This new research project will help to identify best practices to facilitate truck movements and minimize truck wait time at JFK International Airport for land-side cargo movements through a systems analysis of cargo movement and logistics.
The goal of this proposal is to develop and assess an innovative real-time proactive safety monitoring system based on the trajectory of road users (e.g., cars, pedestrians, and cyclists) collected by video cameras.
This proposal will develop a digital twin for urban mobility, the Mobi-Twin platform, focusing on enabling the microscopic accurate modeling and simulation of Urban Mobility System of Systems with the emerging self-driving grade high-resolution 3D data.
The overall grant will fund a 3-year project that calls on students to develop a business plan built around licensable intellectual property that they will present and pitch to a panel of potential investors as part of the competition. The winning team will receive a monetary award to help them launch their venture.
CAIT has been busy in recent months with projects ranging from building virtual reality modules for workforce training to using drones in innovative ways for highway interchange asset inspection. Check out the latest edition of Transportation Today, our print newsletter, for these stories and much more.
A project from UTC partners at Rowan University is working to develop an AI-based algorithm that will automate the process of identifying landing sites from video data as well as satellite images—increasing safety and efficiency in the NAS.
Traffic congestion costs the US billions each year in productivity as commuters find themselves stuck on the nation’s crowded roadways. As more people use the transportation system, investment in innovative, intelligent technology is needed to ensure that current infrastructure can support the growing need.
A recent paper from Rutgers CAIT validated the Paratransit Skills Assessment and found it can predict an individual's ability to use paratransit services with greater than 96% accuracy.
