age_moto returns amount of vehicles at each age

age_moto(x, name = "age", a = 0.2, b = 17, agemin = 1,
agemax = 50, k = 1, bystreet = FALSE, net, verbose = FALSE,
namerows)

## Arguments

x Numeric; numerical vector of vehicles with length equal to lines features of road network Character; of vehicle assigned to columns of dataframe Numeric; parameter of survival equation Numeric; parameter of survival equation Integer; age of newest vehicles for that category Integer; age of oldest vehicles for that category Numeric; multiplication factor. If its length is > 1, it must match the length of x Logical; when TRUE it is expecting that 'a' and 'b' are numeric vectors with length equal to x SpatialLinesDataFrame or Spatial Feature of "LINESTRING" Logical; message with average age and total numer of vehicles Any vector to be change row.names. For instance, name of regions or streets.

## Value

dataframe of age distrubution of vehicles

## Note

The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:

1. If you know the distribution of the vehicles by age of use , use: my_age 2. If you know the sales of vehicles, or the registry of new vehicles, use age to apply a survival function. 3. If you know the theoretical shape of the circulating fleet and you can use age_ldv, age_hdv or age_moto. For instance, you dont know the sales or registry of vehicles, but somehow you know the shape of this curve. 4. You can use/merge/transform/adapt any of these functions.

## Examples

{ data(net) MOTO_E25_500 <- age_moto(x = net$ldv, name = "M_E25_500", k = 0.4) plot(MOTO_E25_500) MOTO_E25_500 <- age_moto(x = net$ldv, name = "M_E25_500", k = 0.4, net = net) plot(MOTO_E25_500) }
#> #> Average = 17.12
#> Warning: plotting the first 9 out of 50 attributes; use max.plot = 50 to plot all