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.
The main objective of this study is the assessment of the Construction Impact Analysis (CIA) and Work Zone Impact and Strategy Estimator (WISE) tools, and determination of the feasibility of their customization with respect to New York City Department of Transportation (NYCDOT) and New York State Department of Transportation (NYSDOT)’s needs and requirements, cost of adoption and modification, and related issues.
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.
The research team will first establish a test bed for the development of the advanced WIM (A-WIM) system by collaborating with local transportation agencies for the selection of the test bed site near a static weighing station. Then, it will develop a set of calibration procedures to guarantee that the level of accuracy is reached and preserved over time. These procedures will include, but are not limited to, the effect of temperature, humidity, and pavement type.
The main objective of this study is the assessment of the Construction Impact Analysis (CIA) and Work Zone Impact and Strategy Estimator (WISE) tools, and determining the feasibility of their customization with respect to New York City Department of Transportation (NYCDOT) and New York State Department of Transportation (NYSDOT)’s needs and requirements, cost of adoption and modification, and related issues.
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.