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. 

CIRCLES: Congestion Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing

The CIRCLES Website https://circles-consortium.github.io contains more detailed information on this project. 

Benching Computer Vision Algorithms for Basketball

Collaborative Research: Operator theoretic methods for identification and verification of dynamical systems

Widespread use of automation in many sectors of society has yielded a large amount of data regarding historical behaviors for a variety of dynamical systems, such as unmanned aerial, marine, and ground vehicles, biological systems, and weather systems. This project aims to develop novel algorithms to discover governing rules that explain the observed behaviors (i.e., trajectories) of dynamical systems. Discovery of underlying models, while useful for analysis and control, can be computationally challenging.

Spatio-Temporal AI Inference Engines for System-Level Reliability

High-dimensional Data-driven Energy optimization for Multi-Modal transit Agencies (HD-EMMA)

Transportation accounts for 28% of the total energy use in the United States and as such, it is responsible for immense environmental impact, including urban air pollution and greenhouse gas emissions, and may pose a severe threat to energy security. As we encourage mode shift from personal vehicles to public transit, it is important to consider that public transit systems still require substantial amounts of energy; for example, public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO2 emission annually.

SCC-IRG Track 1: Mobility for all - Harnessing Emerging Transit Solutions for Underserved Communities

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service.

SAFE-Ride -- A regional transit Coalition for Managing Safe and Efficient Transit Operations using AI

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