The recent network disruptions in the Washington Metro system showed the new reality associated with aging transit infrastructure and highlighted the potential severity of such disruptions. However, relevant studies in the literature are limited and agencies need more empirical evidence to help them better planning and implementing maintenance work.
Harnessing the power of big data to address transportation challenges.
Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.
The customized Lincoln MKZ will help boost the university’s research enterprise in connected and autonomous vehicles.
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.
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.
Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
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.
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.
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.
