ScooterLab: A Programmable and Participatory Sensing Research Testbed using Micromobility Vehicles
Ubaidullah Khan, Raveen Wijewickrama, Buddhi Ashan M. K., A. H. M. Nazmus Sakib, Khoi Trinh, Christina Duthie, Nima Najafian, Ahmer Patel, R. N. Molina, Anindya Maiti, Sushil K. Prasad, Greg P. Griffin, Murtuza Jadliwala
TL;DR
ScooterLab addresses the lack of large-scale, open-access data and research infrastructure for micromobility by delivering a configurable sensing testbed built around instrumented e-scooters. Its architecture combines a Wireless Base Station Computer on each scooter (WBSC), a central Fleet Controller (FC), and a researcher-facing Research Activities Management Portal (RAMP) to enable end-to-end data collection, processing, and analysis in realistic urban settings. The work details hardware and software integrations, data pipelines, and operational procedures, including IRB-approved deployment, to support interdisciplinary studies in urban safety, privacy, and planning. The platform aims to accelerate research across computer science, civil engineering, and urban planning by providing tools to design customized sensing experiments and access curated datasets for sustainable mobility insights.
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.