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aeromaps.models.air_transport.air_traffic.rpk

rpk

Module for computing air traffic (RPK) without price elasticity and effect of ad-hoc measures to reduce traffic.

RPK 

RPK(name='rpk', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute traffic (RPK) without price elasticity considering COVID-19 impact and exogenous growth rates by segment.

Parameters:

Name Type Description Default
name str

Name of the model instance ('rpk' by default).

 'rpk'
Source code in aeromaps/models/air_transport/air_traffic/rpk.py 
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def __init__(self, name="rpk", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(rpk_init, short_range_rpk_share_2019, medium_range_rpk_share_2019, long_range_rpk_share_2019, covid_start_year, covid_rpk_drop_start_year, covid_end_year_passenger, covid_end_year_reference_rpk_ratio, cagr_passenger_short_range_reference_periods, cagr_passenger_short_range_reference_periods_values, cagr_passenger_medium_range_reference_periods, cagr_passenger_medium_range_reference_periods_values, cagr_passenger_long_range_reference_periods, cagr_passenger_long_range_reference_periods_values, rpk_short_range_measures_impact, rpk_medium_range_measures_impact, rpk_long_range_measures_impact)

RPK calculation.

Parameters:

Name Type Description Default
rpk_init Series

Historical number of Revenue Passenger Kilometer (RPK) over 2000-2019 [RPK].

 required
short_range_rpk_share_2019 float

Share of RPK from short-range market in 2019 [%].

 required
medium_range_rpk_share_2019 float

Share of RPK from medium-range market in 2019 [%].

 required
long_range_rpk_share_2019 float

Share of RPK from long-range market in 2019 [%].

 required
covid_start_year Number

Covid-19 start year [yr].

 required
covid_rpk_drop_start_year float

Drop in RPK due to Covid-19 for the start year [%].

 required
covid_end_year_passenger Number

Covid-19 end year [yr].

 required
covid_end_year_reference_rpk_ratio float

Percentage of traffic level reached in Covid-19 end year compared with the one in Covid-19 start year [%].

 required
cagr_passenger_short_range_reference_periods list

Reference periods for the CAGR for passenger short-range market [yr].

 required
cagr_passenger_short_range_reference_periods_values list

CAGR for passenger short-range market for the reference periods [%].

 required
cagr_passenger_medium_range_reference_periods list

Reference periods for the CAGR for passenger medium-range market [yr].

 required
cagr_passenger_medium_range_reference_periods_values list

CAGR for passenger medium-range market for the reference periods [%].

 required
cagr_passenger_long_range_reference_periods list

Reference periods for the CAGR for passenger long-range market [yr].

 required
cagr_passenger_long_range_reference_periods_values list

CAGR for passenger long-range market for the reference periods [%].

 required
rpk_short_range_measures_impact Series

Traffic reduction impact of specific measures for passenger short-range market [%].

 required
rpk_medium_range_measures_impact Series

Traffic reduction impact of specific measures for passenger medium-range market [%].

 required
rpk_long_range_measures_impact Series

Traffic reduction impact of specific measures for passenger long-range market [%].

 required

Returns:

Name Type Description
rpk_short_range Series

Number of Revenue Passenger Kilometer (RPK) for passenger short-range market [RPK].
rpk_medium_range Series

Number of Revenue Passenger Kilometer (RPK) for passenger medium-range market [RPK].
rpk_long_range Series

Number of Revenue Passenger Kilometer (RPK) for passenger long-range market [RPK].
rpk Series

Number of Revenue Passenger Kilometer (RPK) for total passenger air transport [RPK].
annual_growth_rate_passenger_short_range Series

Annual growth rate for short-range passengers [%/year].
annual_growth_rate_passenger_medium_range Series

Annual growth rate for medium-range passengers [%/year].
annual_growth_rate_passenger_long_range Series

Annual growth rate for long-range passengers [%/year].
annual_growth_rate_passenger Series

Annual growth rate for total passengers [%/year].
cagr_rpk_short_range float

Air traffic CAGR over prospective_years for passenger short-range market [%].
cagr_rpk_medium_range float

Air traffic CAGR over prospective_years for passenger medium-range market [%].
cagr_rpk_long_range float

