This research will develop a novel application of LCA and life cycle cost effectiveness that considers the changes in local pollutants across the various lifecycle phases of GHG reduction strategies. These results are further examined to determine environmental equity implications.
This research advances the ongoing development of a real-time, data-driven, transportation simulation tool for a connected infrastructure environment, capable of estimating two environmental performance measures: energy consumption and CO2emissions.
This project is aimed to develop a deep reinforcement learning based smart charging technology for multiple chargers at the University of California, Riverside.
The goal of this research is to understand vehicle interactions in mixed traffic and leverage the understanding to shape AV behaviors, simultaneously benefiting all road users (i.e., ensuring equity) and achieving emission reduction, even with a limited proportion of AVs.
This dissertation studies transportation-related barriers and spatial accessibility regarding national park tourism, focusing on differences between racial/ethnic groups.
This study will develop a framework that integrates an activity-based travel demand model with path retention, an emissions model (MOVES Matrix) and demographic analysis system (Population Synthesis).
This research provides insights to policymakers and academics on how to properly allocate electric vehicle charging infrastructure and manage charging activities.
