Assessing the perception of E-scooters as a practical and equitable first-mile/last-mile solution

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E-scooters have disrupted and altered the urban mobility landscape. During their introductory period, they have been commonly touted as part of a larger micromobility solution that erases equity barriers and solves the first-mile/last-mile problem. However, few studies in the nascent e-scooter literature have considered these claims. In this study, we used a d-efficient method to design and administer a stated choice experiment (SCE) to students at Portland State University (PSU) (n = 1,968). In the SCE, students were given a three-mode labelled choice set consisting of three travel modes: car, bike, and e-scooter + MAX light rail. (The e-scooter + MAX light rail choice is multimodal, with the e-scooter acting as the first-mile access mode). We generated attitudinal indices using exploratory factor analysis (EFA) in order to control for mode perception. To demonstrate the relationship of travel time and cost in addition to other covariates on mode choice, we estimated a mixed multinomial logit (MMNL) model. We performed elasticity and sensitivity analyses to uncover factors that encouraged first-mile/last-mile e-scooter usage. We then used the model to generate catchment area maps for multimodal trips in the Portland area. Results indicated that e-scooters were lackluster in bringing racial and gender equity in transportation. Additionally, we found that there was no place in Portland where combining an e-scooter and light rail to travel to PSU was most utilitarian compared to bike or private car. This suggests that e-scooters are not perceived as a preferred solution to the first-mile/last-mile problem. Yet, our findings revealed “dials” that can be tweaked through policy measures in order to promote this kind of use, including parking pricing, travel times, fares, and traveler attitudes towards modes. Overall, our analysis of the implementation of e-scooters suggests that their promise is overstated, at least without substantial policy changes to encourage desired use cases.