Air traffic CAGR over prospective_years for passenger long-range market [%].
cagr_rpk float

Air traffic CAGR over prospective_years for total passenger market [%].
prospective_evolution_rpk_short_range float

Evolution in percentage of Revenue Passenger Kilometer (RPK) for passenger short-range market between prospection_start_year and end_year [%].
prospective_evolution_rpk_medium_range float

Evolution in percentage of Revenue Passenger Kilometer (RPK) for passenger medium-range market between prospection_start_year and end_year [%].
prospective_evolution_rpk_long_range float

Evolution in percentage of Revenue Passenger Kilometer (RPK) for passenger long-range market between prospection_start_year and end_year [%].
prospective_evolution_rpk float

Evolution in percentage of Revenue Passenger Kilometer (RPK) for total passenger market between prospection_start_year and end_year [%].
Source code in aeromaps/models/air_transport/air_traffic/rpk.py 
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def compute(
    self,
    rpk_init: pd.Series,
    short_range_rpk_share_2019: float,
    medium_range_rpk_share_2019: float,
    long_range_rpk_share_2019: float,
    covid_start_year: Number,
    covid_rpk_drop_start_year: float,
    covid_end_year_passenger: Number,
    covid_end_year_reference_rpk_ratio: float,
    cagr_passenger_short_range_reference_periods: list,
    cagr_passenger_short_range_reference_periods_values: list,
    cagr_passenger_medium_range_reference_periods: list,
    cagr_passenger_medium_range_reference_periods_values: list,
    cagr_passenger_long_range_reference_periods: list,
    cagr_passenger_long_range_reference_periods_values: list,
    rpk_short_range_measures_impact: pd.Series,
    rpk_medium_range_measures_impact: pd.Series,
    rpk_long_range_measures_impact: pd.Series,
) -> Tuple[
    pd.Series,
    pd.Series,
    pd.Series,
    pd.Series,
    pd.Series,
    pd.Series,
    pd.Series,
    pd.Series,
    float,
    float,
    float,
    float,
    float,
    float,
    float,
    float,
]:
    """
    RPK calculation.

    Parameters
    ----------
    rpk_init : pd.Series
        Historical number of Revenue Passenger Kilometer (RPK) over 2000-2019 [RPK].
    short_range_rpk_share_2019 : float
        Share of RPK from short-range market in 2019 [%].
    medium_range_rpk_share_2019 : float
        Share of RPK from medium-range market in 2019 [%].
    long_range_rpk_share_2019 : float
        Share of RPK from long-range market in 2019 [%].
    covid_start_year : Number
        Covid-19 start year [yr].
    covid_rpk_drop_start_year : float
        Drop in RPK due to Covid-19 for the start year [%].
    covid_end_year_passenger : Number
        Covid-19 end year [yr].
    covid_end_year_reference_rpk_ratio : float
        Percentage of traffic level reached in Covid-19 end year compared with the one in Covid-19 start year [%].
    cagr_passenger_short_range_reference_periods : list
        Reference periods for the CAGR for passenger short-range market [yr].
    cagr_passenger_short_range_reference_periods_values : list
        CAGR for passenger short-range market for the reference periods [%].
    cagr_passenger_medium_range_reference_periods : list
        Reference periods for the CAGR for passenger medium-range market [yr].
    cagr_passenger_medium_range_reference_periods_values : list
        CAGR for passenger medium-range market for the reference periods [%].
    cagr_passenger_long_range_reference_periods : list
        Reference periods for the CAGR for passenger long-range market [yr].
    cagr_passenger_long_range_reference_periods_values : list
        CAGR for passenger long-range market for the reference periods [%].
    rpk_short_range_measures_impact : pd.Series
        Traffic reduction impact of specific measures for passenger short-range market [%].
    rpk_medium_range_measures_impact : pd.Series
        Traffic reduction impact of specific measures for passenger medium-range market [%].
    rpk_long_range_measures_impact : pd.Series
        Traffic reduction impact of specific measures for passenger long-range market [%].

