Featured Project: CTfastrak Data Repository & Analysis
The CTDOT is launching the first American pilot of full-sized, automated buses. The three connected, automated, electric buses will be deployed on the CTfastrak as part of a project supported by $2M in FTA funds through the Integrated Mobility Innovation program. We are partnering with CTDOT on this project, along with other industry and non-profit partners.
Interpretable Mobility-on-Demand Prediction & Fleet Coordination
Mobility-on-demand (MOD) services have been redefining personal mobility over the last decade by promoting responsive and accessible multimodal transportation, with the potential to improve transportation efficiency. Focusing on ride-sharing services, his study aims to (1) design interpretable data-driven prediction models for demand forecasting; and (2) devise efficient operational strategies for the coordination of a city-scale ridesharing vehicle fleet to best satisfy that demand.
Commercial Vehicle Parking Information Management System
This project, funded by the Federal Motor Carrier Safety Administration (FMCSA), in collaboration with the CT DMV, focuses on developing a truck parking information management system (TPIMS) for the state of Connecticut. A major goal of the TPIMS is to develop a methodology to evaluate parking capacity and usage in real-time and pilot a technology-based application that can be used to disseminate real-time information regarding the location and space availability of commercial motor vehicle (CMV) parking.
Using Connected Vehicle Data for Vehicle Dynamics Analysis
Connected vehicle (CV) data uniquely allows insights into both driving behavior and traffic operations. Using observations across various time periods in Hartford County, this project focuses on data-driven studies to analyze the driving patterns and resulting traffic dynamics under different traffic conditions and control strategies. The analysis will further test the applicability of data fusion approaches to combine CV data with aggregate traffic data to accurately capture, model, and predict traffic conditions.