학술논문
共享单车城市承载力的测算模型 / Estimating model for urban carrying capacity on bike-sharing
Document Type
Academic Journal
Author
Source
中南大学学报（英文版） / Journal of Central South University. 28(6):1775-1785
Subject
Language
Chinese
ISSN
2095-2899
Abstract
为了合理调控共享单车的供给量,提出了基于需求数据的共享单车城市承载力(UCC)测算模型,由此求取城市能够容纳的共享单车的最大供给量.共享单车的城市承载力体现在道路网络承载力(RNCC)和停车设施承载力(PFCC),采用时空消耗法和基于密度的聚类(DBSCAN)算法,探索了适用于共享单车的RNCC和PFCC计算方法.结合用户需求,对共享单车的城市负荷比进行评估,判断城市承载力是否能满足用户需求,从而对共享单车的供给量和相关设施的布局进行相应调整.应用该模型对南京市各交通小区的城市承载力和负荷比进行了测算.通过与现场实测数据的对比,验证了所提算法的有效性.
As the demand for bike-sharing has been increasing, the oversupply problem of bike-sharing has occurred, which leads to the waste of resources and disturbance of the urban environment. In order to regulate the supply volume of bike-sharing reasonably, an estimating model was proposed to quantify the urban carrying capacity (UCC) for bike-sharing through the demand data. In this way, the maximum supply volume of bike-sharing that a city can accommodate can be obtained. The UCC on bike-sharing is reflected in the road network carrying capacity (RNCC) and parking facilities' carrying capacity (PFCC). The space-time consumption method and density-based spatial clustering of application with noise (DBSCAN) algorithm were used to explore the RNCC and PFCC for bike-sharing. Combined with the users' demand, the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users' demand, so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly. The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing, China. Compared with the field survey data, the effect of the proposed algorithm was verified.
As the demand for bike-sharing has been increasing, the oversupply problem of bike-sharing has occurred, which leads to the waste of resources and disturbance of the urban environment. In order to regulate the supply volume of bike-sharing reasonably, an estimating model was proposed to quantify the urban carrying capacity (UCC) for bike-sharing through the demand data. In this way, the maximum supply volume of bike-sharing that a city can accommodate can be obtained. The UCC on bike-sharing is reflected in the road network carrying capacity (RNCC) and parking facilities' carrying capacity (PFCC). The space-time consumption method and density-based spatial clustering of application with noise (DBSCAN) algorithm were used to explore the RNCC and PFCC for bike-sharing. Combined with the users' demand, the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users' demand, so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly. The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing, China. Compared with the field survey data, the effect of the proposed algorithm was verified.