    Returns
    -------
    rpk_short_range : pd.Series
        Number of Revenue Passenger Kilometer (RPK) for passenger short-range market [RPK].
    rpk_medium_range : pd.Series
        Number of Revenue Passenger Kilometer (RPK) for passenger medium-range market [RPK].
    rpk_long_range : pd.Series
        Number of Revenue Passenger Kilometer (RPK) for passenger long-range market [RPK].
    rpk : pd.Series
        Number of Revenue Passenger Kilometer (RPK) for total passenger air transport [RPK].
    annual_growth_rate_passenger_short_range : pd.Series
        Annual growth rate for short-range passengers [%/year].
    annual_growth_rate_passenger_medium_range : pd.Series
        Annual growth rate for medium-range passengers [%/year].
    annual_growth_rate_passenger_long_range : pd.Series
        Annual growth rate for long-range passengers [%/year].
    annual_growth_rate_passenger : pd.Series
        Annual growth rate for total passengers [%/year].
    cagr_rpk_short_range : float
        Air traffic CAGR over prospective_years for passenger short-range market [%].
    cagr_rpk_medium_range : float
        Air traffic CAGR over prospective_years for passenger medium-range market [%].
    cagr_rpk_long_range : float
        Air traffic CAGR over prospective_years for passenger long-range market [%].
    cagr_rpk : float
        Air traffic CAGR over prospective_years for total passenger market [%].
    prospective_evolution_rpk_short_range : float
        Evolution in percentage of Revenue Passenger Kilometer (RPK) for passenger short-range market between prospection_start_year and end_year [%].
    prospective_evolution_rpk_medium_range : float
        Evolution in percentage of Revenue Passenger Kilometer (RPK) for passenger medium-range market between prospection_start_year and end_year [%].
    prospective_evolution_rpk_long_range : float
        Evolution in percentage of Revenue Passenger Kilometer (RPK) for passenger long-range market between prospection_start_year and end_year [%].
    prospective_evolution_rpk : float
        Evolution in percentage of Revenue Passenger Kilometer (RPK) for total passenger market between prospection_start_year and end_year [%].
    """
    # Initialization based on 2019 share
    for k in range(self.historic_start_year, self.prospection_start_year):
        self.df.loc[k, "rpk_short_range"] = short_range_rpk_share_2019 / 100 * rpk_init.loc[k]
        self.df.loc[k, "rpk_medium_range"] = medium_range_rpk_share_2019 / 100 * rpk_init.loc[k]
        self.df.loc[k, "rpk_long_range"] = long_range_rpk_share_2019 / 100 * rpk_init.loc[k]

    # Covid functions
    reference_years = [covid_start_year, covid_end_year_passenger]
    reference_values_covid = [
        1 - covid_rpk_drop_start_year / 100,
        covid_end_year_reference_rpk_ratio / 100,
    ]
    covid_function = interp1d(reference_years, reference_values_covid, kind="linear")

    # CAGR function
    ## Short range
    annual_growth_rate_passenger_short_range_prospective = aeromaps_leveling_function(
        self,
        cagr_passenger_short_range_reference_periods,
        cagr_passenger_short_range_reference_periods_values,
        model_name=self.name,
    )
    self.df.loc[:, "annual_growth_rate_passenger_short_range"] = (
        annual_growth_rate_passenger_short_range_prospective
    )
    ## Medium range
    annual_growth_rate_passenger_medium_range_prospective = aeromaps_leveling_function(
        self,
        cagr_passenger_medium_range_reference_periods,
        cagr_passenger_medium_range_reference_periods_values,
        model_name=self.name,
    )
    self.df.loc[:, "annual_growth_rate_passenger_medium_range"] = (
        annual_growth_rate_passenger_medium_range_prospective
    )
    ## Long range
    annual_growth_rate_passenger_long_range_prospective = aeromaps_leveling_function(
        self,
        cagr_passenger_long_range_reference_periods,
        cagr_passenger_long_range_reference_periods_values,
        model_name=self.name,
    )
    self.df.loc[:, "annual_growth_rate_passenger_long_range"] = (
        annual_growth_rate_passenger_long_range_prospective
    )

