Dissertation / Thesis

In this dissertation, a cooperative framework for mixed traffic is proposed at both macroscopic and microscopic levels.

This study develops an analytical framework to automatically generate optimal plans for fleet operations and conversion based on fleet-specific characteristics. The goal of the framework is to allow agencies to minimize the fleet-wide energy use, life-cycle CO2 emissions, and/or economic costs when transitioning to alternative fuels.

In this dissertation, advanced eco-trajectory planning algorithms have been developed to address four challenges.

This study explores how has EV adoption increased over the past years and predicts how will the EV market continue to expand and penetrate in the future years using a case study with regional granular travel survey data.

The purpose of this thesis is to analyze the potential effect that pedestrian infrastructure ADA compliance issues may have on persons with disabilities or physical limitations.

The goal of this dissertation is to analyze applications through the lens of circular economy in California based on life cycle assessment (LCA) and techno-economic analysis (TEA).

As e-retailers compete with increasingly consumer-focused service, urban freight witnesses a significant increase in associated distribution costs and negative externalities including greenhouse gas emissions advancing global climate change, as well as criteria pollutant emissions worsening local air quality and thus affecting those living close to logistics clusters. Thus, the author considers the potential of e-commerce to render economically viable, environmentally efficient, and socially equitable urban goods flow, which is pertinent to understand the opportunities and challenges associated with urban freight in light of the increasingly consumer-focused e-commerce distribution.

This research assesses a scenario where transit agencies integrate automated vehicles into their existing systems as a first-and-last mile solution for riders to model whether there would be travel time, cost savings, and other impacts to riders.

This dissertation introduces a novel online prediction algorithm to forecast solar generation and EV charging power and two real-world microgrid applications equipped with solar generation and battery energy storage systems.

This dissertation focuses on observed behavior of current PEV users. Three studies are presented that seek to improve our understanding of PEV driving and charging typified by two levels of disaggregation- vehicle level and household level.