Research Areas

Performance Measurement

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.”
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
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.
Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.
Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.
Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.
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.
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
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.
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.