    # Short range
    for k in range(covid_start_year, covid_end_year_passenger + 1):
        self.df.loc[k, "rpk_short_range"] = self.df.loc[
            covid_start_year - 1, "rpk_short_range"
        ] * covid_function(k)
    for k in range(covid_end_year_passenger + 1, self.end_year + 1):
        self.df.loc[k, "rpk_short_range"] = self.df.loc[k - 1, "rpk_short_range"] * (
            1 + self.df.loc[k, "annual_growth_rate_passenger_short_range"] / 100
        )

    # Medium range
    for k in range(covid_start_year, covid_end_year_passenger + 1):
        self.df.loc[k, "rpk_medium_range"] = self.df.loc[
            covid_start_year - 1, "rpk_medium_range"
        ] * covid_function(k)
    for k in range(covid_end_year_passenger + 1, self.end_year + 1):
        self.df.loc[k, "rpk_medium_range"] = self.df.loc[k - 1, "rpk_medium_range"] * (
            1 + self.df.loc[k, "annual_growth_rate_passenger_medium_range"] / 100
        )

    # Long range
    for k in range(covid_start_year, covid_end_year_passenger + 1):
        self.df.loc[k, "rpk_long_range"] = self.df.loc[
            covid_start_year - 1, "rpk_long_range"
        ] * covid_function(k)
    for k in range(covid_end_year_passenger + 1, self.end_year + 1):
        self.df.loc[k, "rpk_long_range"] = self.df.loc[k - 1, "rpk_long_range"] * (
            1 + self.df.loc[k, "annual_growth_rate_passenger_long_range"] / 100
        )

    rpk_short_range = self.df["rpk_short_range"]
    rpk_medium_range = self.df["rpk_medium_range"]
    rpk_long_range = self.df["rpk_long_range"]

    rpk_short_range = rpk_short_range * rpk_short_range_measures_impact
    rpk_medium_range = rpk_medium_range * rpk_medium_range_measures_impact
    rpk_long_range = rpk_long_range * rpk_long_range_measures_impact

    self.df.loc[:, "rpk_short_range"] = rpk_short_range
    self.df.loc[:, "rpk_medium_range"] = rpk_medium_range
    self.df.loc[:, "rpk_long_range"] = rpk_long_range

    # Total
    for k in range(self.historic_start_year, self.prospection_start_year):
        self.df.loc[k, "rpk"] = rpk_init.loc[k]
    for k in range(self.prospection_start_year, self.end_year + 1):
        self.df.loc[k, "rpk"] = (
            self.df.loc[k, "rpk_short_range"]
            + self.df.loc[k, "rpk_medium_range"]
            + self.df.loc[k, "rpk_long_range"]
        )
    rpk = self.df["rpk"]

    # Annual growth rate
    for k in range(self.historic_start_year + 1, self.prospection_start_year):
        self.df.loc[k, "annual_growth_rate_passenger_short_range"] = (
            self.df.loc[k, "rpk_short_range"] / self.df.loc[k - 1, "rpk_short_range"] - 1
        ) * 100
    for k in range(self.historic_start_year + 1, self.prospection_start_year):
        self.df.loc[k, "annual_growth_rate_passenger_medium_range"] = (
            self.df.loc[k, "rpk_medium_range"] / self.df.loc[k - 1, "rpk_medium_range"] - 1
        ) * 100
    for k in range(self.historic_start_year + 1, self.prospection_start_year):
        self.df.loc[k, "annual_growth_rate_passenger_long_range"] = (
            self.df.loc[k, "rpk_long_range"] / self.df.loc[k - 1, "rpk_long_range"] - 1
        ) * 100
    for k in range(self.historic_start_year + 1, self.end_year + 1):
        self.df.loc[k, "annual_growth_rate_passenger"] = (
            self.df.loc[k, "rpk"] / self.df.loc[k - 1, "rpk"] - 1
        ) * 100

    annual_growth_rate_passenger_short_range = self.df[
        "annual_growth_rate_passenger_short_range"
    ]
    annual_growth_rate_passenger_medium_range = self.df[
        "annual_growth_rate_passenger_medium_range"
    ]
    annual_growth_rate_passenger_long_range = self.df["annual_growth_rate_passenger_long_range"]
    annual_growth_rate_passenger = self.df["annual_growth_rate_passenger"]

