Research Areas

Performance Measurement

11/5/18
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.”
11/5/18
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
1/12/18
Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
12/4/17
This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.
11/2/17
Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.
11/2/17
Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.
11/5/18
Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.
11/2/17
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.
11/2/17
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
11/17/17
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.
12/1/17
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.
11/20/17
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.