Creating a quality-aware crowdsourced road sensing system that integrates sensory data from multiple vehicles while placing more weight on the vehicles that provide high quality data to significantly improve integration accuracy.

Developing models that will predict the delay a passenger car or a truck is likely to encounter by the time the vehicle arrives at the border.

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

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.”

Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.

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

The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.

Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.

Exploring historical incident and traffic data to revolutionize response strategies.

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