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

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

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.

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

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.

The project investigates how real-time conditions interact to affect driver safety performance changes. From that understanding, practitioners and drivers can make more informed decisions to reduce the likelihood of a crash.

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.

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

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.”