Equity implications of electric bikesharing in Philadelphia

Abstract

Bikesharing is an affordable mode of transportation and a potential tool to reduce car usage in cities. However, in many cities, bikesharing seems to be used mostly by affluent populations. Indego, Philadelphia’s bikeshare, embraced the promotion of equity as part of its primary goals. While previous measures were not adequate for that cause, Indego decided to integrate e-bikes into its system to promote usage among current non-users. In this study, I examine how the integration of e-bikes influences Indego’s usage in disadvantaged areas. For that purpose, I combined official publicly available data using spatial analysis methods. Furthermore, I used random forest and spatial negative binomial regression to examine factors associated with shared bicycle and e-bike usage in Philadelphia. The findings show that e-bikes increase the overall usage of Indego, specifically in disadvantaged areas. In these regions, the users use shared e-bikes for commute, leisure, and other utilitarian purposes, while in the rest of the city, users use e-bikes mainly for commuting. I conclude that the integration of e-bikes was successful in promoting bikesharing usage in disadvantaged areas.

Spatial Analysis of Bike-Sharing Ridership for Sustainable Transportation in Houston, Texas

“…Noland et al [9] identified positive correlations between residential land use and bike-sharing trips that occur on weekends and holidays, as well as those between central business land use and weekday trips related to shared bicycle usage. However, their findings indicated an insignificant association between bike-share trips and recreational land use, which aligns with the conclusions of the study by Caspi [10] on Philadelphia's bike-share system. In the Minneapolis-St. Paul, Minnesota area, a higher population density and greater proportion of commercial land use were found to be positively associated with the number of bike-sharing trips taken by annual members.…”

“…However, this correlation was not observed in the regions with a low density of bike-share services, thereby emphasizing the importance of investing in both bike-share systems and bicycle infrastructure. Caspi [10] identified positive correlations between bike-sharing ridership and cycling infrastructure, bus stops, and transit stops in Philadelphia. However, such correlations were not identified for trolley stops and regional train stations.…”

Inferring and comparing trip purposes of shared micromobility services: A case study of Ningbo, China

“…The vast majority of existing studies on SMSs have concentrated on four main areas: the description and profiling of the spatial-temporal characteristics (Chicco and Diana, 2022; Kou and Cai, 2019), the exploration of factors impacting the SMSs’ usage (Li et al, 2020b; Shaer et al, 2021), the planning and operation problems, including fleet scheduling, geofencing area planning (Chen et al, 2022; Zhang et al, 2019b), and the substitution, competition, complementarity between different shared micromobility types, and between these types and other transport modes like car-hailing and public transport (Bieliński et al, 2021; Kroesen, 2017). Furthermore, there is a paucity of studies focused on the issues such as the inequity issue of SMSs (Caspi, 2022), carbon emissions and environmental effects (Liu, 2022), and users’ safety (Li et al, 2022c).…”

Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests

“…The system experimented with ebikes much earlier by introducing a pilot scheme with 10 ebikes in 2018. The fleet grew to 400 by May 2019 [26]. Metro in Los Angeles has 218 stations and around 1500 bikes, of which 164 are electric.…”