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• The impact of e-scooter sharing on the usage of bike sharing is explored using Chicago data. • The weekly usage of bike sharing in e-scooter sharing operation area declined by 10.2% due to the impact. • Usage of bike sharing by members and non-members declined by 4% and 34% respectively. • Bike sharing usage during workday peak hours and other times did not change and declined. As a new type of shared micromobility, e-scooter sharing first appeared in the United States and became popular worldwide. Considering e-scooter sharing and bike sharing have similar service attributes, the ridership of bike sharing may be affected by the introduction of e-scooter sharing. To date, studies exploring this impact are limited. In this study, we seek to analyze the impact of e-scooter sharing on the usage of bike sharing from trip data of e-scooter sharing and bike sharing in Chicago for a total of 30 weeks. We rely on a difference-in-differences modeling approach based on the propensity score matching method. We found that the average duration of e-scooter trips is shorter than that of bike trips. The introduction of e-scooter sharing reduced the overall bike sharing usage by 23.4 trips per week per station (10.2%). bike sharing usage of non-members and members decreased by 18.0 (34.1%) and 5.4 (4.0%) trips, and that of male and female members decreased by 3.3 (3.1%) and 2.0 (7.3%) trips, respectively. Furthermore, the volume of short-, medium-, and long-duration trips of bike sharing decreased by 10.9 (7.5%), 5.4 (9.6%), and 3.4 trips (20.5%), respectively. Finally, bike sharing use during non-peak hours decreased but was not affected during peak hours. [ABSTRACT FROM AUTHOR]

Ridesourcing is popular in many cities. Despite its theoretical benefits, a large body of studies have claimed that ridesourcing also brings negative externalities (e.g., inducing trips and aggravating traffic congestion). Therefore, many cities are planning to enact or have already enacted policies to regulate its use. However, these policies' effectiveness or impact on ridesourcing demand and traffic congestion is uncertain. To this end, this study applies difference-in-differences (i.e., a regression-based causal inference approach) to empirically evaluate the effects of the congestion tax policy on ridesourcing demand and traffic congestion in Chicago. It shows that this congestion tax policy significantly curtails overall ridesourcing demand but marginally alleviates traffic congestion. The results are robust to the choice of time windows and data sets, additional control variables, alternative model specifications, alternative control groups, and alternative modeling approaches (i.e., regression discontinuity in time). Moreover, considerable heterogeneity exists. For example, the policy notably reduces ridesourcing demand with short travel distances, but such an impact is gradually attenuated as the distance increases. [ABSTRACT FROM AUTHOR]