Recently, the characteristics of the built environment that influence the integrated use of bicycle sharing and urban subway systems have also been studied (Guo et al., 2021; Guo & He, 2021; Li et al., 2021). Therefore, some researchers have used actual distance traveled to delineate the extent of influence of a city's subway station; however, they still extracted elements of the built environment according to a fixed radius for all metro stations (Guo & He, 2020; Li et al., 2021). In order to reflect the actual cycling distance, a common method is to choose the 85% cumulative distribution of cycling and metro travel distances as the threshold to define the extent of influence of an urban metro station for metro stations (Zuo et al., 2018).
It is noteworthy that in most previous studies, the socioeconomic variables were derived from census data at the community level or traffic analysis zone (TAZ) level, which is somewhat inconsistent with the analyzes of the urban metro station influence scale (Guo et al. , 2021; Guo &. For example, calculated Guo et al. 2021) the population density and employment density in the buffer zone of 800-1500 m for a subway station based on TAZ to measure the potential demand of bicycle-subway transfer trips. For example, according to Wu, Lu, et al. 2021), the accessibility of metro stations, measured by the average journey time in the metro network, positively affects the number of DBS metro transfer trips from/to the station.
This idea has also been used in previous studies analyzing the interactions between DBS and other modes of travel (Wu, Chung, et al., 2021;.
Delineating urban metro station influence scope
The overlaps of metro station collection areas were further separated using the Thiessen polygon, and a DBS cycling trip was also assumed to belong to the nearest metro station if its origin and destination were in different metro station collection areas. As a result, a total of 567,364 accessible DBS-metro journeys and 581,798 exit journeys were identified and used to build the model. The second step was to determine the extent of the impact of the built environment on DBS travel.
Unlike previous studies that used a fixed range to sample the built environment factors around metro stations, this study defines a new concept, the urban metro station influence scope, to measure the comprehensive influence area of built environments on DBS–metro transfer trips indicate. . The extent of influence of a station is determined by the 85% cumulative distribution of the DBS cycling distances from/to this station, indicated by the blue area in Figure 4. Compared to the traditional methods that used a single fixed value, calculated on grounds of DBS cycling. distances from all metro stations, the adaptive catchment delineation proposed in this study ensures that each station has its own unique range of cycling influence, balancing both sampling aggregate and station heterogeneity.
Model construction
Metro station entrances and exits were analyzed separately to examine whether a significant distinction existed. Description Daily DBS subway transfer density (entry) Daily DBS subway transfer density (exit) Average bicycle speed for DBS subway transfer trips (entry) Average bicycle speed for DBS subway transfer trip (exit) Number of population activities / city metro station influence area Number of residential population / city metro station. Number of Working Population / City Metro Station Area of Influence Percentage of residents with monthly incomes below 2499 RMB Percentage of households with cars.
Extent of influence of daily metro pass / urban metro station (inside) Extent of influence of daily metro pass / urban metro station (outside) Transfer station = 1; else = 0. Describes the number of nodes connected to a node The number of shortest paths through a node to describe the node'. Takes into account the number of adjacent nodes and the influence of these adjacent nodes Road/Urban Metro Station Influence Scope Lengths Number of Intersections (Main Road) / Urban Metro Station Scope of Influence Number of Intersections (Branch Road) / Extent of impact of urban metro station Length of impact of bicycle lanes / urban metro station.
Subway ridership (inbound) Subway ridership (outbound) Transfer station Subway station degree centrality Subway station betweenness centrality Subway station eigenvector centrality Road density Intersection density/highway Intersection density/branch road Bike lane density. Number of bus stops / urban metro station influence scope Lengths of bus lines / urban metro station influence scope Land use mix with 12 patterns Percentage of commercial land Percentage of residential land Percentage of office land Percentage of industrial land Number of school POI / urban metro station influence scope Number of shopping POI / urban metro station influence scope Number of restaurants -POI / urban metro station influence range Number of park/public square POI / urban metro station influence range. Proportion of average tree, plant and glass pixels at the sampling points Proportion of average sky pixels at the sampling points Proportion of average building and tree at the sampling points Mixing entropy of street dominant colors (Simpson index) Proportion of sampling points with street lights Proportion of sampling points with traffic signal, draw and screen Proportion of sampling points with fence and barrels Pixel proportion of pavement to road.
