safety

This paper presents a data-driven framework to analyze the disturbance amplification behavior of automated vehicles in car-following.

The data and materials presented here support a research project conducted by UC Davis in support of Caltrans, a transportation agency that is conducting a wildfire vulnerability risk assessment for fuels reduction in the right of Way (ROW) to protect Caltrans’ infrastructure and travelers.

This tabular data can be used to evaluate perceived bicycling comfort on distinct state highways in California.

These are the Python scripts used in the research project titled, "BikewaySim Technology Transfer: City of Atlanta, Georgia". Note that these scripts may not be the latest version.

This is the dataset for project: "Improving Public Safety Through Spatial Synthesis, Mapping, Modeling, and Performance Analysis of Emergency Evacuation Routes in California Localities".

This is the data for the project "Advancing Active Transportation Project Evaluation". This data set contains organized data created based on data from the City of Santa Cruz, the City of Santa Barbara, and other public records.

This is the dataset associated with the report "Simulating Bike-Transit Trips Using BikewaySim and TransitSim."

This data are output from PTV VISSIM via application programming interfaces (APIs). The files are in .csv format. The contents of each file include vehicle ID, vehicle speed (in mph), MOVES estimate of fuel consumption (in grams), and CMEM estimate of fuel consumption (in grams) on the basis of one simulation time step (1 Hz).