标题: Unlocking the nonlinear TOD-metro ridership relationship: A novel machine learning approach embedding spatiotemporal heterogeneity

作者: Luo, Y (Luo, Yun); Li, BZ (Li, Bozhao); Zhang, H (Zhang, Hui); Kang, MJ (Kang, Mengjun); Su, SL (Su, Shiliang)

来源出版物: JOURNAL OF TRANSPORT GEOGRAPHY 卷: 126 文献号: 104222 DOI: 10.1016/j.jtrangeo.2025.104222 Published Date: 2025 JUN

摘要: Machine learning approaches to unlocking the TOD-metro ridership relationship have attracted great attention due to the strong capability of such approaches to handle the underlying nonlinearity and complexity in this relationship. Considering the peculiarities of spatiotemporal heterogeneity in metro ridership, however, one prominent challenge remains unsettled, namely, the issue that traditional machine learning algorithms are designed to be 'aspatial' and thus only produce global estimations. In this paper, a geographical and temporal random forest regression algorithm (GTRFR) is developed, which extends the traditional random forest (RF) as a disaggregation of a number of local submodels and computes an individual random forest regression for each location i at time j using neighboring observations across time and space. It further employs this algorithm to unlock the nonlinear TOD-metro ridership relationship in the case of the Hangzhou metropolitan area. The results show that the GTRFR outperforms the traditional RF in explaining the TOD-metro ridership relationship. Particularly, the nonlinear TOD-metro ridership relationship is unlocked from two major aspects: (1) the relative importance of TOD structural factors across time and space and (2) spatially and temporally varying threshold effects in the effects of the TOD structural factors. The findings portray a much broader picture of the mechanisms underlying the TOD-metro ridership relationship. This paper contributes to the argument that accounting for spatiotemporal heterogeneity should be beneficial to applying machine learning algorithms to transport geography.

作者关键词: Transit-oriented development; Metro ridership; Threshold effect; Spatially explicit machine learning; Geographical and temporal random forest; regression; Spatiotemporal partial dependence

KeyWords Plus: TRANSIT-ORIENTED DEVELOPMENT; BUILT ENVIRONMENT; DECISION TREES; LAND-USE; STATION AREAS; DEMAND; ASSOCIATIONS; REGRESSION; MODELS; COUNTY

地址: [Luo, Yun; Li, Bozhao; Kang, Mengjun; Su, Shiliang] Wuhan Univ, Sch Resource & Environm Sci, Urban Comp & Visualizat Lab, Wuhan, Peoples R China.

[Zhang, Hui] Beijing Inst Surveying & Mapping, Beijing, Peoples R China.

[Kang, Mengjun; Su, Shiliang] Hubei Luojia Lab, Wuhan, Peoples R China.

通讯作者地址: Su, SL (通讯作者)，129 Luoyu Rd, Wuhan, Hubei Province, Peoples R China.

电子邮件地址: shiliangsu@163.com

影响因子：5.7