The objective of this work is to develop a last-mile delivery optimization model based on Continuous Approximation (CA) techniques and cost-based sustainability assessments, capable of testing various last-mile strategies.
This project will look at the geospatial modeling of electric vehicles supply equipment (EVSE). This project aims to develop a set of up-to-date geospatial models for future fuel infrastructure transition in California.
The objective of this project will be to analyze and explore the current literature available on resilient transportation, especially with regards to climate change and extreme weather events.
The goal of this study intends to understand the relative share of vehicle miles traveled (VMT) between a plug-in hybrid vehicle and a battery electric vehicle.
This research aims to develop an Artificial Neural Network (ANN) to forecast EVs’ trip destinations and charging behavior–information that is essential for electricity load aggregators to effectively manage charging loads.
This research is focused on the optimization of transportation system management by developing statistical models involving dynamic network modeling, spatiotemporal analysis, and theories of AI.
