`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.