Blockchain for Preserving Privacy in V2X Connected Vehicle Applications in Urban Environments

C2SMART researchers developed a more efficient, secure, blockchain-based system to story mobility data on a distribute ledger. To store this data at scale, researchers leverage InterPlanetary File System (OPFS), a scalable distributed peer-to-peer data storage system, and develop efficient consensus algorithms to prevent users from injecting malicious or fake trajectories into the ledger.

A Cost-effective Approach Towards Building a Traffic Sign Data Inventory Using Open Street Images

Traffic signs are critical assets for roadway and infrastructure management. They are also in a great variety and different conditions. According to the asset management plan proposed by US DOT, the research team proposes a cost-effective approach to build a traffic sign data inventory using open street images.

Work Zone Safety: Virtual reality-based traffic co-simulation platform for workforce training and pedestrian behavior analysis

Building off of the research team’s previous work on a smartwatch alarm application and worker attention monitoring system, this project will expand the scope to a) understand workers’ behaviors to modalities of alarms in real physical work environments, and b) improve the VR based traffic co-simulation platform to co-simulate workers position in SUMO in real time as obstacles to be recognized and calibrate the vehicle trajectories in SUMO through larger work zone/traffic vehicle trajectory datasets.

One-to-Many Simulator Interface with Virtual Test Bed for Equitable Tech Transfer

A broad API will be developed to handle interfacing any simulation with a multi-agent demand simulator. This will be tested on the existing MATSim-NYC (which will be enhanced to include freight and parcel delivery activities) and aBEAM implementation, BEAM-NYC, for three use cases in electric transit, freight, and traffic.

Developing a Framework to Optimize FloodNet Sensor Deployments Around NYC for Equitable and Impact-based Hyper-local Street-level Flood Monitoring and Data Collection

The project will build a framework to optimize and prioritize locations for FloodNet sensor deployment, for measurement of hyper – local flooding in New York City (NYC).