With the development of metro systems, the problem of unbalanced ridership into and out of the stations, caused by the singleness of station area development, has become increasingly prominent. Research on land use optimization in metro station areas based on a two-way balance of ridership is proposed. First, the stepwise regression analysis method was used to build a relationship model between ridership and the land use index under the guidance of the two-way balance of ridership. Second, the range was optimized by calculating the land use factors of the metro station area. Finally, the land use of the metro station area was optimized from the perspectives of development intensity and land usage. Taking metro stations in Xi’an as an example, the results show that the land use characteristics of metro station areas are quite different. Under the guidance of the two-way balance of ridership, the current land use values of Daminggongxi Station, Nanshaomen Station, and Tiyuchang Station exceed the optimal value range and can be reduced by more than 2.78%. The current land use values of Chaoyangmen Station, Longshouyuan Station, and Weiyijie Station are within the optimized range. The land use values of Kaiyuanmen Station, Banpo Station, and Fengchengwulu Station are below the optimized range and could be increased by more than 13.7%. In addition, optimizing the development intensity or adjusting the land type is further proposed to ensure that the land use factors of station areas are within the calculated optimal value range. The results provide a reference for the optimization of land use in the Xi’an metro station area.

There is a causal interaction between urban rail passenger flow and the station-built environment. Analyzing the implicit relationship can help clarify rail transit operations or improve the land use planning of the station. However, to characterize the built environment around the station area, existing literature generally adopts classification factors in broad categories with strong subjectivity, and the research results are often shown to have case-specific applicability. Taking 154 stations on 8 rail transit lines in Xi’an, China, as an example, this paper uses the data sources of multiple open platforms, such as web map spatial data, mobile phone data, and price data on house purchasing and renting, then combines urban land classification in the China Urban Land Classification and Planning and Construction La1d Standard to classify the land use in the station area using structural hierarchy. On the basis of extracting fine-grained factors of the built environment, a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model is used to analyze the correlation and influence between the variation of passenger flow and environmental factors. The results show that the area of Class II residential land (called R2) is the basis for generating passenger flow demand during morning and evening peak periods; The connection intensity between rail transit station area and bus services has a significant impact on commuters’ utilization level of urban rail transit. Furthermore, two scenarios in practical applications will be provided as guidance according to the research results. This study provides a general analytical framework using urban multi-source data to study the internal relationship and impact between the built environment of urban rail transit stations and passenger flow demand.

The rail transit station realm is an important urban spatial node that carries various behavioral activities and multiple functions. In order to accurately identify the spatial and temporal distribution of population activities and functional facilities in the rail transit station realm and understand the dynamic influence relationship between them, this paper takes four different types of stations of Xi’an Metro Line 2 as the research object, using real-time positioning data to represent population activities and points of interest (POIs) to represent functional facilities. An analytical framework combining the spatial point pattern identification technique and ordinary least squares (OLS) regression model is proposed. The results show that (1) there is spatial and temporal heterogeneity in the population activities in the rail transit station realm; the density distribution of population activities in different time periods shows the characteristic of clustering within 500 m of the station, regardless of working days or off days; (2) the distribution of shopping service POI, catering service POI, and living service POI in different station realms shows the feature of clustering around the stations; (3) the catering POI, living POI, shopping POI and transportation POI have positive attraction to population activities in different time periods; the constructed OLS model can basically explain the influence relationship between various functional facilities and population activities in all time periods. The conclusions can help city managers understand the spatial and temporal distribution and intrinsic mechanisms of population activities and functional facilities from a microscopic perspective and provide an effective decision-making basis for optimizing the allocation of functional resources in the station realm.

During the different periods of a day, the imbalanced distribution of inbound ridership, that is related to land use, results in extreme flow, which makes metro management challenging. The causes of imbalanced passenger flow from the perspective of land use in metro station areas are studied in this paper. More specifically, based on site classification, the impact of land use, including the floor area ratio and gross floor area on passenger flow, was explored by using a multiple linear regression model. The results first indicate that the impact intensities of the floor area ratio on peak hourly flow were 0.41, 0.21, and 0.20 around business, residential, and mixed sites, respectively. Second, for the abovementioned sites, the types with the greatest impact intensities of gross floor area on peak hourly flow were commercial and business facilities (B), residential (R), as well as administration and public services (A), which were 0.73, 0.32, and 0.87, respectively. Finally, for the land-development-control schemes for business, residential, and mixed sites, the maximum values of the floor area ratio were roughly 7.2, 5.3, and 8.2, respectively. The results presented in this study provide guidance for land development in metro station areas and contribute to avoiding the emergence of extreme passenger flow.