age_moto | R Documentation |
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,
time
)
x |
Numeric; numerical vector of vehicles with length equal to lines features of road network |
name |
Character; of vehicle assigned to columns of dataframe |
a |
Numeric; parameter of survival equation |
b |
Numeric; parameter of survival equation |
agemin |
Integer; age of newest vehicles for that category |
agemax |
Integer; age of oldest vehicles for that category |
k |
Numeric; multiplication factor. If its length is > 1, it must match the length of x |
bystreet |
Logical; when TRUE it is expecting that 'a' and 'b' are numeric vectors with length equal to x |
net |
SpatialLinesDataFrame or Spatial Feature of "LINESTRING" |
verbose |
Logical; message with average age and total numer of vehicles |
namerows |
Any vector to be change row.names. For instance, name of regions or streets. |
time |
Character to be the time units as denominator, eg "1/h" |
dataframe of age distrubution of vehicles
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.
Other age:
age_hdv()
,
age_ldv()
,
age()
## Not run:
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)
## End(Not run)
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