Page 14 - Annual Report - interactive demo

Research
New & Ongoing Projects
Connected & Autonomous Mobility
Automated Truck Lanes in Urban Area for Through and Cross Border Traffic
Principal Investigator: Kelvin Cheu, UTEP
Objective: Recommend infrastructure design and demonstrate, by means of microscopic traffic simulation, the concept of operations of automated truck lanes in highways in urban areas.
Research and Field-Testing of Vehicle-Traffic Control with Limited-Capacity Connected/Autonomous Vehicles
Principal Investigator: Jeff Ban, UW
Objective: Extend and field-test CAV-based traffic signal/vehicle control methods developed by the re- search team in previous projects to understand and quantify the benefits of CAV-based control in the real world.
Integrative Vehicle Infrastructure Traffic System (iVITS) Control in Connected Cities
Principal Investigator: Camille Kamga, CCNY
Objective: Develop network-wide iVITS for New York City by leveraging team members’ current research on local and regional iVITS models and algorithms and develop a simulation-based approach for the evalua- tion of CV applications.
Big Data
Understanding Mobility Patterns and Decision-making Using an Integrated, Multi-modal Sensing Platform in a Quantified Community
Principal Investigator: Constantine Kontokosta, NYU
Objective: Develop new models of pedestrian mobility using WiFi probe data as a novel data source, and combine this with physical, social, and environmental data to understand the impact of various factors on mobility patterns and behavior in urban environments.
Connected Vehicles for Municipal Vehicular Fleets
Principal Investigator: Camille Kamga, CCNY
Objective: Develop new models of pedestrian mobility using WiFi probe data as a novel data source, and combine this with physical, social, and environmental data to understand the impact of various factors on mobility patterns and behavior in urban environments.
Design of Resilient Smart Highway Systems with Data-Driven Monitoring from Networked Cameras
Principal Investigator: Li Jin, NYU
Objective: Develop a systematic way to design smart highway systems with networked video monitoring and control resiliency against environmental disruptions and sensor failures; 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 net- work for global states estimation.
13 C2SMART Center Annual Report