Graduate Research Project

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

This project measures sidewalk, curb ramp, and curb cut quality data to evaluate pedestrian infrastructure quality. The project considers accessible, active travel for people with physical disabilities.

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.

This project will aim to develop a data-driven framework to analyze the disturbance amplification behavior of automated vehicles in car-following (CF).

This project analyzes the prospects of hydrogen as a clean energy carrier for the transportation sector.

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.

Using Atlanta’s MARTA rail system as a case study, this thesis will assess the feasibility of integrating autonomous transit vehicles (transit AVs) into the public transportation system as a first-mile and last-mile solution for riders.

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 project will aim to develop a low-cost and easily extendable data-driven framework for crash modification factor predictions.