关键词: 城市交通, 建成环境, 轨道交通客流, 成长性, 极限梯度提升模型

Abstract: To more accurately grasp the generation law of metro ridership, the relationship between metro ridership and the surrounding built environment is explored from the perspective of the growth stage. Taking Shanghai Metro as the studied case, the built environment is described by 14 factors such as population and employment density, land use, road network density, the number of entrances and exits, betweenness, etc., based on multi-source data including Shanghai smartcard data, population and economic census data, Point of Interest (POI), and road network. The Ordinary Least Square (OLS) model and the eXtreme Gradient Boosting (XGBoost) model are used to quantify the effects of the built environment on metro ridership. The results show that the XGBoost model based on a machine learning algorithm has better model performance than the OLS model. As for the contribution of independent variables, in the early stage, the number of entrances and exits (21.9%), the density of the population (15.9%), and the density of the road network (9.8% ) are the most important built environment factors affecting the metro ridership. In the short term, the built environment such as commercial land (16.5% ), floor area ratio (11.1% ), and job density (8.5% ) have become the key to improving subway passenger flow. In the long term, metro ridership depends on the level of integration between land use and transportation, such as the number of entrances and exits (18.9% ), commercial land development (16.6% ), and the number of transfer lines (7.7% ). The results confirm the progress characteristic relationship between metro ridership and the built environment around the station, which provides an important reference for formulating the integrated development strategy of transit-oriented development according to the time and contexts.

Key words: urban traffic, built environment, metro ridership, growth stage, eXtreme Gradient Boosting