Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
This project aims to quantify the various safety, environmental and financial benefits of connected vehicle technologies applied to the New York City municipal fleet.
This project aims to extend and field-test CAV-based traffic signal/vehicle control methods developed by the research team in previous projects to understand and quantify the benefits of CAV-based control in the real world.
The NYU Rudin Center for Transportation specializes in policy related to the future of urban mobility. In the Future of Mobility workshops, the NYU Rudin Center will convene subject matter experts and government leaders. During the workshop, speakers and participants will identify policy initiatives and needs for seamless, technology-enabled urban travel.
In previous years, the research team has developed and calibrated a base model implemented in MATSim and SUMO. This virtual testbed simulates an 8-million-person population and includes cars, trains, bus, bikeshare, taxi, and other for-hire vehicles calibrated to the year 2016. The team is building the architecture to host this virtual test bed and developing system design and user guide documentation.
This research will focus on false data injection attacks, in which a malicious agent aims to affect the behavior of vehicles in the network by injecting false information about, for example, the traffic condition in the area or the availability of charging stations.
Researchers at NYU are working with NYCDOT and other partners on this portion of the NYC CV Pilot, as well as on safety performance evaluation of the CV technology deployment.
This research aims to develop modeling and analysis methods to capture the key behaviors and intersections of the major players when integrating ridesourcing with transit.
This project developed real-time distributed network control techniques capable of utilizing various types of real-time traffic data, from both fixed and mobile sources. The work is divided into two major parts: traffic state estimation when data is limited and adaptive control.
Although lots of research have been conducted on multi-vehicle cooperative perception, few studies have considered combining information from vehicle onboard sensors and roadside sensors. This project argues that it would be highly beneficial to utilize roadside sensors for cooperative driving perception.
Autonomous vehicles (AV) and connected vehicles (CV) technology has been much of the focus of transportation industry lately, and they will likely make a vast impact on the future of transportation systems. This project will combine AV and CV technologies for connected and autonomous vehicles (CAVs) to reduce congestion and improve network performance and safety by developing new tools and methods using reinforcement learning and nonlinear and optimal control techniques.