University of California, Riverside

The goal of this project is to develop a connected automation-enabled cooperative management framework for mixed traffic.

This project adds secondary particulate matter data to the MOtor Vehicle Emission Simulator to better measure air quality and pollution exposure.

This study develops an energy management for plug-in hybrid vehicles.

This project measures NO2 and particulate matter (PM 2.5) concentrations in Riverside, California. There is special focus on the spatial and temporal variability of the pollutants in relation to major urban roadways.

This project creates a tool that takes into account cyclists' exposure to air pollution when planning new bicycle routes.

This project bridges the gap between predicted and measured near-road PM2.5 from vehicle emissions, by creating and evaluating the effectiveness of two correction modules.

This project investigates the water-uptake of aerosols from gasoline direct injection (GDI) vehicles using a mobile environmental chamber that has been designed and constructed to characterize secondary emissions, or emissions that have undergone atmospheric transformations.

The objectives of the research project are to: 1) Combine cooperativity and the EAD to reduce the negative effect of the increasing penetration rate and 2) conduct field tests in existing connected vehicle testbeds and extend the scope to corridors or networks and study the energy optimization approach for multiple intersections.

This project develops an algorithm to address technological research gaps in eco-driving.

The goal of this project is to design and evaluate a Dynamic Mobility Management System (DyMMS) to address traffic congestion, which leads to more fuel consumption, significant greenhouse gas (GHG) emissions, and poor air quality.