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Author:

Zhou, Yuyang (Zhou, Yuyang.) | Tang, Songtao (Tang, Songtao.) | Zhao, Minhe (Zhao, Minhe.) | Lam, William H.K. (Lam, William H.K..) | Sze, Tony N.N. (Sze, Tony N.N..) | Chen, Yanyan (Chen, Yanyan.) (Scholars:陈艳艳)

Indexed by:

EI Scopus

Abstract:

Both of the transportation and the public hospital outpatient services are the key public services provided by the government, which guarantee the safe, healthy and worthwhile life for daily living in metropolitan cities such as Beijing and Hong Kong. Since the citizens usually have to see their medical doctors at the appointed time, they should arrive punctually as scheduled for their outpatient services in public hospitals. In order to find out how people travel to the public hospitals for their scheduled outpatient appointments particularly before and after the bike-sharing system in Beijing, two interview surveys with a total of 1,047 valid samples were conducted at the Beijing Friendship Hospital in October 2016 and 2017. One was before the launch of the floating bike-sharing system in November 2016 while the other one was conducted in October 2017 after this new system has been implemented in Beijing for about one year. Compared to the other non-motorized travel modes, the bike-sharing mode could provide the best trip experience with the highest evaluation scores on the satisfaction, the comfort, the convenience and the punctuality of the outpatient trips. Multinomial Logit (MNL) model is calibrated to investigate the factors to influence the travel mode choice for outpatient trips. The results show that the outpatient travelers are likely to choose the bike-sharing mode for their return outpatient visits at public hospitals in Beijing. Other factors like online map-usage, the acceptable maximum walking time and cycling time, the weekend appointment for outpatient services and the household income have positive impacts on the choice of the bike-sharing mode. © 2018 IEEE.

Keyword:

Surveys Surveying Electric bikes Intelligent vehicle highway systems Bicycles Intelligent systems Logistic regression Hospitals

Author Community:

  • [ 1 ] [Zhou, Yuyang]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Tang, Songtao]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhao, Minhe]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 4 ] [Lam, William H.K.]Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong
  • [ 5 ] [Sze, Tony N.N.]Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong
  • [ 6 ] [Chen, Yanyan]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

Reprint Author's Address:

  • [zhou, yuyang]beijing key laboratory of traffic engineering, beijing university of technology, beijing, china

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Source :

Year: 2018

Volume: 2018-November

Page: 242-247

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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