This project proposes using intelligent transportation system (ITS) technologies that take into account the presence of trucks in the traffic flow. The researchers anticipate that this will improve impact on the environment by reducing fuel consumption and pollution levels in areas where the truck volume is relatively high.
This white paper applies life cycle assessment (LCA) modeling to measure life cycle greenhouse gas (GHG) emissions. There is special focus on strategies that lead to GHG reductions from the on-road transportation sector.
The purpose of this project is to address the issue of robustness in the design of variable speed limit and bring them closer to successful implementation with consistent and well-understood benefits.
In this study, the researchers will use optimization and simulation modeling to explore the impacts of using battery electric heavy-duty trucks (BEHDTs) in freight operations (e.g., fleet size) and emissions, taking into account differences in performance and refueling.
This research proposes to couple a system dynamics and material flow analysis, with a multi-region life cycle assessment model to assess the impacts of second-hand electric vehicle exports between the US and Mexico.
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).
The research team will create a quantitative spatial equilibrium model of on-site and remote worker location choice and transport demand in the contiguous United States in order to examine how the distribution of jobs and residents within and across U.S. cities would change if the 2020 surge in working from home becomes permanent, and what the effect on demand for commuting and freight transport would be.
The purpose of this research is to provide a ride-sharing planning scheme that will consider all three sources of uncertainties to provide a robust travel plan while at the same time reducing travel time for the commuters.
