<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kadence_starter_templates_imported_post":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"categories":[22],"tags":[],"class_list":["post-869","post","type-post","status-publish","format-standard","hentry","category-novosti"],"_links":{"self":[{"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=\/wp\/v2\/posts\/869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=869"}],"version-history":[{"count":3,"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=\/wp\/v2\/posts\/869\/revisions"}],"predecessor-version":[{"id":878,"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=\/wp\/v2\/posts\/869\/revisions\/878"}],"wp:attachment":[{"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.fpz.unizg.hr\/zits\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}