ScooterLab: A Programmable and Participatory Sensing Research Testbed using Micromobility Vehicles

TL;DR

ScooterLab is a versatile, community-based testbed with customizable e-scooters equipped with sensors and communication tools, designed to facilitate large-scale research in urban mobility, safety, privacy, and infrastructure planning.

Contribution

This paper introduces ScooterLab, a novel, accessible research platform with customizable micromobility vehicles for interdisciplinary urban mobility studies.

Findings

Enables large-scale data collection on micromobility usage.

Supports development of machine learning and privacy-preserving methods.

Fosters collaboration across computing, urban planning, and transportation fields.

Abstract

Micromobility vehicles, such as e-scooters, are increasingly popular in urban communities but present significant challenges in terms of road safety, user privacy, infrastructure planning, and civil engineering. Addressing these critical issues requires a large-scale and easily accessible research infrastructure to collect diverse mobility and contextual data from micromobility users in realistic settings. To this end, we present ScooterLab, a community research testbed comprising a fleet of customizable battery-powered micromobility vehicles retrofitted with advanced sensing, communication, and control capabilities. ScooterLab enables interdisciplinary research at the intersection of computing, mobility, and urban planning by providing researchers with tools to design and deploy customized sensing experiments and access curated datasets. The testbed will enable advances in machine learning, privacy, and urban transportation research while promoting sustainable mobility.