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

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.

Incorporating data analytics into paratransit planning and operations is a promising approach for increasing their cost-effectiveness.

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.

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

Exploring historical incident and traffic data to revolutionize response strategies.

Developing a novel green navigation system, called Green Nav, that gives a driver the most fuel efficient route for his vehicle as opposed to the shortest or fastest route.

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

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.

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