Typology of Bikeshare Users Combining Bikeshare and Transit

Abstract

This study proposes a methodological framework to understand the behavior of bikeshare-metro-bikeshare (BMB) users and assess the complementarity of bikeshare and transit. This analysis was conducted using Montreal’s Bixi bikeshare data collected over an 8-year period. A k-medoid clustering analysis was performed using three variables describing users’ travel behavior: BMB rate, most frequent BMB trip share, and rate of use of different metro stations. It reveals six groups of BMB users: (1) regular commuters, (2) irregular commuters, (3) occasional commuters, (4) mixed users, (5) leisure users, and (6) utility users. Each group’s share of trips is stable over time. BMB users represent an increasing, yet still marginal, share of 1.8% of Bixi’s annual members. The bikeshare segments of BMB trips averaged 1,180 m, with a standard deviation of 830 m. This confirms bikeshare is useful to complete the first and last kilometer of transit trips. Moreover, BMB trips increased with the expansion of Montreal’s bikeshare network to suburban areas serviced by the metro. This study concludes that bikeshare-metro integration allows bikeshare users to cover greater distances and can thus increase both systems’ ridership.

Bike share responses to COVID-19

“…Bike sharing allows socially distant transit while providing healthy, low-impact exercise for its users in addition to curbing emissions and mitigating traffic congestion ( Bullock et al, 2017 , Fishman et al, 2014 , Marshall and Ferenchak, 2019 , Nehme and Kohl, 2014 ). However, substituting public transit trips with bike sharing may impact efficacy for longer trips that leverage bike share access to public transit and final destinations ( Tarpin-Pitre and Morency, 2020 ).…”

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

“…For example, Ma et al applied a maximum transfer time of ten minutes [15]. In Montreal, Canada, researchers evaluated the trips using bikesharing as both an access and egress mode in a single public transit trip [26]. These trips were identified only if they occurred during public transit operating hours.…”

“…With substantiated backing from the literature, the radius of this circle is established at 100 m [26,28]. The distance is chosen so as to select trips that are near enough to the station to be likely connecting to a transit location.…”

“…A buffer too large would include the consideration of trips that are destined for other locations, while a buffer too small would exclude trips that are legitimately connecting to the station. While there is no fixed buffer radius that would perfectly contain all transit-connecting trips and exclude all nonconnecting trips, recent research supports the assumption that this buffer would serve as a lower bound to identify connections [26,28]. A radius-based sensitivity analysis is further conducted to depict the general number of spatially connecting trips under different sizes of these access/egress circles, where:…”