Research Areas

Performance Measurement

11/2/17

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.”

11/2/17

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.

11/20/17

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.