What type of infrastructures do e-scooter riders prefer? A route choice model

Highlights

• E-scooter route choices preferences on university campus.

• Revealed GPS route choice data.

• Route choice modeled using recursive logit model with dynamic choice sets.

• Bicycle friendly infrastructures and roads with low speed limits are preferred.

Abstract

E-scooter is an innovative travel mode that meets the demand of many travelers. A lack of understanding of user routing preferences makes it difficult for policymakers to adapt existing infrastructures to accommodate these emerging travel demands. This study develops an e-scooter route choice model to reveal riders’ preferences for different types of transportation infrastructures, using revealed preferences data. The data were collected using Global Positioning System units installed on e-scooters operating on Virginia Tech’s campus. We applied the Recursive Logit route choice model to 2000 randomly sampled e-scooter trajectories. The model results suggest e-scooter riders are willing to travel longer distances to ride in bikeways (59% longer), multi-use paths (29%), tertiary roads (15%), and one-way roads (21%). E-scooter users also prefer shorter and simpler routes. Finally, slope is not a determinant for e-scooter route choice, likely because e-scooters are powered by electricity.

Introduction

E-scooter is an innovative transportation mode that meets the travel demand of many travelers by providing flexible, affordable, and accessible mobility services. E-scooters are well-suited to replace trips shorter than 2 miles, which are 36% of all trips in the U.S. (U.S. Department of Transportation & Federal Highway Administration, 2019). Currently, the majority of these short trips, i.e., approximately 66% (i.e., calculated using weighted 2017 National Households Travel Survey [NHTS] data [U.S. Department of Transportation & Federal Highway Administration, 2019]), are made by private automobiles. E-scooters have proven popular with the public, with usage increasing at a much faster rate than other forms of shared mobility (i.e., cars, bicycles) (Clewlow, 2019). In 2018, there were 85,000 e-scooters available for public use in 100 U.S. cities, resulting in 38.5 million trips (NACTO, 2018).

In response to growing demand and e-scooter usage, many states and cities have passed laws and regulations to govern the deployment of shared fleets and use of e-scooters. Since the launch of the first shared e-scooter fleet program in 2017, the majority of U.S. states have enacted laws to allow the operation of e-scooters on streets, with some restrictions, such as speed limits, no e-scooters on sidewalks, operation time, driver’s license requirements, age constraints, etc. (Unagi Scooters, 2019). Some states, such as Kentucky and Hawaii, treat e-scooters as bicycles. Many city governments, such as Arlington County, VA, Washington D.C., and most recently, New York City (Toll, 2020), have passed ordinances to locally regulate the use of e-scooters in public spaces, and launched structured pilots or begun permit programs for private sector operators to offer shared fleets of e-scooters to the public.

Local policymakers, however, face the challenge of providing safe and convenient environments to accommodate this emerging travel mode. City transportation planners seeking to adapt the current transportation system for e-scooters must develop an understanding of usage patterns and user needs. Importantly, there is a limited understanding regarding infrastructure preferences of e-scooter riders due to data limitations, especially the lack of revealed preference data. To the authors’ best knowledge, no study, to date, has been devoted to systematically modeling e-scooter user infrastructure preferences using empirical data.

E-scooter riders may have different infrastructure preferences compared with bicyclists. Bicycles are more stable and perform better on rougher roads, due to their geometry and tire size. With smaller tires and their unique geometry designs, e-scooters require a more upright steering angle, which makes them less stable on roads with rough surfaces, such as pebbles and bumps. Meanwhile, e-scooter riders may find roads with steep slopes less intimidating, as riding an e-scooter does not require physical effort. Additionally, e-scooters are also smaller and lighter than bikes, making them easier to switch between sidewalk and street. However, no prior studies have explored the e-scooter riders’ infrastructure preferences to provide insights for infrastructure planning for e-scooters.

This study develops an e-scooter route choice model using revealed preference data collected using Global Position System (GPS) units installed on e-scooters on Virginia Tech Blacksburg Campus. The model results show relative preferences for different transportation infrastructures, as well as the effect of route complexity and topography on route choice. The remainder of the paper is organized as follows. In Section 2, we review existing literature on e-scooter and bicycle route choice preferences to highlight research gaps. In Section 3, we describe the modeling techniques and data used in this study. We provide model results and interpret e-scooter riders’ preferences in transportation infrastructure in Section 4. Finally, we conclude the major contribution of this paper and discuss future research directions in Section 5.