Research Projects A-Z

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.

11/2/17

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.

11/2/17

Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.

11/20/17

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

Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.

11/2/17

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.

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

Invistigating observed and perceived aggressive driving behavior under driver fatigue, and under normal and distracted driving conditions.

11/20/17

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.

11/10/17

Examining in-vehicle and infrastructure-based technologies to assess how they might impact emergency responders, particularly EMS.

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

Exploring historical incident and traffic data to revolutionize response strategies.

11/2/17

Understanding and expressing public transit system utilization based on fundamental travel behavior.

11/2/17

Creating a mobile computer application for documenting and sharing data regarding vehicular accidents.

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.

11/20/17

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

11/2/17

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.

11/2/17

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.

11/17/17

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.

11/2/17

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.