Designing a Mobility Intelligence System for Decision-making with Shared Mobility Data

Abstract

Shared mobility has the potential to become an important driver for sustainable mobility. However, the rapid growth of services in already congested urban areas presents cities with major challenges. It becomes apparent that cities lack tools to manage mobility across all shared mobility services. We propose a mobility intelligence system for cities to leverage the vast amounts of data generated by shared fleets for decision-making. The system is designed to support cities in monitoring, regulating, and optimizing shared mobility. A dashboard provides access to data across all different services. Besides tools for regulating providers, e.g., with no-parking zones, we also provide access to mobility-specific machine learning methods, such as demand prediction. We rely on open source standards for data sharing between cities and providers to facilitate collaboration. The system is designed and implemented as a prototype based on requirements from discussions with cities, public transport agencies, and mobility researchers. As part of the evaluation, eight shared mobility experts tested the system. The results validate the system’s usability for three task scenarios while also revealing potential for future research and development.