A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City
A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City
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DOI：
10.1080/15568318.2019.1645921
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发表时间：
2020-09
影响因子：
3.9
通讯作者：
S. Xu;Joseph Y. J. Chow
中科院分区：
文献类型：
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作者：
S. Xu;Joseph Y. J. Chow
摘要 共享单车系统的可持续性取决于自行车网络的连通性和可达性。然而，自行车道基础设施投资对共享单车客流量影响的研究仅限于站点级别的客流量，以支持共享单车运营商的资源分配决策。城市规划机构还需要预测和分析共享单车需求，以便随着时间的推移做出自行车设施的投资决策。为了衡量随着时间的推移，在整个网络范围内建设自行车道对共享单车需求的边际效应，提出了一种具有自回归（AR）扰动的自回归条件异方差（ARCH）模型来捕获系统范围内的自行车骑行量。该模型用于研究纽约市 (NYC) 共享单车平均每日出行次数与自行车道总长度之间的关系，无论具体位置如何。我们的结果显示，纽约市额外安装一英里的自行车道导致共享单车每日出行量平均增加 102 次。空间异质性是通过按行政区划分的两个细分市场（曼哈顿与非曼哈顿）来解决的，这表明自行车道的改善对曼哈顿的自行车共享出行量产生了重大影响，在曼哈顿建设一英里的自行车道，增加了 285 次出行，但在曼哈顿以外的地区效果不佳。此外，模型结果显示，在曼哈顿和非曼哈顿新增 1 个共享单车站点后，每天的出行量分别增加了 135 次和 13 次。边际效应可能因自行车道的具体实施位置而异。我们证明，与之前在站级开发的文献中的研究相反，该模型可以为系统级因果关系和时间滞后特征提供新的见解。在考虑城市机构是否应该立即安装一组自行车道或分几周进行安装时，乘客福利的“折扣率”是通过针对不同类型投资的拟议模型来估算的。投资情景分析结果显示，前期全部投资对于客流效益有较高的有效“折扣率”。
Abstract The sustainability of bikesharing systems depends on the bicycle network connectivity and accessibility. However, studies of bike lane infrastructure investment impacts on bikesharing ridership have been limited to station-level ridership to support bikeshare operators’ resource allocation decisions. City planning agencies also need to forecast and analyze bikeshare demand in order to make investment decisions of bike facilities over time. To measure the marginal effect of building bike lanes on bikeshare demand at a network-wide level over time, an autoregressive conditional heteroscedasticity (ARCH) model with autoregressive (AR) disturbance is proposed to capture system-wide bike ridership. The model is applied to investigate the relationship between bikeshare average daily trip counts and the total length of bike lanes in New York City (NYC) regardless of specific locations. Our results show that the installation of one additional mile of bike lanes in NYC led to an average increase of 102 bikesharing daily trips. Spatial heterogeneity is addressed through two market segments by borough (Manhattan vs. Non-Manhattan), which indicates that the improvement of bike lanes had a significant impact on bikesharing ridership in Manhattan, generating 285 more trips with one mile of bike lane built in Manhattan, but was not as effective outside of Manhattan. In addition, model results show that there were 135 and 13 more trips generated per day when one more bikesharing sharing station was added in Manhattan and Non-Manhattan, respectively. The marginal effect could vary due to the specific implemented location of a bike lane. We demonstrate that this model, as opposed to the previous studies in the literature developed at the station-level, can provide new insights into system-level causality and temporal lag characteristics. When considering whether a city agency should install a set of bike lanes immediately or to stage them over multiple weeks, the “discount rate” for ridership benefits is estimated via a proposed model for different types of investment. The results of investment scenario analysis show that there is a higher effective “discount rate” for ridership benefits from front-loading all of the investment.