Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers.
The high number of camera views and the quality of the camera views are unique for a large scale Naturalistic driving study.
The objective of this study was to quantify the time to collision (TTC) that drivers applied the brakes during car following scenarios from a large scale naturalistic driving study (NDS).
It is assumed that farm and farmer characteristics serve as mediators of large scale or macro driving forces of change in the present case, a changing farming context including demands for a more environmentally friendly farming practise and a reduced output.
Stars represent the possible large-scale states driving the downscaling simulations.
Fig. 2 Schematic diagram of the large-scale state driving the downscaling simulations.
For future regional projections by DS, GCM results are utilized to obtain the large-scale state driving the DS.
Therefore, there is a need to explore a wider region than that covered by GCM simulations for the large-scale state driving the DS.
In this study, we provided a comprehensive view of the previous downscaling methods based on reconstruction of the large-scale state driving DS from independent decomposed components, such as climatology and perturbation.
The large-scale state driving the DS are denoted by G p, G f, and G p +{G f,G p } for the Present-DDS, Future-DDS, and ATC-Pseudo-Clim-DS runs, respectively, where the states for the present and future climates in the GCM experiment are denoted by G p and G f, respectively.
We also assess human land use changes and long-term weather (i.e., precipitation) to better understand the large-scale factors driving the observed changes over time.
