Carrier¶

class passengersim.core.Carrier¶

Bases: object

Carrier object representing an airline with revenue management controls.

Represents an airline carrier with counters for tracking sales, revenue, and bookings, along with configuration for revenue management systems, pricing strategies, and forecasting.

Methods

`__init__`(args, *kwargs)
`add_ancillary`	Add an ancillary (code & price).
`add_carrier_history`	Add an item to the carrier history.
`add_customer_model`	Add a customer model to the Carrier
`add_frat5_mkt`	Specify Frat5 for an O&D market.
`get_ancillary_by_index`	Get ancillary by index.
`get_ancillary_price`	Get the price for an ancillary, by code.
`get_ancillary_sold`	Get the sold for an ancillary, by code.
`get_carrier_history`	Return carrier history.
`get_forecast_accuracy`	Return forecast accuracy.
`get_frat5_mkt`	Get Frat5 for an O&D market.
`get_history_def`	Get the history definition.
`get_spill_counters`	Return spill counters.
`raw_bid_price_trace`	Raw bid price trace.
`raw_bookings_by_day`	Raw bookings by fare class by days prior to departure.
`raw_bookings_by_segment_fare_dcp`	Raw bookings by segment, fare class, and DCP.
`raw_displacement_cost_trace`	Raw displacement cost trace.
`raw_fare_class_distribution`	Raw fare class distribution.
`raw_load_factor_distribution`	Raw load factor distribution.
`raw_revenue_by_segment_fare_dcp`	Raw revenue by segment, fare class, and DCP.
`reset_bid_price_trace`	Reset bid price trace.
`reset_displacement_cost_trace`	Reset displacement cost trace.

Attributes

`ancillaries`	List of ancillaries that this airline will offer
`brand_preference`	An array of Brand Preference information
`cabin_ordering`	Ordering of cabin types on this carrier, from best to worst.
`control`	RM control technique, such as 'leg', 'bp', 'bp_loose', 'vn', 'classless'
`cp_algorithm`	Algorithm to use for Continuous Pricing, defaults to 'none'
`cp_elasticity`	For Continuous Pricing, estimate customer price elasticity
`cp_quantize`	For Contrinuous Pricing, round the value to this.
`cp_record`	For Continuous Pricing, do we record in the highest_closed class or the lowest_open?
`cp_record_highest_closed_as_open`	For continuous pricing, do we record the highest closed class as open in the pathclass history?
`cp_scale`	Scale the Contuous Pricing modifier.
`current_tf_index`	Current timeframe index
`dcp_days_prior`	Days prior for DCPs (i.e. the start time of each timeframe).
`default_booking_classes`	Default booking classes for legs and paths on this carrier.
`frat5`	Default Frat5 curve to use for this Carrier
`gt_available_seat_miles`	Grand total ASM, gets updated outside the burn period and is not reset by the reset methods
`gt_cp_revenue`	Grand total Continuous Pricing revenue, gets updated outside the burn period and is not reset by the reset methods
`gt_cp_sold`	Grand total Continuous Pricing sales, gets updated outside the burn period and is not reset by the reset methods
`gt_demand`	Grand total generated demand, gets updated outside the burn period and is not reset by the reset methods
`gt_revenue`	Grand total revenue, gets updated outside the burn period and is not reset by the reset methods
`gt_revenue_passenger_miles`	Grand total RPM, gets updated outside the burn period and is not reset by the reset methods
`gt_sold`	Grand total sold, gets updated outside the burn period and is not reset by the reset methods
`history_length`	History length
`load_factor_curve`	Load Factor curve, was an experiment in used this type of RM control.
`metadata`	A dictionary for storing arbitrary metadata on the Carrier object
`name`	Name is usually the code, such as 'AL1' or 'DL'
`num_objects`	Number of Carrier objects allocated, was used for memory leak detection
`proration_rule`	How do we prorate revenue between legs on connecting paths?
`revenue`	Revenue total, reset after each sample
`rm_sys`	The RM system used by this carrier.
`sold`	Number sold, reset after each sample
`sold_priceable`	Number sold priceable, reset after each sample
`store_q_history`	Whether to store the Q history
`truncation_rule`	Do we mark a TimeFrame as closed if it's closed at the start, end, or both

add_ancillary()¶

Add an ancillary (code & price).

