Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
This project is focused on developing a deep learning based data acquisition and analytics tool using vision-based sensors (i.e., cameras) to understand cities with machine eyes.
The COVID-19 outbreak has dramatically changed travel behavior in cities across the world. With changed travel demand, economic activity, and social-distancing/stay-at-home policies, transportation systems have experienced an unprecedented shift in demand and usage. Since the start of the pandemic, the C2SMART research team has been collecting data and investigating the impact of COVID-19 on mobility and sociability.
An NSF project named “study of driving volatility in connected and cooperative vehicle systems” aims at extracting driving volatility, characterized by hard acceleration/braking, jerky movements, sharp lane changes or turns, and abnormally high speeds in a connected vehicle environment. The objective of this project is to model computationally efficient algorithms for predicting driver actions and volatility using information about their prior behaviors combined with positions and motions obtained via wireless communications.
The NYCDOT Team (NYCDOT and C2SMART) will assist the USDOT in understanding and enabling Cooperative Driving for Advanced Connected Vehicles (CD for ACV) in New York City. In partnership with the USDOT, NYCDOT will identify an area in the CV Pilot site as a location to support Advanced Connected Vehicles (ACVs) to better understand the role of infrastructure in supporting and enabling connected driving in an urban environment.
The primary mission of the Bridge Resource Program (BRP) is to provide ongoing engineering evaluation and research support to the New Jersey Department of Transportation’s (NJDOT) Division of Bridge Engineering and Infrastructure Management. Major Goals of BRP is to (1) Preserve the state’s Bridge and Structural Assets, (2) Optimize the overall condition of the state’s assets within available funding limits and (3) Assist in developing the policy and standard based on new technologies to ensure structures safety and preserve NJDOT’s structures.
C2SMART researchers are working in partnership with Noblis to provide technical and management support for the ITS Deployment Evaluation program by populating and providing analysis of the ITS Benefits, Costs and Lessons Learned/Best Practices for an ITS Deployment Database. Additionally, C2SMART provides technical and program support for the ITS Deployment Tracking Survey and is providing technical support for modal collaboration on Evidence Based Decision Making (EBDM) to accelerate deployment.
The overall scope of the project is to assess use cases where freeway operations strategies could be improved through the transmission of data between a traffic management system (TMS) and the larger cooperative automated transportation (CAT) system (either directly or through a third party). This assessment should (1) spur development of enhanced and new operational strategies and (2) help agencies justify gaining access to additional CAT data.
As part of USDOT’s Connected Vehicle Project, C2SMART researchers at New York University – in collaboration with NYCDOT and industry partners JHK and Harman – recruited volunteer participants with vision disabilities via local and national organizations to help conduct field tests of a phone application, PED-SIG, which could improve mobility of pedestrians with vision disabilities to navigate safely and independently through New York City.
This project has many parts, and the NYU team is currently working with Rutgers on the RE-CAST 2D subproject. This subproject aims to test the bend strength of reinforced concrete that is repaired and strengthened using the four techniques: External Prestressing, Fiber-Reinforced Ferrocement Composite, Fiber-Reinforced Self Consolidating Concrete, and Fiber-Reinforced.
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.