{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:07:37Z","timestamp":1760238457082,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study explores the relationship between the spatial distribution of relative transfer location (i.e., the location of the transfer point in relation to the trip origin and destination points) and the attractiveness of the transit service using smart card data. Transfer is an essential component of the transit trip that allows people to reach more destinations, but it is also the main factor that deters the smartness of the public transit. The literature quantifies the inconvenience of transfer in terms of extra travel time or cost incurred during transfer. Unlike this conventional approach, the new \u201ctransfer location\u201d variable is formulated by mapping the spatial distribution of relative transfer locations on a homogeneous geocoordinate system. The clustering of transfer points is then quantified using grid-based hierarchical clustering. The transfer location factor is formulated as a new explanatory variable for mode choice modelling. This new variable is found to be statistically significant, and no correlation is observed with other explanatory variables, including transit travel time. These results imply that smart transit users may perceive the travel direction (to transfer) as important, in addition to the travel time factor, which would influence their mode choice. Travellers may disfavour even adjacent transfer locations depending on their relative location. The findings of this study will contribute to improving the understanding of transit user behaviour and impact of the smartness of transfer, assist smart transport planning and designing of new transit routes and services to enhance the transfer performance.<\/jats:p>","DOI":"10.3390\/s20164418","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T05:07:23Z","timestamp":1597036043000},"page":"4418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["How Does the Location of Transfer Affect Travellers and Their Choice of Travel Mode?\u2014A Smart Spatial Analysis Approach"],"prefix":"10.3390","volume":"20","author":[{"given":"Jason","family":"Chia","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwoo (Brian)","family":"Lee","sequence":"additional","affiliation":[{"name":"City Planning, City Planning Discipline, School of Architecture and Built Environment, Faculty of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3200-7728","authenticated-orcid":false,"given":"Hoon","family":"Han","sequence":"additional","affiliation":[{"name":"City Planning, City Planning Discipline, School of Architecture and Built Environment, Faculty of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.tranpol.2013.01.002","article-title":"Modelling public-transport users\u2019 behaviour at connection point","volume":"27","author":"Ceder","year":"2013","journal-title":"Transp. 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