Parameters:

anc : Ancillary: The ancillary item to add.

Returns:

int – Returns 0 on success.

add_carrier_history()¶

Add an item to the carrier history.

ONLY USED FOR UNIT TESTING.

Parameters:

trial : int: Trial number.
sample : int: Sample number.
sold : int: Number sold.
sold_priceable : int: Number sold priceable.
revenue : float: Revenue amount.

Returns:

int – Returns 0 on success.

add_customer_model()¶

Add a customer model to the Carrier

Parameters:

customer_model : CustomerModel: The CustomerModel to use.

add_frat5_mkt()¶

Specify Frat5 for an O&D market.

Parameters:

orig : str: Origin airport code.
dest : str: Destination airport code.
frat5 : Frat5: The Frat5 curve to use for this market.

ancillaries¶: List of ancillaries that this airline will offer

brand_preference¶: An array of Brand Preference information

cabin_ordering¶

Ordering of cabin types on this carrier, from best to worst.

For any leg that has multiple cabins, all cabin names must appear in this list. The cabins are strictly ordered such that any customer can be accommodated in the cabin for the ticket they purchased or any better cabin.

Returns:: list[str] – List of cabin name strings ordered from best to worst.

control¶: RM control technique, such as ‘leg’, ‘bp’, ‘bp_loose’, ‘vn’, ‘classless’

cp_algorithm¶: Algorithm to use for Continuous Pricing, defaults to ‘none’

cp_elasticity¶: For Continuous Pricing, estimate customer price elasticity

cp_quantize¶: For Contrinuous Pricing, round the value to this. i.e. 10 will result in allprices being a multiple of 10

cp_record¶: For Continuous Pricing, do we record in the highest_closed class or the lowest_open?

cp_record_highest_closed_as_open¶

For continuous pricing, do we record the highest closed class as open in the pathclass history?

Has no effect unless cp_record is set to highest_closed

cp_scale¶: Scale the Contuous Pricing modifier. Example, 0.5 will add 50% of the modifier to the bid price

current_tf_index¶: Current timeframe index

dcp_days_prior¶: Days prior for DCPs (i.e. the start time of each timeframe)

default_booking_classes¶

Default booking classes for legs and paths on this carrier.

Each entry is a pair (booking_class, lowest_cabin) where booking_class is the name of the booking class and lowest_cabin is the lowest cabin type where customers who purchase this booking class may be accommodated. When setting, each item may be a plain string (stored with an empty lowest_cabin) or a (str, str) pair.

Returns:: list[tuple[str, str]] – List of (booking_class, lowest_cabin) pairs.

frat5¶: Default Frat5 curve to use for this Carrier

get_ancillary_by_index()¶

Get ancillary by index.

ONLY USED FOR DEBUGGING.

Parameters:

index : int: The index of the ancillary to retrieve.

Returns:

Ancillary – The ancillary at the specified index.

get_ancillary_price()¶

Get the price for an ancillary, by code.

Parameters:

name : str: The ancillary code to look up.

Returns:

float – The price of the ancillary.

get_ancillary_sold()¶

Get the sold for an ancillary, by code.

Parameters:

name : str: The ancillary code to look up.

Returns:

int – The number sold of the ancillary.

get_carrier_history()¶

Return carrier history.

Returns carrier history as a list of dictionaries, with one item for each sample.

Returns:: list[dict] – List of dictionaries containing carrier history data with keys: ‘trial’, ‘sample’, ‘carrier’, ‘sold’, ‘sold_priceable’, ‘revenue’.

get_forecast_accuracy()¶

Return forecast accuracy.

Returns forecast accuracy as a list of dictionaries.

