ECET: Empowering Community-centric Electrified Transportation
Electrified (E-) transportation is rapidly gaining popularity in metropolitan areas to address environmental and sustainability issues induced by urbanization. Ongoing efforts by individuals, transit agencies, and local governments are increasing the availability of E-transportation modes (E-buses, EVs, and E-scooters). This transition could introduce an unprecedented opportunity to improve regional transportation and electricity systems, as well as promote socio-economic growth in urban space. To realize the potential of E-transportation, it is necessary to pursue an interdisciplinary approach and continuous engagement with stakeholders. This project will build a collaborative team to investigate an E-hub concept that seeks to convert transit centers to host various E-modes. The team will collaborate with the public transit agency, regional electric utility, and city government in Austin, Texas to explore the potential of using E-hubs to support urban infrastructure and address social equity issues. This planning activity will benefit our partners with actionable results and ultimately contribute to the development of a large-scale study.
This project will investigate an E-hub concept through a collaborative pilot study on its impacts on urban infrastructure and socio-economic growth. The goal is to develop integrative solutions to promote E-transportation at the scale of urban communities. Using real data sets provided by our partners, we propose three cohesive research thrusts: T1) Locating the E-hubs by developing traffic models with improved resolution and addressing transit equity issues; T2) Powering the E-hubs by considering emerging energy resources and intelligent charging for grid support; and T3) Studying community-level effects of E-hubs, focusing on potentially distributing the impacts across the whole community. The team will closely work with our partners (City of Austin, CapMetro, and Austin Energy) to facilitate the adoption of E-hubs in our community through regular discussions and feedback collection. We will also organize faculty-industry workshops through UT’s Energy Institutive to reach out to industry stakeholders, policymakers, and the public.
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Performance PeriodJuly 2020 - June 2022
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University of Texas at Austin
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Award Number1952193
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Lead PIHao Zhu
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Co-PIVarun Rai
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Co-PIMingyuan Zhou
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Co-PIJunfeng Jiao
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Co-PIAndrew Waxman
Project Material
- Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price
- Effects of urban environmental factors on heat-related emergency medical services (EMS) response time
- Forecasting Traffic Speed during Daytime from Google Street View Images using Deep Learning
- What Are the Relationships between Public Transit and Gentrification Progress? An Empirical Study in the New York–Northern New Jersey–Long Island Areas
- Model-free Learning for Risk-constrained Linear Quadratic Regulator with Structured Feedback in Networked Systems
- Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
- Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning
- Exploring the Spatial Distribution of Air Pollutants and COVID-19 Death Rate: A Case Study for Los Angeles County, California
- Reinforcement Learning Based Optimal Battery Control Under Cycle-based Degradation Cost
- Bayesian Attention Belief Networks
- ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
- Bayesian Attention Modules
- Contextual Dropout: An Efficient Sample-Dependent Dropout Module
- Graph Gamma Process Linear Dynamical Systems
- Implicit Distributional Reinforcement Learning
Dr. Hao Zhu is an Associate Professor and holds the Texas Atomic Energy Research Foundation Centennial Fellowship in Electrical Engineering in the Chandra Family Department of Electrical & Computer Engineering at The University of Texas at Austin. Before that, she has been an Assistant Professor of ECE at the University of Illinois Urbana-Champaign (UIUC) from 2014-2017. Her research interests include energy data analytics and cyber-physical situational awareness for power grids. Dr. Zhu received the NSF CAREER Award in 2017, the Siebel Energy Institute Seed Grant in 2016.