Evaluating Remote Repositioning for Shared Scooters

Overview

Utilization of vehicles, commonly measured in trips per vehicle per day, is a key to economic and environmental sustainability of shared micromobility (i.e. bikeshare and scootershare) services. With large capital costs tied up in bikes or scooters, a micromobility operator needs its vehicles to be used frequently to break even or turn a profit. Employees or contractors driving vans to reposition scooters, collect them for recharging, or rectify parking problems increase both the net emissions and the operating cost per customer trip served. Therefore, increasing the number of customer trips per day, and reducing the frequency of service vehicle trips, contribute to both environmental and economic goals.

Self-repositioning scooters offer a promising path to increasing micromobility utilization, and lowering costs and emissions. Such scooters would use autonomous driving capabilities to relocate themselves, without having to be moved in a service vehicle. Kondor et al. (2020) assessed the potential reductions in fleet size that could be realized with a self-repositioning fleet in Singapore. They explored two limiting cases: an “oracle” case in which all trips are known in advance; and an “online” case in which vehicles are dispatched on-demand. They concluded that in the “oracle” case, 2300 self-repositioning scooters capable of moving at 5 km/h could serve the same set of trips currently served by 17,000 shared bicycles. In the “online” case, with a requirement that all trips be served within 5 minutes, about 8000 scooters would be needed. In a real system with (imperfect) predictive analysis of demand, the actual reduction in fleet size would likely fall somewhere between these limits.

Research Objectives & Deliverables

We propose evaluating the effects of remote-repositioning capabilities on shared scooter systems from the first US deployment of Segway Ninebot T-60 e-scooters in a pilot project in Boise, Idaho. The project involves a collaboration among the City of Boise, scooter company Spin, and remote repositioning service provider Tortoise. The T-60s are a three-wheeled e-scooter that allows remote operators from Tortoise to pilot them from one location to another at speeds under 5 mph. We will collect data to address research questions about (1) external costs and benefits, (2) operator economics, and (3) user experience. Our research questions in each category include the following:

  • Does the public accept autonomous operations of vehicles at slow speeds?
  • What sorts of safety issues arise during the pilot?
  • How does remote repositioning (RR) affect compliance with parking rules? Data will be collected through parking audits before and after RR deployment, using the MisplacedWheels web application developed with previous C2SMART support.
  • How does RR affect greenhouse gas emissions per revenue mile, when accounting for
    emissions from service vehicle fleets?

Personnel

Don Mackenzie

Don Mackenzie

PRINCIPAL INVESTIGATOR

Daniel Malarkey

Daniel Malarkey

RESEARCHER

Principal InvestigatorDon Mackenzie
Funding SourceC2SMART $85,000
Spin $42,500
Total Project Cost$127,500
USDOT Award #69A3551747124 
Implementation of Research OutcomesThe work will yield quantitative estimates of the benefits to cities, users, and operators of remote repositioning technology for shared electric scooters. These will inform a set of recommendations to city governments for implementing remote repositioning of vehicles by micromobility providers, to increase benefits for all stakeholders.
Impacts/Benefits of Implementation

Expected benefits include an improved ability for cities and the private sector to deploy remote repositioning, and in the future autonomous repositioning, for micromobility vehicles to reduce costs for operators, increase usability for travelers, and mitigate impacts on residents. Ultimately, this will lead to greater availability and use of micromobilty, increasing accessibility and reducing single-occupancy vehicle traffic and its associated traffic and emissions.

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