Returns:: list[dict] – List of dictionaries containing forecast accuracy data with keys: ‘trial’, ‘sample’, ‘carrier’, ‘booking_class’, ‘timeframe’, ‘num_zero_displacement’, ‘num_obs’, ‘sold’, ‘sold_priceable’, ‘fcst_mean’, ‘fcst_variance’.

get_frat5_mkt()¶

Get Frat5 for an O&D market.

Parameters:

orig : str: Origin airport code.
dest : str: Destination airport code.

Returns:

Frat5 or None – The Frat5 curve for the specified market, or None if not found.

get_history_def()¶

Get the history definition.

Returns the history definition used to initialize the history for this carrier’s buckets and pathclasses.

Returns:: dict – Dictionary with keys: - ‘num_departures’ : int - Number of samples in carrier history - ‘num_timeframes’ : int - Number of timeframes in carrier history - ‘store_priceable’ : bool - Whether carrier has a Frat5 curve

get_spill_counters()¶

Return spill counters.

Returns spill counters as a list of dictionaries.

gt_available_seat_miles¶: Grand total ASM, gets updated outside the burn period and is not reset by the reset methods

gt_cp_revenue¶: Grand total Continuous Pricing revenue, gets updated outside the burn period and is not reset by the reset methods

gt_cp_sold¶: Grand total Continuous Pricing sales, gets updated outside the burn period and is not reset by the reset methods

gt_demand¶: Grand total generated demand, gets updated outside the burn period and is not reset by the reset methods

gt_revenue¶: Grand total revenue, gets updated outside the burn period and is not reset by the reset methods

gt_revenue_passenger_miles¶: Grand total RPM, gets updated outside the burn period and is not reset by the reset methods

gt_sold¶: Grand total sold, gets updated outside the burn period and is not reset by the reset methods

history_length¶: History length

load_factor_curve¶: Load Factor curve, was an experiment in used this type of RM control. (deprecated)

metadata¶: A dictionary for storing arbitrary metadata on the Carrier object

name¶: Name is usually the code, such as ‘AL1’ or ‘DL’

num_objects¶: Number of Carrier objects allocated, was used for memory leak detection

proration_rule¶: How do we prorate revenue between legs on connecting paths?

raw_bid_price_trace()¶

Raw bid price trace.

Returns:: dict – Dictionary with DCP keys and values containing bid price statistics.

raw_bookings_by_day()¶

Raw bookings by fare class by days prior to departure.

Returns:: dict – Dictionary with fare class keys and booking data by days prior.

raw_bookings_by_segment_fare_dcp()¶

Raw bookings by segment, fare class, and DCP.

Returns:: dict – Dictionary with segment keys, then fare class keys, then DCP keys with booking counts.

raw_displacement_cost_trace()¶

Raw displacement cost trace.

Returns:: dict – Dictionary with DCP keys and values containing displacement cost statistics.

raw_fare_class_distribution()¶

Raw fare class distribution.

Returns:: dict – Dictionary with fare class keys and values containing ‘sold’ and ‘revenue’ keys.

raw_load_factor_distribution()¶

Raw load factor distribution.

Returns:: numpy.ndarray – Array of load factor distribution data.

raw_revenue_by_segment_fare_dcp()¶

Raw revenue by segment, fare class, and DCP.

Returns:: dict – Dictionary with segment keys, then fare class keys, then DCP keys with revenue values.

reset_bid_price_trace()¶

Reset bid price trace.

Clears the accumulated bid price trace data.

Returns:: None

reset_displacement_cost_trace()¶

Reset displacement cost trace.

Clears the accumulated displacement cost trace data.

Returns:: None

revenue¶: Revenue total, reset after each sample

rm_sys¶

The RM system used by this carrier.

This RM system should be a daily callback that implements the RM system for this carrier. It should also be attached to the Simulation as a daily callback and is triggered via the regular callback process. This attribute is provided for convenience so that the RM system can be easily accessed if needed.

Type:: RmSys

sold¶: Number sold, reset after each sample

sold_priceable¶: Number sold priceable, reset after each sample

store_q_history¶: Whether to store the Q history

truncation_rule¶: Do we mark a TimeFrame as closed if it’s closed at the start, end, or both