Harnessing the power of big data to address transportation challenges.

Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
Examining in-vehicle and infrastructure-based technologies to assess how they might impact emergency responders, particularly EMS.
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.
Incorporating data analytics into paratransit planning and operations is a promising approach for increasing their cost-effectiveness.
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.
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.”
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. 
Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.

We mine a wealth of data by employing a wide variety of methods, tools and models, including those from artificial intelligence (AI), machine learning, statistics, and database systems.  

When these methods are applied to large datasets that have been appropriately compiled and fused together, the result is invaluable and actionable information that can help improve the efficiency, safety, sustainability and resiliency of transportation systems. Our results inform and guide transportation planning, investment decisions, and transportation policies.

Changing The Way The World Works

SSISTL's Benefactor and Professor of Practice, Stephen Still, is featured in UB's We Are Boldy Buffalo Campaign. Stephen gave $4 million to support the Stephen Still Institute for Sustainable Transportation and Logistics. His gift–and many other gifts–are being immediately invested in opportunities for faculty and students to address society’s biggest transportation challenges.

Transportation News

3/31/20

Civil engineering alumnus Stephen Still has donated $10,000 to UB’s student emergency funds, to help students with limited access to food, housing, technology and other critical resources.

9/23/19

The customized Lincoln MKZ will help boost the university’s research enterprise in connected and autonomous vehicles.

12/15/15

Chunming Qiao received the 2015 Distinguished Technical Achievement Award from IEEE’s Communications Society Communications Switching and Routing Technical Committee.