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Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China

Yang Liu (), Yanjie Ji (), Tao Feng () and Zhuangbin Shi ()
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Yang Liu: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Southeast University Road 2, Nanjing 211189, China
Yanjie Ji: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Southeast University Road 2, Nanjing 211189, China
Tao Feng: Department of Urban Science and Systems, Eindhoven University of Technology, P.O.Box 513, 5600MB Eindhoven, The Netherlands
Zhuangbin Shi: Intelligent Transportation System Research Center, Southeast University, Southeast University Road 2, Nanjing 211189, China

Sustainability, 2020, vol. 12, issue 4, 1-1

Abstract: Promoting a transition in individuals’ 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–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–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–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’ use frequency of integrated metro–bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.

Keywords: metro–bikeshare integration; frequency; ordered logistic regression; survey; GPS data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2020
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