TDOT RDS Data Quality Assurance and High-Resolution Content Enhancement

The project focuses on addressing data quality and integrity challenges in the use of roadside radar detector data, the primary source of fixed sensor traffic data in Tennessee. The project aims to standardize sensor configurations, audit data quality, and establish a robust framework for roadside radar data utilization.

Scalable Cyber-Physical Simulation for Automated Cyber Agent Training

Modern cyber-physical systems (CPS) are highly complex systems-of-systems, in which understanding the breadth and severity of cyberattacks is highly challenging. As cyberattacks and defensive operations become increasingly automated, there is a greater need to understand the complexities of interactions between the cyber and physical worlds. A scalable, detailed simulation platform will provide a means of developing and evaluating automated techniques within these complex systems.

Decision Support System for Integrated Corridor Management using Artificial Intelligence

During the past two decades, the Federal Highway Administration (FHWA) has invested heavily in researching, piloting, and demonstrating that Integrated Corridor Management (ICM) strategies and systems are a viable alternative to mitigating congestion when lane expansion is not possible. The vision of ICM is that transportation networks will realize significant improvements in the efficient movement of people and goods through institutional collaboration and aggressive, proactive integration of existing infrastructure along major corridors.

CPS: TTP Option: Medium: Collaborative Research: Smoothing Traffic via Energy-efficient Autonomous Driving (STEAD)

Studies show five of the top 10 most-gridlocked cities in the world are in the United States. Traffic congestion puts undue burden on transportation systems across the United States, raising transportation costs and the energy footprint. Vehicle automation creates an opportunity to reduce traffic and improve efficiency of the transportation infrastructure.

Collaborative Research: CPS: TTP Option: Medium: Coordinating Actors via Learning for Lagrangian Systems (CALLS)

This project will improve the ability to build artificial intelligence algorithms for Cyber-Physical Systems (CPS) that incorporate communications technologies by developing methods of learning from simulation environments. The specific application area is connected and automated vehicles (CAV) that drive strategically to reduce stop-and-go traffic. 

Signatures and Barcodes: Data-driven Understanding of Transportation System Performance during Extreme Events

This project focuses on understanding the effects of extreme events such as natural disasters on urban transportation systems necessary for emergency response and recovery services. Motivated both by continued urbanization and the frequency of extreme weather events, this project will investigate novel methods to quantify infrastructure performance and resilience at city-level scales. Outcomes of the project work will provide data-driven insights relevant to authorities responsible for extreme event mitigation and response.

Managing epidemics by managing mobility

This NSF grant will study the coupling between personal mobility and the spread of infectious disease. Recent experiences with COVID-19 have highlighted the importance of directly modeling transportation flows within epidemiological models and understanding the impacts of complex, local-scale travel patterns and their network effects. In particular, the project will help address questions of the following nature: i) Which communities are most likely to accelerate disease propagation throughout the network? ii) Which recurrent travel patterns are most likely to become disease vectors?

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