The DataCity Smart Mobility Testing Ground is a 2.4-mile multi-modal corridor "living laboratory" in downtown New Brunswick, NJ, for collecting multi-modal smart-mobility data that will help the region improve safety, congestion, and equity in its transportation systems, while also establishing NJ as a hub for CAV R&D.
This pilot project will identify and catalog highly localized career opportunities within transportation and the City of Camden NJ. The primary goal is to gather and document this information to begin creating future entry points into the transportation workforce for youth in Camden.
This course will review basic concepts in probability and statistics and their application in designing traffic control features and regulating traffic.
This course provides participants with (1) a basic understanding of intersection safety issues, (2) “how to” information for common safety tasks and low cost safety improvements that do not require an engineered design, and (3) background information on safety tasks that do not require an engineer.
University at Buffalo engineers, and CAIT UTC partners, Dr. Negar Elhami-Khorasani and Dr. Anthony Tessari are looking at how to improve fire safety in tunnels.
Inaccurate network pavement data can impact pavement management decisions such as roadway repairs and more. CAIT researchers at the Rutgers Asphalt Pavement Lab are working with NJDOT to test new pavement inspection equipment and locations to ensure data is accurate and representative of roads in New Jersey.
Ahead of National Work Zone Awareness Week, Rutgers Center for Advanced Infrastructure and Transportation will host the annual New Jersey Work Zone Safety Conference on Thursday, April 7th, from 8:30 am to 1 pm.
The primary goal of this proposal is to assist NJ TRANSIT’S Bus Service Planning department to create a complete roster of the 500 bus capacity Northern bus garage determining stats such as platform hours and non-revenue mileage totals for potential auditing purposes.
On August 30th, Rutgers CAIT hosted UTC partners from Rowan University to discuss their research Evaluating the Mobility Impacts of American Dream Complex and Developing Innovative Intersection Safety Tools, as part of a presentation during the CAIT Seminar Series.
This proposal will develop machine-learning algorithms using real-time vehicle, pedestrian, and infrastructure data to improve our understanding of how people drive on highways and urban roads. These models will help monitor and support the transportation systems to accommodate both human-driven and automated vehicles.
