E-scooter sharing is a potential feeder to complement public transit for alleviating the first-and-last-mile problem. This study investigates the integration between shared e-scooters and public transit by conducting a comparative analysis in 124 European cities based on vehicle availability data.
RESULTS suggest that the integration ratios of e-scooter sharing in different cities show significant variations and range from 5.59% to 51.40% with a mean value of 31.58% and a standard deviation of 8.47%. The temporal patterns of integration ratio for first- and last-mile trips present an opposite trend. An increase in the integration ratio for first-mile trips is related to a decrease in the integration ratio for last mile in the time series. Additionally, these cities can be divided into four clusters according to their temporal variations of the integration ratios by a bottom-up hierarchical clustering method. Meanwhile, we explore the nonlinear effects of city-level factors on the integration ratio using explainable machine learning. Several factors are found to have noticeable and nonlinear influences. For example, the density of public transit stations and a higher ratio of the young are positively associated with the integration ratio to a certain extent. The results potentially support transport planners to collectively optimize and manage e-scooter sharing and public transport to facilitate multi-modal transport systems.
Language: en
Keywords
Explainable machine learning; First-and-last-mile problem; Integration with public transit; Shared micro-mobility; Spatial and temporal patterns