    # Compound Annual Growth Rate (CAGR)
    cagr_rpk_short_range = 100 * (
        (
            self.df.loc[self.end_year, "rpk_short_range"]
            / self.df.loc[self.prospection_start_year - 1, "rpk_short_range"]
        )
        ** (1 / (self.end_year - self.prospection_start_year))
        - 1
    )
    cagr_rpk_medium_range = 100 * (
        (
            self.df.loc[self.end_year, "rpk_medium_range"]
            / self.df.loc[self.prospection_start_year - 1, "rpk_medium_range"]
        )
        ** (1 / (self.end_year - self.prospection_start_year))
        - 1
    )
    cagr_rpk_long_range = 100 * (
        (
            self.df.loc[self.end_year, "rpk_long_range"]
            / self.df.loc[self.prospection_start_year - 1, "rpk_long_range"]
        )
        ** (1 / (self.end_year - self.prospection_start_year))
        - 1
    )
    cagr_rpk = 100 * (
        (
            self.df.loc[self.end_year, "rpk"]
            / self.df.loc[self.prospection_start_year - 1, "rpk"]
        )
        ** (1 / (self.end_year - self.prospection_start_year))
        - 1
    )

    # Prospective evolution of RPK (between prospection_start_year-1 and end_year)
    prospective_evolution_rpk_short_range = 100 * (
        self.df.loc[self.end_year, "rpk_short_range"]
        / self.df.loc[self.prospection_start_year - 1, "rpk_short_range"]
        - 1
    )
    prospective_evolution_rpk_medium_range = 100 * (
        self.df.loc[self.end_year, "rpk_medium_range"]
        / self.df.loc[self.prospection_start_year - 1, "rpk_medium_range"]
        - 1
    )
    prospective_evolution_rpk_long_range = 100 * (
        self.df.loc[self.end_year, "rpk_long_range"]
        / self.df.loc[self.prospection_start_year - 1, "rpk_long_range"]
        - 1
    )
    prospective_evolution_rpk = 100 * (
        self.df.loc[self.end_year, "rpk"] / self.df.loc[self.prospection_start_year - 1, "rpk"]
        - 1
    )

    self.float_outputs["cagr_rpk_short_range"] = cagr_rpk_short_range
    self.float_outputs["cagr_rpk_medium_range"] = cagr_rpk_medium_range
    self.float_outputs["cagr_rpk_long_range"] = cagr_rpk_long_range
    self.float_outputs["cagr_rpk"] = cagr_rpk
    self.float_outputs["prospective_evolution_rpk_short_range"] = (
        prospective_evolution_rpk_short_range
    )
    self.float_outputs["prospective_evolution_rpk_medium_range"] = (
        prospective_evolution_rpk_medium_range
    )
    self.float_outputs["prospective_evolution_rpk_long_range"] = (
        prospective_evolution_rpk_long_range
    )
    self.float_outputs["prospective_evolution_rpk"] = prospective_evolution_rpk

    return (
        rpk_short_range,
        rpk_medium_range,
        rpk_long_range,
        rpk,
        annual_growth_rate_passenger_short_range,
        annual_growth_rate_passenger_medium_range,
        annual_growth_rate_passenger_long_range,
        annual_growth_rate_passenger,
        cagr_rpk_short_range,
        cagr_rpk_medium_range,
        cagr_rpk_long_range,
        cagr_rpk,
        prospective_evolution_rpk_short_range,
        prospective_evolution_rpk_medium_range,
        prospective_evolution_rpk_long_range,
        prospective_evolution_rpk,
    )

RPKReference 

RPKReference(name='rpk_reference', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute reference Revenue Passenger Kilometers (RPK) with baseline air traffic growth.

Parameters:

Name Type Description Default
name str

Name of the model instance ('rpk_reference' by default).

 'rpk_reference'
Source code in aeromaps/models/air_transport/air_traffic/rpk.py 
369
370
def __init__(self, name="rpk_reference", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(rpk, reference_cagr_passenger_reference_periods, reference_cagr_passenger_reference_periods_values, covid_start_year, covid_rpk_drop_start_year, covid_end_year_passenger, covid_end_year_reference_rpk_ratio)

RPK reference calculation.