Basic features of urban metro station influence scopes
Regression analysis results
Trip density and cycling speed for DBS metro transfer trips in city metro stations affect the scope. Previous studies have reported infinite influence of population density on the DBS subway transfer journeys (Guo et al., 2021; Lin et al., 2018; Wang, Lu, et al., 2020); however, this work revealed positive associations between residential density and both the DBS subway transfer density and cycling speed.
Similar to Lu et al. 2019), no statistically significant correlations of individual income (%<2499 RMB/month) and car ownership (% for households) with DBS metro transfer density were identified in this study. Regarding the characteristics of the subway stations, it was found that the number of subway passengers, both inbound and outbound, is positively correlated with the DBS subway transfer density, which is consistent with previous reports (Fan & Zheng, 2020; Guo et al., 2021). In terms of the sphere of influence of transportation infrastructure in the urban metro station, the intersection density of the main road is negatively correlated with the DBS metro transfer density; this was also reported by Guo and He (2020).
In terms of land use, land use mix was found to be negatively correlated with DBS cycle rate. In terms of residential land use, the results show that the DBS metro transfer bicycle trips fall under a higher percentage of residential land use, contrary to the results of a previous study (Zhang, Shen, et al., 2021). Relative to POI, school density was positively correlated with DBS metro transfer trip density, but negatively correlated with DBS cycling speed.
These results show that the density of commercial facilities in the impact goals of urban subway stations is negatively related to the DBS subway transfer trip density, which is consistent with previous studies (Wu, Lu, et al., 2021 ;. In this work, no significant correlation was observed between colors and DBS-metro transfer trips; this may help ease certain constraints for urban designers. Regarding the presence of transport infrastructure, a finding unexpected was that the presence of street lights was negatively correlated with both DBS cycling speed and outbound trip density in urban metro station impact goals.
Policy implication
Similar to the intersection density on main roads, as previously explained, the presence of traffic signals negatively affects the DBS trip density in the scope of influence of urban metro stations due to the traffic delay they cause among cyclists. Conversely, the presence of barriers within the urban metro station's scope of influence positively affected the cycling density and speed of the movement. Subsequently, the macro-scale and micro-scale built environment variables were comprehensively extracted from the multi-source data of the urban metro stations' scope of influence to analyze their relationships with the trip density and cycling speed of DBS metro transfer trips.
The urban metro station influence range radii were 0.8–3.65 km and about 75% of the urban metro station influence range was smaller than 2 km. The entry and exit DBS trips of the stations showed remarkably similar spatial distributions for both the trip density and cycling speed in the urban metro station influence scopes. An unexpected finding was that the urban metro station influence range near tourist attractions has wider ranges and lower cycling speeds.
In terms of socio-economic attribute, metro station attribute and macro-scale built environment factors, 10 independent variables were statistically correlated with DBS-subway transfer trip density for urban metro station impact purposes ; further, 11 independent variables were correlated with cycling speed. Furthermore, for small-scale built environment factors, correlations of four independent variables with DBS travel density and those of three variables with cycling speed for urban metro station impact purposes were identified. Interestingly, although overall color does not have a significant impact on DBS-metro transfer trips in urban metro station catchment areas, greenery plays an essential role in promoting cycling environments that attract more DBS trips and improve the experience of cycling.
Unexpectedly, the presence of street lights leads to a decrease in the trip density and cycling speed in urban metro station influences. Finally, the findings of the current study have significant implications for understanding how micro-scale building environmental factors such as greenness, the presence of street lights, the presence of barriers, and the presence of signals affect DBS–metro transfer trips in urban metro stations. Explores the effects of the built environment on two transit modes for subways: dockless bike sharing and taxis.