CRII: CPS: Towards Efficient Shared Electric Micromobility: An Interaction-aware Management Framework for Mobile Cyber-Physical Systems

共享电动自行车和滑板车等共享电动微移动(SEM)服务作为移动网络物理系统的新兴范例，近年来在短途旅行(如从汽车站到家)中越来越受欢迎，通过多式联运实现了便捷的移动，并通过减少交通拥堵造成的排放减少了对环境的影响。然而，这项服务的成功取决于对数千辆电动汽车(例如自行车或滑板车)的有效和高效管理。现有的管理框架主要着眼于平衡供需，考虑能源充电。然而，它们中的大多数(如果不是全部的话)都忽略了人与系统的交互(例如，用户如何选择和使用车辆)，这导致了实验和现实世界之间的有效性之间的巨大差距。本项目的目标是开发一种面向移动网络物理系统的交互感知管理框架，该项目利用并创新机器学习和CPS技术来填补交互感知管理的关键空白，从而为人在环中的CPS提供一个集成的框架。项目任务包括：(1)设计新的基于自适应时空相关性的预测算法来预测微移动车辆的实时需求；(2)设计新的预测模型来模拟人类交互，并设计新的控制算法来考虑人类交互来对车辆进行再平衡和充电；(3)在微移动服务提供商的真实试验台上测试这些方法。该项目的成功可能会导致CPS的范式转变，通过明确地结合人类交互、建模和预测，实现人在环中的CPS。由于人们对微移动服务和人在环系统的兴趣与日俱增，以及与物理世界交互或监控物理世界的计算产品的激增，这项提议是及时的。该项目还将对CPS课程的课程做出贡献，并将吸引STEM学科的本科生和研究生以及来自代表性不足群体的本科生和研究生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.