Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.

Developing the tools needed to process immense amounts of data, develop new performance metrics based on the data collected, and propose methods to enhance performance.

Examining in-vehicle and infrastructure-based technologies to assess how they might impact emergency responders, particularly EMS.

Extending the work that was completed for year one funding related to “Developing Highway Safety Performance Metrics in an Advanced Connected Vehicle Environment Utilizing Near-Crash Events from the SHRP 2 Naturalistic Driving Study.”

Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.

Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.

Mining social media data to deduce useful information about present or future travelers’ behavior, with a special emphasis under events, including both planned and unplanned.

This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.

Analyzing traffic violations and traffic crash records to develop a probabilistic model that will help detect high risk drivers with the main goal of preventing future crashes.

Invistigating aggressive driving behavior under driver fatigue, and under normal and distracted driving conditions.