城市轨道交通出行研究：多角度分析与多元数据融合

When studying urban rail transit travel, we need to approach it from multiple perspectives and aspects. Firstly, it is necessary to utilize a large amount of card-swipe data to understand the spatial and temporal variations of population mobility within the city at a macro level, focusing on station-level and network-level analysis. Simultaneously, it is important to understand the characteristics of residents' rail transit usage at an individual level. Secondly, fundamentally, transportation demand originates from people's activity needs, thus there is an inseparable connection between urban rail transit ridership and travel characteristics and the built environment. For instance, the average travel distance and spatial distribution of residents are greatly influenced by land use patterns. Lastly, traditional methods of analyzing rail transit ridership typically rely on single data sources, which may inadequately capture the complexity of passenger behavior. Therefore, integrating diverse data sources for analyzing and modeling rail transit ridership characteristics holds significant theoretical and practical implications.