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

2025-01-10

AI Summary

The rapid proliferation of urban micromobility (e.g., e-scooters) raises critical challenges in safety, privacy, infrastructure compatibility, and sustainable governance—necessitating multimodal, real-world data. To address this, we introduce ScooterLab: the first open-source community testbed for urban computing and transportation science. It integrates embedded sensors (IMU, GPS, camera) into instrumented e-scooters, augmented with LoRaWAN/4G connectivity and real-time edge control firmware, and supported by a modular software stack for experiment orchestration and data management. Its key contributions are threefold: (1) enabling reproducible, large-scale field experiments through programmable micromobility hardware, participatory sensing, and cross-disciplinary data governance; (2) facilitating training of multimodal ML models for urban analytics; and (3) providing empirical foundations for privacy-enhancing sensing frameworks and evidence-based sustainable mobility policy analysis.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Micro-mobility
• Data-collection Platform
• Urban Planning

Innovation

• Electric Scooters
• Machine Learning
• Eco-friendly Commuting