Award Abstract # 2152258
Science to Policy Education: Activating Knowledge for Sustainable Transportation (SPEAKS)

NSF Org: DGE
Division Of Graduate Education
Recipient: REGENTS OF THE UNIVERSITY OF CALIFORNIA AT RIVERSIDE
Initial Amendment Date: June 24, 2022
Latest Amendment Date: June 24, 2022
Award Number: 2152258
Award Instrument: Standard Grant
Program Manager: Liz Webber
ewebber@nsf.gov
 (703)292-4316
DGE
 Division Of Graduate Education
EDU
 Directorate for STEM Education
Start Date: July 1, 2022
End Date: June 30, 2027 (Estimated)
Total Intended Award Amount: $3,000,000.00
Total Awarded Amount to Date: $3,000,000.00
Funds Obligated to Date: FY 2022 = $3,000,000.00
History of Investigator:
  • Matthew Barth (Principal Investigator)
    barth@cert.ucr.edu
  • Jan Stets (Co-Principal Investigator)
  • Kevin Esterling (Co-Principal Investigator)
  • Amir-Hamed Mohsenian-Rad (Co-Principal Investigator)
  • Susan Hackwood (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-Riverside
200 UNIVERSTY OFC BUILDING
RIVERSIDE
CA  US  92521-0001
(951)827-5535
Sponsor Congressional District: 39
Primary Place of Performance: University of California at Riverside
900 University Avenue
Riverside
CA  US  92501-0001
Primary Place of Performance
Congressional District:
39
Unique Entity Identifier (UEI): MR5QC5FCAVH5
Parent UEI:
NSF Program(s): NSF Research Traineeship (NRT)
Primary Program Source: 04002223DB NSF Education & Human Resource
Program Reference Code(s): 090Z, SMET, 9179, 063Z
Program Element Code(s): 199700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

As society looks increasingly to scientific advances for solutions to major challenges, researchers must engage with the public and policymakers to develop and implement new technologies based on those advances to benefit society. For example, such engagement is essential to address climate change, as people and industries must quickly and equitably embrace renewable energy sources and sustainably powered vehicles and technologies. This NSF Research Traineeship (NRT) project will train STEM graduate students to integrate engineering, social and environmental science, and public policy to conduct research focused on accelerating the integration of renewable energy into the electric grid, taking advantage of renewable hydrogen and different energy storage capabilities. Such integration will ultimately help accelerate the decarbonization of transportation and other sectors. This project will use a novel science-to-policy (S2P) training program to teach students to engage stakeholder groups and understand and incorporate their needs into research programs while communicating the science, and prioritizing attention to diversity, equity, inclusion, and ethics. This project anticipates training eighty (80) Ph.D. students, including thirty (30) funded trainees, from different engineering programs, environmental sciences, sociology, and political science. Funded trainees will intern in legislative offices, government agencies, non-profits, and industry for hands-on career preparation with a tangible product, such as a policy recommendation or report. This project will convene expert practitioners from these areas to teach students and participate in research projects. Project faculty and researchers from engineering, policy, sciences, and humanities will implement an interdisciplinary training program providing Ph.D. students skills and experience necessary to address key scientific challenges to transportation decarbonization and to implement their findings in public policy. Faculty will also work with campus leadership to integrate elements of the S2P training program in campus-wide introductory graduate courses and collaborate with the National Science Policy Network to share this training program nationally. This training program is the first step in transforming STEM graduate education to better prepare students for any career and to translate societally beneficial research into public policy.

