Project Grant 2450471
- The National Science Foundation (NSF) awarded a $350,000 Project Grant under the Engineering program (CFDA 47.041) to the University of Washington (UW) for research on developing deep learning (DL)-based methodologies to model traffic dynamics and the behavior of connected and automated vehicles (CAVs). The objective is to transform the study of traffic dynamics from conventional approaches to an automatic, DL-based paradigm. The 3-year project aims to design new DL structures to address data...
- The National Science Foundation awarded a $236,970 project grant under the Engineering program (CFDA 47.041) to Vanderbilt University for research titled "COLLABORATIVE RESEARCH: OPTIMAL SENSOR SELECTION AND ROBUST TRAFFIC DETECTION AND ESTIMATION IN A WORLD OF CONNECTED VEHICLES." The grant period is from October 1, 2021 to July 31, 2023. The project aims to improve traffic flow estimation through optimal sensor selection and robust modeling. Vanderbilt University will collaborate...
- This National Science Foundation (NSF) Engineering (CFDA 47.041) award provides $518,552 to the Regents of the University of Minnesota to conduct research that leverages artificial intelligence and individual vehicle trajectory data to develop novel traffic flow models and control strategies. The 5-year "HARNESSING ARTIFICIAL INTELLIGENCE TO IMPROVE THE EFFICIENCY OF TRANSPORTATION CONTROL INFRASTRUCTURE" project aims to create more nuanced physics-informed traffic models that can...
- This EAGER (Early-Concept Grants for Exploratory Research) project, awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041), will identify gaps in translating traffic control theoretical research into practical traffic controls used in real-world settings. The $300,000 project, running from September 1, 2024 to August 31, 2026, will attempt to discover scientific reasons for the disconnect between academic traffic control research and the legacy theories and models...
- This $600,000 Project Grant was awarded on February 1, 2025 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) federal grant program (CFDA 47.070). The grant funds a collaborative research project between Vanderbilt University and other institutions to accelerate the design of controllers for large-scale cyber-physical systems, with a focus on transportation applications. The key objectives are to bridge the gap between simulated cyber...
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will promote progress in data science to support smart transportation systems. The funding of $317,374 over 3 years, starting on October 1, 2024, will establish a theoretical framework for quantifying the value of data from various sources in a transportation network. The project aims to: 1) quantify how changes in data affect estimates of network performance metrics to identify important data...
- This National Science Foundation Project Grant award of $538,633 supports fundamental research in modeling stochastic traffic flows for smart mobility systems through February 2026. Funded under the Engineering program (CFDA 47.041), the award to the University of Maryland, College Park aims to develop machine learning and traffic flow theory to better estimate and predict mobility patterns. The grantee will fuse classical transportation models with learning techniques using transportation...
- This National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award of $367,616 to the University of California, Davis (UC Davis) will fund research to establish a theoretical framework for evaluating and optimizing data acquisition in transportation networks. The research project aims to quantify the value of data from various sources to help transportation network operators, such as the California Department of Transportation, make more informed decisions about which data to...
- The National Science Foundation (NSF) awarded a Project Grant of $113,069 to the University of Tennessee, operating as Univ Tennessee System Office, under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). This grant aims to develop effective and efficient methods for reconstructing city-scale traffic dynamics using mobile data. Specifically, the project will: Estimate travel times and other macroscopic traffic states (speed, flow, density) from...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $217,276 project grant to The University of North Carolina at Charlotte to support research titled "COLLABORATIVE RESEARCH: LEARNING-BASED SCALABLE PREDICTIVE CONTROL STRATEGIES FOR HETEROGENEOUS TRAFFIC NETWORKS." The two-year project beginning January 1, 2022 falls under the NSF Engineering Program (CFDA #47.041), which aims to improve quality of life and economic strength through...
This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $149,998 to Vanderbilt University to support collaborative research on developing deep learning-based methodologies for discovering the governing equations of traffic dynamics and understanding how connected and automated vehicles (CAVs) behave and interact with other road users. The key objectives of this 3-year project are to transform conventional methods of learning traffic dynamics into an automatic, deep learning-based paradigm. The research aims to develop specialized, effective methods for learning traffic dynamics, especially for traffic flow with CAVs, directly from data. This involves designing new deep learning structures to address data noise and a coordinated learning framework to handle the unique features of traffic dynamics due to diverse vehicle classes and driving behaviors. The research also formulates new metrics and methods to ensure accuracy, parsimony, interpretability, and generalizability of the developed traffic dynamics models with CAVs. The findings will be integrated into courses and provide research opportunities for students, while being broadly shared with transportation agencies, academic communities, and industry to help prepare for the deployment of emerging transportation technologies.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 7/1/25 |