ABSTRACT

Shared electric micromobility (SEM) services such as shared electric bikes and scooters, as an emerging example of mobile cyber-physical systems, have been increasingly popular in recent years for short-distance trips such as from bus stops to home, enabling convenient mobility through multi-modal transportation and less environmental impact by reducing emission by traffic congestion. However, the success of the service depends on the effective and efficient management of thousands of electric vehicles (e.g., bikes or scooters). Existing management frameworks mainly focused on balancing demand and supply considering energy recharging. However, most of them, if not all, ignored human interactions with systems (e.g., how users select and use vehicles), which leads to a significant gap between experimental and real-world effectiveness. The objective of this project is to develop an interaction-aware management framework for mobile cyber-physical systems.

This project leverages and innovates machine learning and CPS techniques to fill the critical gap of interaction-aware management, thereby providing an integrated framework for human-in-the-loop CPS. The project tasks include: (1) designing new prediction algorithms based on adaptive spatial-temporal correlations to predict the real-time demand of micromobility vehicles, (2) designing new prediction models to imitate human interactions and new control algorithms to rebalance and charge vehicles considering human interactions, and (3) testing the approaches in a real-world testbed of a micromobility service provider. The success of this project could lead to a paradigm shift in CPS with human-in-the-loop by explicitly incorporating human interaction modeling and prediction. The proposal is timely due to the increasing interest in micromobility services and human-in-the-loop systems, as well as the proliferation of computing artifacts that interact with or monitor the physical world. This project will also contribute to the curriculum of CPS courses and will engage undergraduate and graduate students in STEM disciplines and from underrepresented groups.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Please report errors in award information by writing to: awardsearch@nsf.gov.