Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Abstract

The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

The Public Bicycle as a Feeder Mode for Metro Commuters in the Megacity Beijing: Travel Behavior, Route Environment, and Socioeconomic Factors

“…As for the built environment, mixed land use, parks and public squares, and transportation facilities such as bicycle lanes have been found to be positively related to integrated use (Guo and He 2020). Areas that are less economically developed with fewer signalized intersections are more favored by those who use metro-bicycle integration (Ni and Chen 2020). However, research conclusions have differed among cities.…”

“…The public bicycle is widely believed to be one of new ways to solve the last-mile problem (Liu et al 2012;Ma et al 2015;Ni and Chen 2020). It can improve the accessibility to public transportation systems by directly connecting the origin/destination and metro stations (Liu et al 2012;Zhang et al 2019).…”

“…For example, a study in Hamilton County, Ohio, showed that the transit access distance covered by bicycling may be tripled compared with walking, such that bicycling enhances transit equity (Zuo et al 2020). A study in Beijing pointed out that people prefer to use dockless bike sharing instead of taxis as a transfer mode, especially in less economically developed areas (Ni and Chen 2020). Furthermore, promoting public cycling provides direct health benefits at the population level (Mueller et al 2015), along with substantial environmental and economic advantages (Woodcock et al 2009; Handy and Xing 2011;Handy et al 2014).…”

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

“…Bicycle turnover and time-varying demand characteristics varied widely across docked public bicycle stations, with bicycle stations adjacent to public transportation stations, shopping centers, restaurants, schools, and finance having high ridership on both weekdays and weekends, but stations adjacent to office concentration areas having high ridership only on weekdays [39]. Dockless bike-sharing usually serves the initial or last mile of public transportation transfer connections, and suburban areas with dense branch roads and few traffic light intersections are more popular among bike-sharing users [40]. Population density and employment density are the two most significant factors affecting bike-sharing use, and built environment has a nonlinear effect on bike-sharing use [41].…”

Dockless bike-sharing’s impact on mode substitution and influential factors: Evidence from Beijing, China

“…To what extent findings from docked bike-sharing studies on mode substitution apply to dockless bike-sharing remains unexplored. In addition, although increasing evidence has revealed that individuals' socio-demographics, attitudes, and built environment often influence how, when, and where dockless bike-sharing systems are used (e.g., Guo & He, 2020;Ni & Chen, 2020), there is a lack of knowledge about how these factors relate to the mode that is substituted for. Finally, it is unclear what role varying travel purposes, such as commuting or traveling for leisure, play in mode substitution patterns of dockless bike-sharing.…”