Citation

Zuniga-Garcia N, Tec M, Scott JG, Machemehl RB. Transp. Res. C Emerg. Technol. 2022; 139: e103660.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trc.2022.103660

PMID

unavailable

Abstract

E-scooters are an alternative for short trips and are particularly suitable for solving the last-mile transit problem, yet their impact on transit is not well understood. There is a need to understand the e-scooter demand patterns and users' characteristics to develop adequate policies and regulations. In this research, we 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. We use a revealed-preference survey to evaluate the e-scooter usage in one of the highest-demand areas in the City of Austin, corresponding to a university campus. We explore population characteristics, mode shift, and mode interaction. Then, using publicly available datasets, we 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 of both types of trips (e.g., location of attractive zones), known as confounding variables. We 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 the 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.

Language: en

Keywords

Electric dockless scooters; Gradient boosting; Machine learning; Micromobility; Public transportation; Ride-share