Accelerating decarbonization of the transportation sector while continuing to integrate renewables into the electric grid is critical to address climate change. Towards this goal NRT trainees will pursue three key activities. First is to conduct cutting-edge research in integrating the high renewables electric grid with transportation infrastructure, investigating renewable hydrogen as a fuel and energy storage medium. Second is to understand policies around sustainability, air pollution, and renewable energy, learn to communicate science to all audiences, and appreciate the needs of various stakeholders to create equitable solutions for environmental justice. Third is to develop best practices for human interaction with renewable energy technology by designing solutions based on individual, societal, and political needs. Engaging key stakeholder groups during research and development will accelerate the deployment of renewable technologies and foster effective communication and decision-making on scientific topics. This project will prepare trainees to develop and deploy a model for scientific research that is closely connected with societal challenges through meaningful community engagement and responsive programs.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 58)
Abdo, Ahmed and Chen, Hanlin and Zhao, Xuanpeng and Wu, Guoyuan and Feng, Yiheng "Cybersecurity on Connected and Automated Transportation Systems: A Survey" IEEE Transactions on Intelligent Vehicles , v.9 , 2024 https://doi.org/10.1109/TIV.2023.3326736 Citation Details
Ahmadi-Gorjayi, Fatemeh and Mohsenian-Rad, Hamed "Data-Driven Models for Sub-Cycle Dynamic Response of Inverter-Based Resources Using WMU Measurements" IEEE Transactions on Smart Grid , v.14 , 2023 https://doi.org/10.1109/TSG.2023.3280367 Citation Details
Anderson, Osten and Yu, Nanpeng and Hong, Wanshi and Wang, Bin "Impact of flexible and bidirectional charging in medium- and heavy-duty trucks on Californias decarbonization pathway" Applied Energy , v.377 , 2025 https://doi.org/10.1016/j.apenergy.2024.124450 Citation Details
Bai, Zhengwei and Hao, Peng and ShangGuan, Wei and Cai, Baigen and Barth, Matthew J. "Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections" IEEE Transactions on Intelligent Transportation Systems , v.23 , 2022 https://doi.org/10.1109/TITS.2022.3145798 Citation Details
Bai, Zhengwei and Nayak, Saswat P and Zhao, Xuanpeng and Wu, Guoyuan and Barth, Matthew J and Qi, Xuewei and Liu, Yongkang and Sisbot, Emrah Akin and Oguchi, Kentaro "Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR" IEEE Transactions on Intelligent Transportation Systems , v.24 , 2023 https://doi.org/10.1109/TITS.2023.3268281 Citation Details
Bai, Zhengwei and Wu, Guoyuan and Barth, Matthew J and Liu, Yongkang and Akin_Sisbot, Emrah and Oguchi, Kentaro and Huang, Zhitong "A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation" IEEE Transactions on Intelligent Transportation Systems , v.25 , 2024 https://doi.org/10.1109/TITS.2024.3436012 Citation Details
Bai, Zhengwei and Wu, Guoyuan and Barth, Matthew J and Liu, Yongkang and Sisbot, Emrah Akin and Oguchi, Kentaro "Dynamic Feature Sharing for Cooperative Perception from Point Clouds" , 2023 https://doi.org/10.1109/ITSC57777.2023.10422242 Citation Details
Bai, Zhengwei and Wu, Guoyuan and Barth, Matthew J and Liu, Yongkang and Sisbot, Emrah Akin and Oguchi, Kentaro "VINet: Lightweight, scalable, and heterogeneous cooperative perception for 3D object detection" Mechanical Systems and Signal Processing , v.204 , 2023 https://doi.org/10.1016/j.ymssp.2023.110723 Citation Details
Bai, Zhengwei and Wu, Guoyuan and Barth, Matthew J and Qiu, Hang and Liu, Yongkang and Sisbot, Emrah Akin and Oguchi, Kentaro "Pillar Attention Encoder for Adaptive Cooperative Perception" IEEE Internet of Things Journal , 2024 https://doi.org/10.1109/JIOT.2024.3390552 Citation Details
Barth, Matthew "Co-Benefits and Tradeoffs Between Safety, Mobility, and Environmental Impacts for Connected and Automated Vehicles" IEEE Transactions on Intelligent Transportation Systems , v.25 , 2024 https://doi.org/10.1109/TITS.2024.3376869 Citation Details
Chen, L-W Antony and Wang, Xiaoliang and Lopez, Brenda and Wu, Guoyuan and Ho, Steven_Sai Hang and Chow, Judith C and Watson, John G and Yao, Qi and Yoon, Seungju and Jung, Heejung "Contributions of non-tailpipe emissions to near-road PM2.5 and PM10: A chemical mass balance study" Environmental Pollution , v.335 , 2023 https://doi.org/10.1016/j.envpol.2023.122283 Citation Details
(Showing: 1 - 10 of 58)

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