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A study on the trip behavior of shared bicycles and shared electric bikes in Chinese universities based on NL model—Henan Polytechnic University as an example

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  • Jiageng, Niu
  • Lanlan, Zheng
  • Xianghong, Li

Abstract

With the gradual expansion of the university scale, it is extremely inconvenient for students to travel to and from campus and classrooms. Bikeshare and shared electric bike (e-bikeshare) are currently promising solutions for campus travel. Most literature has recently focused on bikeshare and e-bikeshare studies in the urban environment, with little attention on campus. However, campuses are significantly different from urban environments, which hinder the active promotion of campus bikeshare and e-bikeshare use. The subject of this paper is Henan Polytechnic University, which is one of the largest bikeshare and e-bikeshare universities in China. Based on the analysis and summary of travel characteristics of Chinese college travelers, this paper uses RP and SP survey data to construct a Nested Logit model to explore the potential factors influencing the use behavior of bikeshare and e-bikeshare in Chinese college environments. The results show that bikeshare and e-bikeshare are the most sensitive to trip time. The influence of trip purpose and weather conditions on the selection of bikeshare and e-bikeshare is smaller than that of time urgency. According to the estimated trip sharing rate of the model, we find that bikeshare and e-bikeshare are attractive.

Suggested Citation

  • Jiageng, Niu & Lanlan, Zheng & Xianghong, Li, 2022. "A study on the trip behavior of shared bicycles and shared electric bikes in Chinese universities based on NL model—Henan Polytechnic University as an example," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122005544
    DOI: 10.1016/j.physa.2022.127855
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    References listed on IDEAS

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    Cited by:

    1. Ma, Changxi & Liu, Tao, 2024. "Demand forecasting of shared bicycles based on combined deep learning models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).

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