Increasing Work Zone Safety: Worker behavioral analysis with integration of wearable sensors and virtual reality

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.

Impact of Ride-Sharing in New York City

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.

Developing Secure Strategies for Vehicular Ad Hoc Networks in Connected and Autonomous Vehicles

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.

Development of Autonomous Enforcement Approach using Advanced Weigh-In-Motion (A-WIM) System to Minimize Impact of Overweight Trucks on Infrastructure

In this study, the team investigated the effect of overweight trucks on the pavement and bridge damage from a national perspective to develop the most efficient enforcement approach to minimize infrastructure damage. The enforcement approach will include the continuation of the development of the A-WIM system and expanding its deployment.

Statewide Mobility Services Program Strategic Procurement Planning

The current Statewide Active Transportation Demand Management (ATDM) program is nearing the end of its contract period. This assignment will assist NYSDOT in setting a strategic direction and executing a procurement strategy for a new Statewide Mobility Services Program, building on the strengths of the evolving ATDM Program while leveraging the opportunities now available in the private marketplace.

Design of Resilient Smart Highway Systems with Data-Driven Monitoring from Networked Cameras

This project aims to develop a systematic way to design smart highway systems with networked video monitoring and control resiliency against environment disruptions and sensor failures. The research team will 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 network for global states estimation, such as vehicle tracking and reidentification or traffic prediction in an unobserved area.