The 2019 3 Revolutions Future Mobility Research Workshop brought together a group of academics, planning agency members, policy makers, and private sector representatives for a series of discussions, poster sessions, presentations, and keynote talks.
This brief summarizes research findings from the University of California, Riverside, on a prediction-based, adaptive connected eco-driving strategy to account for real-world uncertainties.
The objectives of the research project are to: 1) Combine cooperativity and the EAD to reduce the negative effect of the increasing penetration rate and 2) conduct field tests in existing connected vehicle testbeds and extend the scope to corridors or networks and study the energy optimization approach for multiple intersections.
This dataset includes model results on trips, VMT, and emissions for California, generated as the outcome of the research project, "Emissions Impact of Connected and Automated Vehicle Deployment in California".
This data supports the project and report, "Exploring the Role of Attitude in the Acceptance of Self-driving Shuttles." The information collected in this study was categorized into two general groups, individual characteristics and attitude.
This data repository is for the "Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications" project. This project investigated and demonstrated the utility of lane-level map-matching and localization.
