California has a long history of reducing greenhouse gas (GHG) emissions, and has been working to accelerate the adoption of battery electric heavy-duty trucks (BEHDTs) that have a restricted, load-dependent driving range, which makes charging planning an important role in the use of BEHDTs as an alternative to DHDTs. This research study investigates a mixed fleet drayage routing problem (MFDRP) with non-linear charging times.
The proposed research examines the financial state of the practice for sidewalk asset management in the United States, taking economic, social and legal costs into consideration.
This research identifies pedestrian infrastructure elements as assets and defines construction costs, common issues, repair costs, and maintenance practices.
UC Davis researchers surveyed homeowners in Sacramento and collected lot size and other data to investigate whether the total effective parking supply of the average single-family detached home is sufficient to accommodate the vehicles associated with the residents of both a primary dwelling and a potential ADU.
The goal of this project is to develop and disseminate data and a method that practitioners can use to estimate multimodal trip-generation rates for “smart growth” land use development projects proposed in California.
This research provides insights to policymakers and academics on how to properly allocate electric vehicle charging infrastructure and manage charging activities.
This dissertation research demonstrates the importance of assessing BEV charging infrastructure in an integrated perspective, focusing on key interactions between transportation, energy, and economy across individual patterns of travel behavior, dwelling constraints, pricing elasticity of consumers with regards to charging, and the temporal and spatial diversity in price and GHG intensity of electricity through three studies.
This policy brief summarizes interviews with stakeholders from 38 agencies and organizations throughout California on how they view the possible impacts of ridehailing and the policies that might best address those impacts.
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.
A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties.
