Measuring Bikeshare Access/Egress Transferring Distance and Catchment Area around Metro Stations from Smartcard Data

Abstract

Metro–bikeshare integration is considered a green and efficient travel model. To better develop such integration, it is necessary to monitor and analyze metro–bikeshare transfer characteristics. This paper measures access and egress transferring distances and catchment areas based on smartcard data. A cubic regression model is conducted for the exploration of the 85th access and egress network-based transferring distance around metro stations. Then, the independent samples t-test and one-way analysis of variance (ANOVA) are used to explore access and egress transfer characteristics in demographic groups and spatial and temporal dimension. Additionally, the catchment area is delineated by applying both the network-based distance method and Euclidean distance method. The result reveals that males outcompete females both in access and egress distances and urban dwellers ride a shorter distance than those in suburban areas. Access and egress distances are both shorter in morning peak hours than those in evening peak hours and access distance on weekdays is longer than that on weekends. In addition, network-based catchment area accounts for over 90% of Euclidean catchment area in urban areas, while most of the ratios are less than 85% in suburban. The paper uses data from Nanjing, China as a case study. This study serves as a scientific basis for policy makers and bikeshare companies to improve metro–bikeshare integration.

Information fusion to estimate resilience of dense urban neighborhoods

“…Catchment area analysis has been extended to include the delineation of boundaries for more than just schools and hospitals. Recent studies have extended problems to include retail store locations [20], bike share access [21], and food store accessibility [22]. Researchers investigating catchment areas are often interested in determining who is served (in terms of socio-economic or demographic characteristics), the size of the catchment area, and relatedly, the ability of the service to meet the demand of the population in the catchment.…”

What Is the Connection? Understanding Shared Micromobility Links to Rail Public Transit Systems in Major California Cities

“…Ma et al defined a maximum transfer distance of 300 m and observed that connecting trips generally occurred less than six times per three weeks [15]. They further computed the bikesharing activity space at each station and concluded that the space was smaller in the downtown, which reinforced the results of other studies showing that connecting trips were shorter in the urban core [22,23]. These studies also found that the connecting activity exhibited a two-peak pattern on weekdays and was attracted by mixed land uses with better job accessibility [15,20].…”

Exploring the Spatial Variation of Access/Egress Distances of Subway Stations Using Mobile Phone Positioning Data in Chengdu, China

“…Te average distance between males (1.126 km) is higher than that of females (0.983 km), consistent with most previous studies. Whether walking or bicycling, the distance between males is higher than that of females [26,33]. In terms of age, the distance of people aged 16-25 is the shortest.…”

“…When considering the travel purpose, compared with shopping travelers, working travelers have the most extended walking distances and the highest possibility of walking to the subway station [14]. For users in bike-sharing, Ma et al [26] found that the distances between males are higher than that of females, and urban residents are shorter than suburban residents. In addition, they also analyzed the possible infuence of time on distance.…”