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.
Harnessing the power of big data to address transportation challenges.
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.
Developing models that will predict the delay a passenger car or a truck is likely to encounter by the time the vehicle arrives at the border.
Creating a mobile computer application for documenting and sharing data regarding vehicular accidents.
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.
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.
Examining in-vehicle and infrastructure-based technologies to assess how they might impact emergency responders, particularly EMS.
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.
Upcoming Events
This symposium includes informative lectures, discussions, and enlightening activities, and will showcase not only the experience and innovative thinking of our speakers and guests, but TransInfo's ability to present Transportation Informatics in a way that is both stimulating and relevant.
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.
