Analyzing traffic violations and traffic crash records to develop a probabilistic model that will help detect high risk drivers with the main goal of preventing future crashes.
Harnessing the power of big data to address transportation challenges.
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.
Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.
Understanding and expressing public transit system utilization based on fundamental travel behavior.
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.
Creating a mobile computer application for documenting and sharing data regarding vehicular accidents.
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.
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.”
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.
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.
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.