Parameters:

Name Type Description Default
rpk Series

Number of Revenue Passenger Kilometer (RPK) for all passenger air transport [RPK].

 required
reference_cagr_passenger_reference_periods list

Reference periods for the reference CAGR for passenger market [yr].

 required
reference_cagr_passenger_reference_periods_values list

Reference CAGR for passenger market for the reference periods [%].

 required
covid_start_year Number

Covid-19 start year [yr].

 required
covid_rpk_drop_start_year float

Drop in RPK due to Covid-19 for the start year [%].

 required
covid_end_year_passenger Number

Covid-19 end year [yr].

 required
covid_end_year_reference_rpk_ratio float

Percentage of traffic level reached in Covid-19 end year compared with the one in Covid-19 start year [%].

 required

Returns:

Name Type Description
rpk_reference Series

Number of Revenue Passenger Kilometer (RPK) for all passenger air transport with a baseline air traffic growth [RPK].
reference_annual_growth_rate_passenger Series

Reference annual growth rate for passenger market [%/year].
Source code in aeromaps/models/air_transport/air_traffic/rpk.py 
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def compute(
    self,
    rpk: pd.Series,
    reference_cagr_passenger_reference_periods: list,
    reference_cagr_passenger_reference_periods_values: list,
    covid_start_year: Number,
    covid_rpk_drop_start_year: float,
    covid_end_year_passenger: Number,
    covid_end_year_reference_rpk_ratio: float,
) -> Tuple[pd.Series, pd.Series]:
    """
    RPK reference calculation.

    Parameters
    ----------
    rpk : pd.Series
        Number of Revenue Passenger Kilometer (RPK) for all passenger air transport [RPK].
    reference_cagr_passenger_reference_periods : list
        Reference periods for the reference CAGR for passenger market [yr].
    reference_cagr_passenger_reference_periods_values : list
        Reference CAGR for passenger market for the reference periods [%].
    covid_start_year : Number
        Covid-19 start year [yr].
    covid_rpk_drop_start_year : float
        Drop in RPK due to Covid-19 for the start year [%].
    covid_end_year_passenger : Number
        Covid-19 end year [yr].
    covid_end_year_reference_rpk_ratio : float
        Percentage of traffic level reached in Covid-19 end year compared with the one in Covid-19 start year [%].

    Returns
    -------
    rpk_reference : pd.Series
        Number of Revenue Passenger Kilometer (RPK) for all passenger air transport with a baseline air traffic growth [RPK].
    reference_annual_growth_rate_passenger : pd.Series
        Reference annual growth rate for passenger market [%/year].
    """
    for k in range(self.historic_start_year, self.prospection_start_year):
        self.df.loc[k, "rpk_reference"] = rpk.loc[k]

    covid_start_year = int(covid_start_year)
    covid_rpk_drop_start_year = int(covid_rpk_drop_start_year)
    covid_end_year_passenger = int(covid_end_year_passenger)
    covid_end_year_reference_rpk_ratio = int(covid_end_year_reference_rpk_ratio)

    self.df.loc[covid_start_year - 1, "rpk_reference"] = rpk.loc[covid_start_year - 1]

    # Covid functions
    reference_years = [covid_start_year, covid_end_year_passenger]
    reference_values_covid = [
        1 - covid_rpk_drop_start_year / 100,
        covid_end_year_reference_rpk_ratio / 100,
    ]
    covid_function = interp1d(reference_years, reference_values_covid, kind="linear")

    # CAGR function
    reference_annual_growth_rate_passenger = aeromaps_leveling_function(
        self,
        reference_cagr_passenger_reference_periods,
        reference_cagr_passenger_reference_periods_values,
        model_name=self.name,
    )
    self.df.loc[:, "reference_annual_growth_rate_passenger"] = (
        reference_annual_growth_rate_passenger
    )

    # Main
    for k in range(covid_start_year, covid_end_year_passenger + 1):
        self.df.loc[k, "rpk_reference"] = self.df.loc[
            covid_start_year - 1, "rpk_reference"
        ] * covid_function(k)
    for k in range(covid_end_year_passenger + 1, self.end_year + 1):
        self.df.loc[k, "rpk_reference"] = self.df.loc[k - 1, "rpk_reference"] * (
            1 + self.df.loc[k, "reference_annual_growth_rate_passenger"] / 100
        )

    rpk_reference = self.df["rpk_reference"]

    return (rpk_reference, reference_annual_growth_rate_passenger)

RPKMeasures 

RPKMeasures(name='rpk_measures', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute the impact of ad-hoc measures to reduce short, medium, and long-range traffic.

