<p>In recent years, the rapid advancement of shared electric bicycles has exerted a significant influence upon the interrelationship between shared electric bicycles and other modes of transportation. The present paper conducts a spatiotemporal analysis concerning shared electric bicycles within Kunming, a pivotal tourist destination. Four models are compared to investigate the impact of the constructed environment on the interplay of shared electric bicycles. Moreover, this investigation stratifies interconnection scenarios into two distinct categories, viz., access to and egress from other forms of public transportation. Furthermore, the interconnection modes are divided into two types: shared electric bicycle connections with buses and shared electric bicycle connections with subways. For the selection of feature factors, the XGBoost machine learning algorithm is harnessed to compare the importance indices of relevant factors. The research findings suggest that, for the interconnection of shared electric bicycles with subways and buses, it is advisable to increase the deployment of shared electric bicycles in the vicinity of the two types of transportation nodes and their adjacent workplaces in the Chenggong area. By taking into consideration the influence of the constructed environment upon the interplay of shared electric bicycles, this study effectively promotes the spatiotemporal scheduling of shared electric bicycles.</p>

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Evaluating the impact of built environment on shared electric bicycle connectivity in kunming’s public transport system

  • Chaodong Gou,
  • Churan Feng,
  • Shucheng Tan,
  • Miqi Guo,
  • Rongwei Li,
  • Fei Zhao

摘要

In recent years, the rapid advancement of shared electric bicycles has exerted a significant influence upon the interrelationship between shared electric bicycles and other modes of transportation. The present paper conducts a spatiotemporal analysis concerning shared electric bicycles within Kunming, a pivotal tourist destination. Four models are compared to investigate the impact of the constructed environment on the interplay of shared electric bicycles. Moreover, this investigation stratifies interconnection scenarios into two distinct categories, viz., access to and egress from other forms of public transportation. Furthermore, the interconnection modes are divided into two types: shared electric bicycle connections with buses and shared electric bicycle connections with subways. For the selection of feature factors, the XGBoost machine learning algorithm is harnessed to compare the importance indices of relevant factors. The research findings suggest that, for the interconnection of shared electric bicycles with subways and buses, it is advisable to increase the deployment of shared electric bicycles in the vicinity of the two types of transportation nodes and their adjacent workplaces in the Chenggong area. By taking into consideration the influence of the constructed environment upon the interplay of shared electric bicycles, this study effectively promotes the spatiotemporal scheduling of shared electric bicycles.