TransInfo Research Facilities and Associated Centers
In support of its research, educational and training mission, TransInfo UTC will have access to the extensive and world-class research facilities, equipment and data at its member institutions.
The University at Buffalo is home to several transportation research labs as well as the Center for Computational Research and Stephen Still Institute for Sustainable Transportation and Logistics.
The Department of Civil, Environmental and Infrastructure Engineering at GMU is establishing a lab for driving behavior and safety studies. GMU also houses several transportation-related software including ArcGIS, TransCAD, VISSIM, CORSIM, and HCS.
CUBRC's Center for Transportation Injury Research (CenTIR) was established in 1998 is developing the next generation of emergency response technologies.
Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.
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.
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.
The project suggests a bottom-up travel behavior driven approach which obtains trends in individual travel behavior first and use such information to enhance longitudinal origin-destination demand monitoring.
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.
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.
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.
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.
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.
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.”
This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.
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.
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
