Exploring historical incident and traffic data to revolutionize response strategies.

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

Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.

Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.

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

Integrating machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data.

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.

Creating a mobile computer application for documenting and sharing data regarding vehicular accidents.

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.

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.