VKT_table: VKT estimation to a complete table
In Iavalos/VKT: An implementation of the VKT in a region.

Description Usage Arguments Details Value Author(s) Examples

View source: R/VKT_table.R

This function estimates the VKT from a specific area using data included in a table.

1	VKT_table(unique.ID, location.ID, df.base, df.assumptions, mode, purpose)

`unique.ID`	Is the identifier of every Minimal Mapping Unit (numeric value).
`location.ID`	Is the ID of every urban category (I.e. distinction between urban and rural, or districts). This should be in numeric value.
`df.base`	Is the table reference that contains the unique ID and location ID columns as well as population and employment information.
`df.assumptions`	Is the table reference with all the assumption values.
`mode`	Is the travel modal where we want to estimate the VKT, this can be a car or public transport. The modal selected should be included in the assumptions table.
`Purpose`	The purpose of the trip where we want to estimate the VKTs, this could be work or school.
`policy`	Is the policy that will intervene in the final VKTs for each MMU. The elasticity value should be included on the assumptions table, incorporating a new category called scenarios and the name of the policy (i.e. BRT) as a subcategory.
`share`	If policy is not NA, the user has to especify is this policy will impacted the total MMU (100%) or just a proportion of the Unit.
`Npt`	Is the new share of occupancy in a bus or the public transport mode modeled after the incorporation of the policy.

Before running this function, the user has to load the two datasets needed here.

The daily VKTs related to the mode of travel and a trip purpose selected.

Ricardo Ochoa

##----Load data tables need it to run the function ----
data("assumptions_MX")
data("Base_table_weekday_CDMX")

# Estimate the VKTs for the complete dataset of Mexico City for cars, and using school as the purpose of the trip.

new.table = data.frame(matrix(ncol=0,nrow=nrow(base_table_week)))

for (i in 1:nrow(base_table_week)){
  x <- VKT_table(unique.ID = base_table_week[i,1], location.ID = base_table_week[i,2], df.base = base_table_week,
           df.assumptions = assumptions_MX, mode = "Car", purpose = "School", policy= NA, share= NA, Npt= NA)
  new.table <- rbind(new.table,x)
  names(new.table) <- c("VKT")


}
print(new.table)