EAGER: Feedback-based Network Optimization for Smart Cities

EAGER：基于反馈的智慧城市网络优化

• 批准号: 1647361
• 负责人: Ilya Safro
• 金额: $ 15.11万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647361&HistoricalAwards=false
• 关键词: EAGER; Feedback; based; Network; Optimization

This EArly-concept Grant for Exploratory Research (EAGER) project will focus on human-infrastructure interactions within future smart cities. Specifically, the goal of this project is to develop a conceptual network optimization framework that exploits user feedback from crowd-sourced data. The next-generation ubiquitous traffic sensors, such as fixed traffic detectors, mobile sensors with location-based services (e.g., Google traffic), and traffic active mobile sensors (e.g., Waze), have been used to retrieve large and diverse geo-located and time-stamped data. In addition, data crowd-sourced from social media and mobile applications are increasingly available for understanding human mobility. On the other hand, existing network optimization models in transportation were largely developed without considering human behavior. This project will explore methods to design an electric vehicle wireless charging network of the future. Such a charging network will provide non-stop, in-motion charging particularly suitable for urban environments. For many users, wireless charging will be an opportunistic, emergency charging choice that supplements distributed charging resources at home, the workplace, and at retail facilities. Traditional network optimization models, which implicitly assume that charging demand is distributed and a given, may not result in a user-satisfactory solution in such tasks as finding the best routes that contain wireless charging network segments. The PIs aim to overcome this deficiency by developing: (i) user feedback-driven network optimization models that explicitly account for user satisfaction, and (ii) fast and scalable optimization methods for real-world, large-scale problem implementations that efficiently and effectively utilize crowd-sourced information. The results of this research will include a "living lab" assessment in which students will develop a mobile app to collect feedback about the route with wireless charging network segments. This feedback will be analyzed and incorporated in optimization models.This approach will be implemented using a combination of large-scale machine learning and optimization solvers. The mobile app will provide basic anonymized information about users, trip types, and route feedback through secured channels. These data and open information about all road segments will be used to explore relevant latent factors and create cost-sensitive support vector machine based classifiers to identify the most suitable segments for wireless charging network and adjust the optimization-based network design. Rather than using traditional, computationally expensive optimization solvers, the PIs will pursue an algebraic multigrid-based approach to cope with large-scale, real-world problems, and leverage their support vector machine solvers.

这项对探索性研究（急切）项目的早期概念赠款将着重于未来智能城市中的人类基础结构互动。具体而言，该项目的目标是开发一个概念网络优化框架，从人群中利用用户反馈。下一代无处不在的流量传感器，例如固定的流量探测器，具有位置的服务（例如Google流量）和流量活动活动的移动传感器（例如Waze）的移动传感器（例如Waze），已被用来检索大型且多样化的地理位置和时间戳记的数据。此外，从社交媒体和移动应用程序中众包的数据越来越多地用于理解人类流动性。另一方面，运输中现有的网络优化模型在很大程度上是开发的，而无需考虑人类的行为。该项目将探讨设计未来电动汽车无线充电网络的方法。这样的充电网络将提供不间断的运动充电，特别适合城市环境。对于许多用户而言，无线充电将是一个机会性的紧急收费选择，可以在家中，工作场所和零售设施中补充收费资源。传统的网络优化模型隐含地假设收充电需求是分发和给定的，这可能不会在找到包含无线充电网络段的最佳路线等任务中提供用户满意的解决方案。 PI的目的是通过开发来克服这种缺陷：（i）用户反馈驱动的网络优化模型明确解释了用户满意度，并且（ii）用于现实世界中的，大规模的问题实现的快速可扩展的优化方法，这些方法有效地有效地利用了人群来源的信息。这项研究的结果将包括“生活实验室”评估，其中学生将开发一个移动应用程序，以收集有关无线充电网络细分路线的反馈。将分析此反馈并将其纳入优化模型。这种方法将使用大型机器学习和优化求解器的组合来实现。该移动应用程序将通过安全渠道提供有关用户，旅行类型和路由反馈的基本匿名信息。这些数据和有关所有道路细分市场的开放信息将用于探索相关的潜在因素并创建基于成本敏感的支持向量机的分类器，以识别用于无线充电网络的最合适段并调整基于优化的网络设计。 PIS不使用传统的计算昂贵优化求解器，而是将采用基于代数的Multigrid的方法来应对大规模，现实世界中的问题，并利用其支持向量机求解器。

项目成果

Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

• DOI: 10.1111/mice.12439
• 发表时间: 2019-07-01
• 期刊: COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
• 影响因子: 9.6
• 作者: Khan, Zadid;Khan, Sakib Mahmud;Ushijima-Mwesigwa, Hayato
• 通讯作者: Ushijima-Mwesigwa, Hayato

Centralities for networks with consumable resources

• DOI: 10.1017/nws.2019.7
• 发表时间: 2019-03
• 期刊: Network Science
• 影响因子: 1.7
• 作者: Hayato Ushijima-Mwesigwa;Zadid Khan;M. Chowdhury;Ilya Safro

Generating realistic scaled complex networks

• DOI: 10.1007/s41109-017-0054-z
• 发表时间: 2016-09
• 期刊: Applied Network Science
• 影响因子: 2.2
• 作者: Christian Staudt;M. Hamann;Alexander Gutfraind;Ilya Safro;Henning Meyerhenke

Utility Maximization Framework for Opportunistic Wireless Electric Vehicle Charging

• 发表时间: 2017-08
• 期刊: ArXiv
• 作者: M. Z. Khan;M. Chowdhury;S. Khan;Ilya Safro;Hayato Ushijima-Mwesigwa