Research Areas

Transportation Operations

11/5/18
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
6/14/19
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.
1/12/18
Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
4/30/19
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.
11/5/18
Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.
11/15/19
Exploring historical incident and traffic data to revolutionize response strategies.
3/12/19
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.
3/12/19
Incorporating data analytics into paratransit planning and operations is a promising approach for increasing their cost-effectiveness.
3/12/19
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.
11/15/19
Recently, there has been an unprecedented interest in Connected and Automated Vehicles (CAVs) or self-driving vehicles. CAVs have the potential to revolutionize transportation, resulting in a major paradigm shift in the way we move and move our goods. The current project is conducted in synergy with another project at UB, funded by New York State Energy and Research Development Authority (NYSERDA) and New York State Department of Transportation (NYSDOT). That project is evaluating the technical feasibility, safety and reliability of using CAV technology, and in particular the self-driving shuttle, Olli, manufactured by Local Motors. 
11/15/19
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.
11/5/18
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.