Volume 39 Issue 1
Feb.  2021
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YU Erze, ZHOU Jibiao. Travel Characteristics and Influencing Factors of Bike Sharing Based on Spatial Lag Model[J]. Journal of Transport Information and Safety, 2021, 39(1): 103-110. doi: 10.3963/j.jssn.1674-4861.2021.01.0012
Citation: YU Erze, ZHOU Jibiao. Travel Characteristics and Influencing Factors of Bike Sharing Based on Spatial Lag Model[J]. Journal of Transport Information and Safety, 2021, 39(1): 103-110. doi: 10.3963/j.jssn.1674-4861.2021.01.0012
shu

Travel Characteristics and Influencing Factors of Bike Sharing Based on Spatial Lag Model

doi: 10.3963/j.jssn.1674-4861.2021.01.0012
  • 1.

    School of Highway, Chang'an University, Xi'an 710064, China

  • 2.

    Beijing PKU ChinaFront High Technology Co., Ltd, Beijing 100085, China

  • 3.

    School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ZheJiang, China

  • Received Date: 2020-12-09
  • Publish Date: 2021-02-28
    Abstract
  • The paper aims to investigate the spatial-temporal characteristics of the bike sharing system(BSS)and quantify factors affecting BSS usage from the urban spatial environment. The spatio-temporal analysis is conducted to investigate the mobility pattern of BSS using the massive IC-card data in central urban area of Ningbo, China. By considering the spatial autocorrelation of pick-up and drop-off, a spatial lag model is established to identify the internal relationship between BSS usage and spatial variables from population density, road distribution, public transportation, station infrastructure, and built environment. The results show that: ①The global Moran's I for pick-up and drop-off on weekdays and weekends is 0.294, 0.281, 0.272, and 0.271, indicating the spatial correlation is significantly positive. ②The goodness of fit is 0.431, 0.424, 0.412, and 0.401, showing that these models have good fitness and explanatory. ③There are also significant temporal differences between road distribution and built environment variables influencing BSS usage. The length of bus lanes is negatively correlated with the usage demand during weekends, and the POI mixing degree positively affects the demand for pick-up and drop-off on weekdays.

     

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