A segment-level model of shared, electric scooter origins and destinations. (March 2021) - Merlin, Louis A.; Yan, Xiang; Xu, Yiming; Zhao, Xilei
Graphical abstract: Highlights: We build segment-level models of electric scooter trip origins and destinations. Proportion of college educated persons and lower median age are predictors. Tourist attractions, transit stops, hotels, and commercial areas are predictors. The supply of available scooters is the dominant predictor of scooter trip origins. The model can identify street segments with high demand for scooter parking. Abstract: Electric scooters (or e-scooters) have quickly proliferated in cities worldwide, presenting a host of regulatory challenges. We analyze trip origins and destinations for shared e-scooter use at the street-segment level. Street segments are a relevant unit for policy analysis because users park scooters along the streets' curbside, and parking policies target specific street segments. We build Hurdle models for trip origins and destinations using data from Washington DC in 2019. Results show that street segments near tourist sites, hotels, and transit stops attract the most scooter-trip destinations. In contrast, the supply of available e-scooters is the dominant force shaping scooter-trip origins. We find preliminary evidence to suggest that scooters are complementing public transit. Like other studies, we find that areas with younger and more educated demographics correlate with higher e-scooter use. Finally, we demonstrate that the model can identify segments with a high demand for scooter parking.