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- W2969379819 abstract "Given the lack of demand forecasting models for e-scooter sharing systems, we address this research gap using data from Portland, OR, and New York City. A log-log regression model is estimated for e-scooter trips based on user age, income, labor force participation, and health insurance coverage, with an adjusted R squared value of 0.663. When applied to the Manhattan market, the model predicts 66K daily e-scooter trips, which would translate to 67 million USD in annual revenue (based on average 12-minute trips and historical fare pricing models). We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternate modes of transportation that they would likely substitute. The final model parameters reveal a relationship with taxi trips as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters would replace at most 1% of taxi trips; the model can explain $800,000 of the annual revenue from this competition. The distance structure of revenue from access/egress trips is found to differ significantly from that of substituted taxi trips." @default.
- W2969379819 created "2019-08-29" @default.
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- W2969379819 date "2019-08-21" @default.
- W2969379819 modified "2023-09-27" @default.
- W2969379819 title "Forecasting e-scooter competition with direct and access trips by mode and distance in New York City" @default.
- W2969379819 hasPublicationYear "2019" @default.
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