Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
project posts that is managed in CIP
The main deliverable for this project was a smartphone navigation app that addresses the specific mobility needs and priorities of seniors, improving their ability to travel around their cities. After the prototype was developed, the researchers recruited seniors to test the app for a few weeks, and then gathered their feedback.
This multi-disciplinary project will combine the results of engineering modeling in the area of transportation infrastructure deterioration related to overweight trucks in New Jersey with economic approaches to estimate the contribution of these vehicles to maintenance costs.
This project will investigate technologies to screen overweight trucks including a high-speed weigh-in-motion (HS-WIM) system integrated with a license plate reader and/or security camera, and to evaluate the feasibility of such technologies compared to current screening practices at weighing stations.=.
This project aims to develop a student parking lot zoning and zone permit pricing model, and then integrate the total demand model, “base price” model, and the zoning model into a software tool named Sparkman, which can be used by all university parking offices.
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.
Through wearable sensors and realistic representations of work zones in virtual reality, we plan to collect worker behavioral and physiological (heart rate) responses to warnings issued under various realistic scenarios and various warning mechanisms.
This project aims to develop a comprehensive holistic model of urban transportation demand given multiple available modes, including for-hire vehicles and their shared options. The model will enable assessment of the impact of shared mobility on urban transportation mode choice, which can be further translated into economic, social, and environmental impacts.
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.
These new sensors are focused on obtaining data about the bicyclist’s behavior, which will complement the current data and contribute to new findings. An iOS platform is also being developed to track the devices and visualize real-time data collected with them. More units are expected to be implemented into the device in the near future for crowdsourcing.