Incorporating data analytics into paratransit planning and operations is a promising approach for increasing their cost-effectiveness.

Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.

Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.

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.

This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.

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 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 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.