This project proposes a next-generation safe, efficient and reliable traffic signal control framework powered by advanced roadside sensing and foundation models, specifically Visual Language Models and Multi-Modal Large Language Models.
Researchers developed a variety of eco-driving technologies for trucks. They then tested how effective these technologies were in reducing emissions and fuel consumption.
The researchers used mobile crowd-sourced data to create an effective tool that evaluates the environmental impact of traffic congestion in an accurate, timely and economic way.
The research team will utilize the before/after “Smart Pedal” data to conduct a cost/benefit analysis of the top 2% of Caltrans Fleet vehicles which would benefit from “Smart Pedal” technology installation.
This project will investigate and evaluate the activity patterns and energy use of battery electric vehicles (BEV) and equipment that directly work in goods movement or work in the logistics relate
This project focuses on safety, mobility, and environmental sustainability Measures of Effectiveness (MOEs) of Connected and Automated Vehicle (CAV) applications.
This project focuses on Connected and/or Automated Vehicle (CAV) applications. The researchers studied different types of CAV applications in terms of measures of effectiveness.
This project aims to understand the secondary pollutants that form when vehicle exhaust reacts with atmospheric oxidants. This project encompasses various studies related to the main objective.
