EAGER: Exploring the Coupled Dynamics of Urban Systems Using Data Science and Micro-Experimentation

EAGER:利用数据科学和微观实验探索城市系统的耦合动态

基本信息

  • 批准号:
    1642315
  • 负责人:
  • 金额:
    $ 14.67万
  • 依托单位:
  • 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:
    2016-10-01 至 2020-09-30
  • 项目状态:
    已结题

项目摘要

This EArly-concept Grant for Exploratory Research (EAGER) project will help develop the underlying scientific and engineering foundation necessary to spawn the new technologies and systems necessary to make cities and communities more sustainable. Three to four blocks in downtown Palo Alto, California, will be instrumented to form a living lab for the collection of high-resolution data on building, energy and transportation infrastructure systems and the underlying human systems of the community. By engaging the City of Palo Alto in a close partnership, this project will have broad impacts in both the academic and civic communities. Results will be readily accessible and disseminated to Palo Alto municipal officials to empower municipal officials to make data-informed design, management and policy decisions. This work will include making study data and findings available to public as part of Palo Alto's Open Data Initiative thereby helping to promote a more engaged population and overall citizen well-being. This project will also help train an advanced scientific workforce capable of designing and managing our future cities and communities through pedagogical integration of the "living lab" into MS and PhD course offerings at Stanford University and a Massive Online Open Course (MOOC) initiative.This project addresses the enormous pressure rapid urbanization is exerting on the myriad of complex and interdependent urban systems (e.g., energy, transportation, environmental, buildings). Changes in one system can have substantial impacts on others making it difficult to discern and predict the effects of urban design, management and policy decisions. This work aims to develop and employ a radically new data-driven micro-experimentation framework to characterize and quantify the coupled interactions and dynamics between urban infrastructure and human systems. Three to four blocks in downtown Palo Alto, California, will be outfitted with sensors to form a living lab for the collection of high-resolution data on building, energy and transportation infrastructure systems and the underlying human systems of the community. A multi-dimensional network model will be developed to simultaneously analyze multiple incoming urban data streams. Utilizing the results of the network data analysis, empirical micro-experiments will be conducted in this living lab to understand how changes in one urban system impact other systems. Ultimately, this project will contribute a novel micro-experimental framework for studying the coupled dynamics of urban systems that represents a radical shift away from viewing urban systems as purely technical, and integrates concepts from social policy to study urban systems from a socio-technical perspective.
EARLY概念探索性研究补助金(EAGER)项目将有助于开发必要的基础科学和工程基础,以产生使城市和社区更具可持续性所需的新技术和系统。 加州帕洛阿尔托市中心的三到四个街区将被安装仪器,形成一个生活实验室,用于收集有关建筑、能源和交通基础设施系统以及社区底层人类系统的高分辨率数据。通过与帕洛阿尔托市建立密切的伙伴关系,该项目将在学术界和民间社区产生广泛的影响。研究结果将随时提供给帕洛阿尔托市政府官员,以增强市政府官员根据数据进行设计、管理和决策的能力。 这项工作将包括作为帕洛阿尔托开放数据倡议的一部分向公众提供研究数据和调查结果,从而帮助促进更多参与的人口和整体公民福祉。 该项目还将通过将“生活实验室”教学整合到斯坦福大学的硕士和博士课程中以及大规模在线开放课程(MOOC)计划,帮助培养能够设计和管理我们未来城市和社区的先进科学人才。该项目旨在解决快速城市化对无数复杂和相互依存的城市系统(例如,能源、交通、环境、建筑)。 一个系统的变化可能对其他系统产生重大影响,因此难以识别和预测城市设计、管理和政策决定的影响。这项工作旨在开发和采用一个全新的数据驱动的微观实验框架,以表征和量化城市基础设施和人类系统之间的耦合互动和动态。加州帕洛阿尔托市中心的三到四个街区将配备传感器,形成一个生活实验室,用于收集有关建筑、能源和交通基础设施系统以及社区底层人类系统的高分辨率数据。 将开发多维网络模型,以同时分析多个传入的城市数据流。利用网络数据分析的结果,将在这个生活实验室中进行实证性的微观实验,以了解一个城市系统的变化如何影响其他系统。最终,该项目将为研究城市系统的耦合动力学提供一个新的微观实验框架,这代表了从纯粹技术角度看待城市系统的根本转变,并将社会政策的概念整合到从社会技术角度研究城市系统中。

项目成果

期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow
数据驱动的城市能源模拟 (DUE-S):将机器学习集成到城市建筑能源模拟工作流程中
  • DOI:
    10.1016/j.egypro.2017.12.614
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nutkiewicz, Alex;Yang, Zheng;Jain, Rishee K.
  • 通讯作者:
    Jain, Rishee K.
Human-Technology Frontier, Sensing, and Computing
人类技术前沿、传感和计算
Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems
  • DOI:
    10.1061/(asce)cp.1943-5487.0000806
  • 发表时间:
    2019-03
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Karan Gupta;Zheng Yang;Rishee K. Jain
  • 通讯作者:
    Karan Gupta;Zheng Yang;Rishee K. Jain
Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective
  • DOI:
    10.1016/j.enpol.2020.111327
  • 发表时间:
    2020-04-01
  • 期刊:
  • 影响因子:
    9
  • 作者:
    Roth, Jonathan;Lim, Benjamin;Grueneich, Dian
  • 通讯作者:
    Grueneich, Dian
A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning
基于地理关系学习的城市系统分析数据集成框架
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Rishee Jain其他文献

Rishee Jain的其他文献

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{{ truncateString('Rishee Jain', 18)}}的其他基金

CAREER: UrbanEMOS: An Urban Energy Management Operating System for understanding and co-optimizing building, energy and human systems at multiple scales
职业:UrbanEMOS:城市能源管理操作系统,用于在多个尺度上理解和共同优化建筑、能源和人类系统
  • 批准号:
    1941695
  • 财政年份:
    2020
  • 资助金额:
    $ 14.67万
  • 项目类别:
    Continuing Grant
CPS: Medium: Collaborative Research: Building Information, Inhabitant, Interaction and Intelligent Integrated Modeling (BI5M)
CPS:中:协作研究:建筑信息、居民、交互和智能集成建模(BI5M)
  • 批准号:
    1836995
  • 财政年份:
    2018
  • 资助金额:
    $ 14.67万
  • 项目类别:
    Standard Grant
SEES Fellows: Building Informatics: Utilizing Data-Driven Methodologies to Enable Energy Efficiency and Sustainability Planning of Urban Building Systems
SEES 研究员:建筑信息学:利用数据驱动的方法实现城市建筑系统的能源效率和可持续性规划
  • 批准号:
    1415134
  • 财政年份:
    2014
  • 资助金额:
    $ 14.67万
  • 项目类别:
    Standard Grant
SEES Fellows: Building Informatics: Utilizing Data-Driven Methodologies to Enable Energy Efficiency and Sustainability Planning of Urban Building Systems
SEES 研究员:建筑信息学:利用数据驱动的方法实现城市建筑系统的能源效率和可持续性规划
  • 批准号:
    1461549
  • 财政年份:
    2014
  • 资助金额:
    $ 14.67万
  • 项目类别:
    Standard Grant

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