Latest revision as of 22:56, 22 March 2021

Abstract

Promoting a transition in individuals&rsquo

travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro&ndash

bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots

(2) the distribution of cycling times between origins/destinations and metro stations

and (3) the times when metro&ndash

bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro&ndash

bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers&rsquo

use frequency of integrated metro&ndash

bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/su12041426 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/2071-1050/12/4/1426/pdf under the license http://creativecommons.org/licenses/by/3.0/

• http://dx.doi.org/10.3390/su12041426,

http://www.scopus.com/inward/record.url?scp=85081274847&partnerID=8YFLogxK under the license cc-by

• https://www.mdpi.com/2071-1050/12/4/1426,

https://doaj.org/toc/2071-1050

• https://www.mdpi.com/2071-1050/12/4/1426,

https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1426-d320852.html,

https://www.mdpi.com/2071-1050/12/4/1426/pdf,

https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2F3828c4a5-4a0a-4011-a7bc-7838ab7832e2,

https://research.tue.nl/en/publications/use-frequency-of-metro-bikeshare-integration-evidence-from-nanjin,

https://academic.microsoft.com/#/detail/3006518653

• https://www.mdpi.com/2071-1050/12/4/1426/pdf,

http://dx.doi.org/10.3390/su12041426

under the license https://creativecommons.org/licenses/by/4.0/