Examining the relationship between built environment and metro ridership at station-to-station level

Abstract

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Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

“…With regard to different regression models, taking into account the overdispersion of ridership, nonlinear relationship between the independent and the dependent variables, and spatial error model could generate better results than the multiple linear regression based on adjusted R 2 . In particular, the nonlinear model, GAM, has both lower MAPE value and higher adjusted R 2 , which is consistent with the findings of existing studies [17,23,42].…”

“…Many studies have found that higher population density and employment density increase the ridership [3,[19][20][21]. Built environment factors around the station such as density, diversity, and design have a significant effect on the ridership of urban rail [14,[22][23][24]. e attributes of the station itself also have a significant impact on ridership, with transfer and terminal stations associated with higher ridership [25][26][27].…”

Built environment and early infection of COVID-19 in urban districts: A case study of Huangzhou

“…The well-supplied medical service is associated with high commercial prosperity at the community level, which indirectly and positively affects the COVID-19 cluster size. Community transportation infrastructure is the key essence of developing convenient commercial and medical facilities that in general ensure citizen’s quality of life ( Peng, Feng, & Timmermans, 2019 ; Peng, Feng, & Timmermans, 2019 ; Gan, Yang, Feng, & Timmermans, 2020 ). However, when it comes to the prevention and control of contagion, the well-developed built environment with high population density is prone to a high transmission rate of the epidemic.…”

Understanding Human Mobility Within Metro Networks – Flow Distribution and Community Detection

“…Moreover, most previous studies focused on the properties of the node (station) including inflow, outflow, and visitation frequency, while a small body of prior studies paid attention to the properties of linkage between stations such as trip displacement and OD (origin to destination) flows. However, the OD flows are more conductive to understand human mobility characteristics from a space-time perspective [16]. Meanwhile, few existing studies are attentive to whether the transport properties are different during different time periods (e.g.…”