Parameters:

Name Type Description Default
name str

Name of the model instance ('rpk_measures' by default).

 'rpk_measures'
Source code in aeromaps/models/air_transport/air_traffic/rpk.py 
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def __init__(self, name="rpk_measures", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(rpk_short_range_measures_final_impact, rpk_medium_range_measures_final_impact, rpk_long_range_measures_final_impact, rpk_short_range_measures_start_year, rpk_medium_range_measures_start_year, rpk_long_range_measures_start_year, rpk_short_range_measures_duration, rpk_medium_range_measures_duration, rpk_long_range_measures_duration)

RPK measures impact calculation.

Parameters:

Name Type Description Default
rpk_short_range_measures_final_impact float

Final impact of specific measures in terms of percentage reduction in RPK for short-range market [%].

 required
rpk_medium_range_measures_final_impact float

Final impact of specific measures in terms of percentage reduction in RPK for medium-range market [%].

 required
rpk_long_range_measures_final_impact float

Final impact of specific measures in terms of percentage reduction in RPK for long-range market [%].

 required
rpk_short_range_measures_start_year Number

Start year for implementing specific measures to reduce RPK on short-range market [yr].

 required
rpk_medium_range_measures_start_year Number

Start year for implementing specific measures to reduce RPK on medium-range market [yr].

 required
rpk_long_range_measures_start_year Number

Start year for implementing specific measures to reduce RPK on long-range market [yr].

 required
rpk_short_range_measures_duration float

Duration for implementing 98% of specific measures to reduce RPK on short-range market [yr].

 required
rpk_medium_range_measures_duration float

Duration for implementing 98% of specific measures to reduce RPK on medium-range market [yr].

 required
rpk_long_range_measures_duration float

Duration for implementing 98% of specific measures to reduce RPK on long-range market [yr].

 required

Returns:

Name Type Description
rpk_short_range_measures_impact Series

Traffic reduction impact of specific measures for passenger short-range market [%].
rpk_medium_range_measures_impact Series

Traffic reduction impact of specific measures for passenger medium-range market [%].
rpk_long_range_measures_impact Series

Traffic reduction impact of specific measures for passenger long-range market [%].
Source code in aeromaps/models/air_transport/air_traffic/rpk.py 
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def compute(
    self,
    rpk_short_range_measures_final_impact: float,
    rpk_medium_range_measures_final_impact: float,
    rpk_long_range_measures_final_impact: float,
    rpk_short_range_measures_start_year: Number,
    rpk_medium_range_measures_start_year: Number,
    rpk_long_range_measures_start_year: Number,
    rpk_short_range_measures_duration: float,
    rpk_medium_range_measures_duration: float,
    rpk_long_range_measures_duration: float,
) -> Tuple[pd.Series, pd.Series, pd.Series]:
    """
    RPK measures impact calculation.

    Parameters
    ----------
    rpk_short_range_measures_final_impact : float
        Final impact of specific measures in terms of percentage reduction in RPK for short-range market [%].
    rpk_medium_range_measures_final_impact : float
        Final impact of specific measures in terms of percentage reduction in RPK for medium-range market [%].
    rpk_long_range_measures_final_impact : float
        Final impact of specific measures in terms of percentage reduction in RPK for long-range market [%].
    rpk_short_range_measures_start_year : Number
        Start year for implementing specific measures to reduce RPK on short-range market [yr].
    rpk_medium_range_measures_start_year : Number
        Start year for implementing specific measures to reduce RPK on medium-range market [yr].
    rpk_long_range_measures_start_year : Number
        Start year for implementing specific measures to reduce RPK on long-range market [yr].
    rpk_short_range_measures_duration : float
        Duration for implementing 98% of specific measures to reduce RPK on short-range market [yr].
    rpk_medium_range_measures_duration : float
        Duration for implementing 98% of specific measures to reduce RPK on medium-range market [yr].
    rpk_long_range_measures_duration : float
        Duration for implementing 98% of specific measures to reduce RPK on long-range market [yr].

