Project Grant 2149511
- The Massachusetts Institute of Technology (MIT) received a $800,000 Project Grant from the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support the development of an online learning framework to optimize transportation systems as emerging mobility options like connected and automated vehicles alter travel demand and efficiency. Specifically, MIT...
- This $175,822 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports Cornell University in developing an online learning framework for socially emerging mixed mobility systems. The goal is to create an equitable transportation system that improves safety, efficiency, and traveler acceptance by merging learning and control approaches. The framework will use mechanism design theory and contextual...
- The National Science Foundation (NSF) Engineering program (CFDA 47.041) has awarded a $400,000 project grant to the Massachusetts Institute of Technology (MIT) to accelerate the design of controllers for large-scale engineering systems, focusing on the application of artificial intelligence in transportation. The project aims to bridge the gap between simulated cyber environments and real-world physical operations by utilizing extensive offline datasets and offline reinforcement learning....
- This $388,847 Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) to Trustees of Boston University is focused on developing new methods for managing the complex uncertainties introduced by increasing user participation and responsiveness in critical infrastructure systems such as power grids and transportation networks. The project seeks to integrate classical feedback control, incentive design, online optimization, and stochastic modeling to enable...
- This EAGER (Early-Concept Grants for Exploratory Research) award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $150,000 to conduct research on advancing system designs, models, and algorithms for state-aware demand control to enhance shared use of emerging autonomous mobility systems. The research aims to leverage real-time network data and vehicle/rider trajectories to dynamically shape demand and scale ride-pooling operations, with potential applications...
- 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 $175,000 Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will fund research at Texas Tech University from April 2022 to March 2024 related to developing novel modeling, control, and optimization methods for connected and automated vehicles. The research aims to improve the efficiency and sustainability of urban transportation systems while respecting individual...
- 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 $500,000 project grant, awarded on January 1, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address the urgent need for end-to-end safety in learning-enabled autonomous systems across various application scenarios, such as self-driving cars and urban air mobility. The project, titled "COLLABORATIVE RESEARCH: SLES: GUARANTEED TUBES FOR SAFE LEARNING ACROSS AUTONOMY ARCHITECTURES,"...
- This $547,447 National Science Foundation project grant supports research at Northwestern University toward developing an integrative machine learning approach for traffic management. The three-year award, issued on September 1, 2022 under the Engineering program (CFDA 47.041), will create and test hybrid machine learning methods tailored for transportation domain problems involving hierarchical optimization of ridesharing and traffic flows. The grantee will introduce a novel framework combining...
This National Science Foundation project grant of $799,986 will support research into developing an online learning framework for emerging mobility systems through June 2025. Funded under the Computer and Information Science and Engineering program, the Trustees of Boston University will consolidate real-world transportation data to establish approaches for optimally controlling cyber-physical systems. Researchers will merge learning and control methods to enhance accessibility, safety, and equity in transportation. Specifically, the project aims to distribute travel demand across networks to create a socially optimal mobility system travelers will accept. Mechanism design and contextual multi-armed bandit algorithms will identify traveler preferences and responses to recommendations on routing and modes. The outcome intends to enhance understanding of efficiency impacts like rebound effects from automation and shared mobility adoption on travel behavior and capacity.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 6/30/22 |