Abstract:

E-scooters are a viable alternative for short trips and are especially well suited to address the last-mile transit issue, but it is unclear how they will affect transit. Understanding the e-scooter demand patterns and users’ characteristics is necessary to develop adequate policies and regulations. In this study, the authors consider the problem of modeling the interaction of e-scooters and bus transit services and provide an overview of e-scooter trips and user characteristics. The authors use a revealed-preference survey to evaluate e-scooter usage in one of the highest-demand areas in the City of Austin, corresponding to a university campus. They explore population characteristics, mode shift, and mode interaction. Then, using publicly available datasets, the authors provide a causal analysis to evaluate the nature of the relationship between e-scooter and transit trips in the whole city. Assessing this relationship is challenging because several factors affect the demand for both types of trips (e.g., location of attractive zones), known as confounding variables. The authors develop a methodological framework to isolate the effects of confounding variables on transit trips using a two-stage regression procedure. The first stage aims to isolate confounding variables using a gradient-boosting regression. The second stage models first and last-mile trips using a negative binomial and a zero-inflated negative binomial count model. The university survey indicated that 12 percent of e-scooter users employed transit to complement their trips. Although small in magnitude, the data modeling results show that a statistically significant relationship was found on the university campus and downtown areas, supporting the survey results and extending the analysis to other areas of the city. However, the overall interaction between the two modes has a small magnitude. The proposed methodology can be used to identify areas with potential e-scooter and transit interaction.