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.
In this project, the researcher aims to create an agent-based cooperative vehicle-infrastructure system (CVIS) framework, and develop modeling and control approaches in a connected and automat
This project develops a Markov Decision Process (MDP) based approach to account for uncertain traffic conditions while developing eco-driving technology.
This project will address the dispatching problem of a generalized battery-electric truck fleet, in the form of an (electric) vehicle routing problem with pick-up and delivery windows.
Principal InvestigatorLuis Fernando Enriquez-Contreras
University of California, Riverside
This project is focused on developing a free and open-source Python library for integrating various microgrid components and electric vehicle charging infrastructure.
This report proposes a machine-learning-based sidewalk digitization method, taking full advantage of roadway networks to reconstruct an initialized (connected) sidewalk network.
Researchers will equip the City of Riverside's Innovation Corridor with roadside communications equipment and advanced traffic signal controllers at several key intersections, to help improve safety, mobility and environmental sustainability.
This project creates a sensor to measure the fuel quality of natural gas, with the intention of advancing renewable natural gas as a vehicular fuel source.
This white paper examines truck eco-driving programs from Europe, Asia, and North America. It discusses challenges and solutions for implementing educational eco-driving programs.
