Harnessing the power of big data to address transportation challenges.

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.”
Exploring historical incident and traffic data to revolutionize response strategies.
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.
Creating a quality-aware crowdsourced road sensing system that integrates sensory data from multiple vehicles while placing more weight on the vehicles that provide high quality data to significantly improve integration accuracy.
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.
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.
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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