{"id":2,"date":"2019-11-20T14:36:22","date_gmt":"2019-11-20T14:36:22","guid":{"rendered":"https:\/\/www2.dcp.ufl.edu\/iadapt\/?p=1"},"modified":"2020-07-15T18:15:46","modified_gmt":"2020-07-15T22:15:46","slug":"hello-world","status":"publish","type":"post","link":"https:\/\/dcp.ufl.edu\/iadapt\/hello-world\/","title":{"rendered":"Measuring Citywide Transportation Efficiency"},"content":{"rendered":"
Doctoral students Wei Zhai and Xueyin Bai, and Dr. Zhong-Ren Peng, developed a bottom-up approach to measuring transportation network efficiency with a case study of New York City taxis.<\/strong><\/h5>\n\n\n
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Taxis navigating around Manhattan, New York City. Photo by Ferdinand St\u00f6hr on Unsplash<\/figcaption><\/figure><\/div>\n\n\n\n

October 31, 2019<\/p>\n\n\n\n

Zhai, Wei, Xueyin Bai, Zhong-Ren Peng, Chaolin Gu, \u201cA bottom-up transportation network efficiency measuring approach: A case study of taxi efficiency in New York City,\u201d Journal of Transport Geography, <\/em>Volume 80, October 2019, 102502, (https:\/\/doi.org\/10.1016\/j.jtrangeo.2019.102502<\/a>)<\/p>\n\n\n\n

The article by URP doctoral students Wei Zhai and Xueyin Bai, and URP Professor Dr. Zhong-Ren Peng, addresses two basic research questions: 1) How to measure the transportation network efficiency of each origin-destination pair. 2) Can the approach be applied to measure citywide efficiency? These research questions are very important, because previous methods in transportation network efficiency have not been demonstrated to be effective in measuring citywide efficiency.<\/p>\n\n\n\n

To answer these questions, the paper first created a new transportation network efficiency index, which makes an important contribution by integrating the average speed of each trip and the number of passengers with the conventional distance factor.<\/p>\n\n\n\n

Then, the paper demonstrated that origin-destination-level efficiency can be an alternative cost unit to time (or distance) in measuring citywide efficiency.<\/p>\n\n\n\n

In this way, the bottom-up approach (i.e., the citywide efficiency is derived from origin-destination-level efficiency) is developed, which was applied to the origin-destination-level efficiency measurement and citywide efficiency measurement.<\/p>\n\n\n\n

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The bottom-up approach accounted for trip origin and destination. Photo by Tony Dinh on Unsplash.<\/figcaption><\/figure><\/div>\n\n\n\n

The research results also have important implications in urban and transportation planning: 1) Transportation planners should amend the transportation network between low efficient origins and destinations in New York City identified in this paper. 2) The maps of minimized origin-destination flows indicate that taxi trips within Manhattan and to the two airports (LGA and JFK) are the key areas for maximizing citywide efficiency. Therefore, transportation planners should accordingly optimize the operation of airport taxis to increase the citywide taxi efficiency.<\/p>\n\n\n\n

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<\/p>\n","protected":false},"excerpt":{"rendered":"

Doctoral students Wei Zhai and Xueyin Bai, and Dr. Zhong-Ren Peng, developed a bottom-up approach to measuring transportation network efficiency with a case study of New York City taxis. October 31, 2019 Zhai, Wei, Xueyin Bai, Zhong-Ren Peng, Chaolin Gu, \u201cA bottom-up transportation network efficiency measuring approach: A case study of taxi efficiency in New York […]<\/p>\n","protected":false},"author":2,"featured_media":2756,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"wds_primary_category":0,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"uagb_featured_image_src":{"full":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196.jpg",1000,667,false],"thumbnail":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196-150x150.jpg",150,150,true],"medium":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196-300x200.jpg",300,200,true],"medium_large":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196-768x512.jpg",768,512,true],"large":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196.jpg",1000,667,false],"1536x1536":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196.jpg",1000,667,false],"2048x2048":["https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2019\/11\/photo-1485739139909-d0d1783a7196.jpg",1000,667,false]},"uagb_author_info":{"display_name":"Ferr Bfer","author_link":"https:\/\/dcp.ufl.edu\/iadapt\/author\/dcpsuperadmin\/"},"uagb_comment_info":0,"uagb_excerpt":"Doctoral students Wei Zhai and Xueyin Bai, and Dr. Zhong-Ren Peng, developed a bottom-up approach to measuring transportation network efficiency with a case study of New York City taxis. October 31, 2019 Zhai, Wei, Xueyin Bai, Zhong-Ren Peng, Chaolin Gu, \u201cA bottom-up transportation network efficiency measuring approach: A case study of taxi efficiency in New York…","_links":{"self":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/posts\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/comments?post=2"}],"version-history":[{"count":0,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/posts\/2\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/media\/2756"}],"wp:attachment":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/media?parent=2"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/categories?post=2"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/tags?post=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}