期刊
TRANSPORTATION RESEARCH RECORD
卷 2676, 期 9, 页码 693-710出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/03611981221088223
关键词
data and data science; geographic information science; geographic information systems; planning and analysis; transportation demand management; peak hour; non-peak hour travel; travel demand modeling; public transportation; planning and development; transit
This study explores the influencing factors and spatial variations of subway trips at the grid level, revealing the impact of the built environment on trip origin and destination and the spatial differences. The findings can guide planning departments and transportation agencies in implementing targeted policies and creating a convenient travel environment.
An accurate understanding of the relationship between subway trips and the built environment is crucial to meet people's travel demands and promote the coordinated development of urban land. However, existing literature mainly examines this relationship at the station level, ignoring the variations between the surrounding areas of the station. Therefore, the present study aims to explore the influencing factors and spatial variations of subway trips at the grid level. First, a method was proposed to extract the subway trips using mobile positioning data in Chengdu, China. Then, two geographically weighted regression (GWR) models were adopted to examine the spatially varying relationships between the subway trip origin and destination and selected explanatory variables. The results show that the hotel, company, residential, tourist, bus, subway, road density, and transfer stations variables positively affect trip origin and destination. However, distance to the nearest subway station has a negative impact. Besides, the goodness of fit of the GWR model is better than that of the global regression model, indicating that the influence of the built environment on trip origin and destination varies across space. This study can guide planning departments and transportation agencies to implement target policies and create a convenient travel environment at the micro-level.
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