Connected Cities with Smart Transportation
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
In this project, the research team built on work done in a Year 1 C2SMART project, in which a decomposition method was developed to address traffic signal optimization. This project aims to develop methods to deal with mixed traffic flow and develop CAV-based signal coordination methods with multiple signalized intersections
This project aims to develop a systematic way to design smart highway systems with networked video monitoring and control resiliency against environment disruptions and sensor failures. The research team will investigate deep learning methods for extracting fine-grained local categorical traffic information from surveillance videos and novel graph neural network methods to correlate and propagate the local information through the highway network for global states estimation, such as vehicle tracking and reidentification or traffic prediction in an unobserved area.
Overview Remote repositioning (RR) technology allows a scooter or similar lightweight, slow-moving vehicle to be controlled by a human driver from a remote location. RR can resolve many mis-parking problems
Collaborative driving results are available for vehicle-level controls and mostly focused on architecture and human-in-the-loop approaches. We aim at a macroscopic and network-level approach to exploit the potential impact of collaborative driving.
Exposure to ambient particulate matter (PM) air pollution is one of the leading risk factors for global disease burden. Preliminary measurements carried out by the PI and colleagues have identified that concentrations of PM2.5 in subway cars frequently reach 100 mg/m2, and concentrations in stations reach 200 mg/m3, almost 6 times EPA standards.
Trucks have been an integral part of the freight movement network in distributing goods and services to various communities; however, a percentage of these trucks are often overloaded beyond legal load limits. A more practical and efficient OW enforcement scheme would be needed to discourage the trucking industry from overloading their fleets.
Cities like NYC and Seattle need to deal with significant growth of urban deliveries as a result of increasing e-commerce compounded by increased stay-at-home behavior due to COVID-19. We propose to develop a citywide model of truck network flows, one that relates changes to truck routes to changes in truck tours or to time-of-day congestion pricing policies.
This research aims to develop an innovative approach for energy harvesting from transportation infrastructures and demonstrate the feasibility of the approach through laboratory testing and field demonstration.