    Returns
    -------
    rpk_short_range_measures_impact : pd.Series
        Traffic reduction impact of specific measures for passenger short-range market [%].
    rpk_medium_range_measures_impact : pd.Series
        Traffic reduction impact of specific measures for passenger medium-range market [%].
    rpk_long_range_measures_impact : pd.Series
        Traffic reduction impact of specific measures for passenger long-range market [%].
    """
    short_range_transition_year = (
        rpk_short_range_measures_start_year + rpk_short_range_measures_duration / 2
    )
    medium_range_transition_year = (
        rpk_medium_range_measures_start_year + rpk_medium_range_measures_duration / 2
    )
    long_range_transition_year = (
        rpk_long_range_measures_start_year + rpk_long_range_measures_duration / 2
    )
    rpk_short_range_measures_limit = 0.02 * rpk_short_range_measures_final_impact
    rpk_medium_range_measures_limit = 0.02 * rpk_medium_range_measures_final_impact
    rpk_long_range_measures_limit = 0.02 * rpk_long_range_measures_final_impact
    rpk_short_range_measures_parameter = np.log(100 / 2 - 1) / (
        rpk_short_range_measures_duration / 2
    )
    rpk_medium_range_measures_parameter = np.log(100 / 2 - 1) / (
        rpk_medium_range_measures_duration / 2
    )
    rpk_long_range_measures_parameter = np.log(100 / 2 - 1) / (
        rpk_long_range_measures_duration / 2
    )

    for k in range(self.historic_start_year, self.prospection_start_year):
        self.df.loc[k, "rpk_short_range_measures_impact"] = 1
        self.df.loc[k, "rpk_medium_range_measures_impact"] = 1
        self.df.loc[k, "rpk_long_range_measures_impact"] = 1

    for k in range(self.prospection_start_year - 1, self.end_year + 1):
        if (
            rpk_short_range_measures_final_impact
            / (
                1
                + np.exp(
                    -rpk_short_range_measures_parameter * (k - short_range_transition_year)
                )
            )
            < rpk_short_range_measures_limit
        ):
            self.df.loc[k, "rpk_short_range_measures_impact"] = 1
        else:
            self.df.loc[k, "rpk_short_range_measures_impact"] = (
                1
                - rpk_short_range_measures_final_impact
                / 100
                / (
                    1
                    + np.exp(
                        -rpk_short_range_measures_parameter * (k - short_range_transition_year)
                    )
                )
            )
        if (
            rpk_medium_range_measures_final_impact
            / (
                1
                + np.exp(
                    -rpk_medium_range_measures_parameter * (k - medium_range_transition_year)
                )
            )
            < rpk_medium_range_measures_limit
        ):
            self.df.loc[k, "rpk_medium_range_measures_impact"] = 1
        else:
            self.df.loc[k, "rpk_medium_range_measures_impact"] = (
                1
                - rpk_medium_range_measures_final_impact
                / 100
                / (
                    1
                    + np.exp(
                        -rpk_medium_range_measures_parameter
                        * (k - medium_range_transition_year)
                    )
                )
            )
        if (
            rpk_long_range_measures_final_impact
            / (
                1
                + np.exp(-rpk_long_range_measures_parameter * (k - long_range_transition_year))
            )
            < rpk_long_range_measures_limit
        ):
            self.df.loc[k, "rpk_long_range_measures_impact"] = 1
        else:
            self.df.loc[k, "rpk_long_range_measures_impact"] = (
                1
                - rpk_long_range_measures_final_impact
                / 100
                / (
                    1
                    + np.exp(
                        -rpk_long_range_measures_parameter * (k - long_range_transition_year)
                    )
                )
            )

    rpk_short_range_measures_impact = self.df["rpk_short_range_measures_impact"]
    rpk_medium_range_measures_impact = self.df["rpk_medium_range_measures_impact"]
    rpk_long_range_measures_impact = self.df["rpk_long_range_measures_impact"]

    return (
        rpk_short_range_measures_impact,
        rpk_medium_range_measures_impact,
        rpk_long_range_measures_impact,
    )