Harnessing the power of big data to address transportation challenges.

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

Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.
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.”
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
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.
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 smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.
Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
Conventional travel demand and other planning data sources provided very limited coverage on non-motorized modes such as biking and pedestrian. Crowd-sourcing approach has the potential to collect more up-to-date data for these modes with minimal costs and at a continuous basis. However, such data is mostly self-reported and lacks a unified format and standard, which compromises the data quality. More advanced data processing, cleansing, and integration methods are needed to make such data sources useful and valuable. This study investigated a set of biking incidents data collected in the Washington D.C. metropolitan area to explore such potentials.

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.

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