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Traffic_demand_model.py
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from Create_distributioncenter_Layer import Create_distributioncenter_layer
from Create_vertiport_layer import Create_vertiport_layer
from Node_coupling import Node_coupling
from Distribute_demand import Distribute_demand
from Loitering_missions import Loitering_missions
from Fix_Intention import fix_times
from multiprocessing import Pool as ThreadPool
list_of_demands = ['very_low','low', 'medium', 'high', 'ultra']
list_of_Dcenter_proportions = [0.40, 0.50, 0.60, 0.70, 0.80]
list_of_loitering_missions_number = [5, 6, 7, 8, 9] #set them accordingly for the demandlevels in this order: ['very_low', 'low', 'medium', 'high', 'ultra']
##Fixed variables distribute demand:
Number_of_samples = 9 #amount of samples that are created with the exact same parameters
timesteps = 3600 #amount of seconds in which flights are distributed
#Percentage_Dcenters = 0.80 #proportion of vertiport demand that will come from distribution centers (taken from list)
Percentage_closest_Dcenters = 0.80 #proportion of vertiport demand that will come from the closest distribution centers
Number_of_Dcenters_per_vertiport = 5 #amount of distribution centers that are considered closest
Percentage_known_flights = 0.80 #percentage of all flights that are revealed at 00:00:00
Percentage_emergency_flights = 0.002 #percentage of the flights, that are not revealed at the start, that are revealed 1 minute in advance instead of 10 minutes
ac_types= ['MP20', 'MP30']
#input loitering
negative_time_margin = 120 #seconds before departure from when the loiter missions start
positive_time_margin = 600 #seconds after departure until when the loiter missions last (since the arrival time is unknown)
loiter_area_side = 300 #meter: square 500 by 500 meter
#number_of_loitering_missions = 5 #(taken from list)
asd = []
input_arr = []
for demandlevel in range(len(list_of_demands)):
for proportion in list_of_Dcenter_proportions:
for sample in range(Number_of_samples):
input_arr.append([demandlevel,proportion,sample])
def calculate_intention(variables):
demandlevel,proportion,sample = variables
traffic_level = list_of_demands[demandlevel]
number_of_loitering_missions = list_of_loitering_missions_number[demandlevel]
Percentage_Dcenters = proportion
Distribution_centers_locations = Create_distributioncenter_layer()
Vertiport_locations = Create_vertiport_layer(traffic_level)
Distribution_centers_df, Vertiports_df = Node_coupling(Distribution_centers_locations,
Vertiport_locations)
# flight_schedule_df = Distribute_demand(timesteps, Percentage_Dcenters, Percentage_closest_Dcenters,
# Number_of_Dcenters_per_vertiport, Percentage_known_flights,
# Percentage_emergency_flights, ac_types,
# Distribution_centers_df, Vertiports_df)
# semi_final_flight_schedule_df = Loitering_missions(traffic_level, Percentage_Dcenters, negative_time_margin,
# positive_time_margin, loiter_area_side, number_of_loitering_missions,
# sample, flight_schedule_df, Distribution_centers_df)
# final_df = fix_times(semi_final_flight_schedule_df)
# global asd
# asd = final_df
# filename = 'Final_flight_intentions/' + 'Flight_intention_' + traffic_level + '_' + str(int(Percentage_Dcenters*100)) + '_' + str(sample) + '.csv'
# final_df.to_csv(filename, header = False, index = False)
# print(f'File saved: {filename}')
return
def main():
calculate_intention(input_arr[0])
# pool = ThreadPool(32)
# results = pool.map(calculate_intention, input_arr)
# pool.close()
if __name__ == '__main__':
main()
# for demandlevel in range(len(list_of_demands)):
# traffic_level = list_of_demands[demandlevel]
# number_of_loitering_missions = list_of_loitering_missions_number[demandlevel]
# for proportion in list_of_Dcenter_proportions:
# Percentage_Dcenters = proportion
# for sample in range(Number_of_samples):
# Distribution_centers_locations = Create_distributioncenter_layer()
# Vertiport_locations = Create_vertiport_layer(traffic_level)
# Distribution_centers_df, Vertiports_df = Node_coupling(Distribution_centers_locations,
# Vertiport_locations)
# flight_schedule_df = Distribute_demand(timesteps, Percentage_Dcenters, Percentage_closest_Dcenters,
# Number_of_Dcenters_per_vertiport, Percentage_known_flights,
# Percentage_emergency_flights, ac_types,
# Distribution_centers_df, Vertiports_df)
# Loitering_missions(traffic_level, Percentage_Dcenters, negative_time_margin,
# positive_time_margin, loiter_area_side, number_of_loitering_missions,
# sample, flight_schedule_df, Distribution_centers_df)
#Reveal time
#1. Part of the flights (60%) is known at 00:00:00
#1. Function of departure time (10-15 minutes before departure)
#2. Emergency flights (1 minute before departure)
#Drone Type
#1. 3 types (20, 25 and 30 knots)
#2. Equal proportion
#Priority (perhaps based on distance) (higher is more important)
#1. 3 Levels and a 4th for emergency flights
# food local deliveries more important
# loitering more important
# Emergency = 4
# Loitering = 3
# Vertirport to Vertiport = 2
# Dcenter to Vertiport = 1
#loitering:
#1. Needs to be destined in constrained airpsace
#2. Needs to be destined not in range of Dcenter
#3. Choose random
#add new center_points
#fix loitering N per Traffic level
#append to flight intention start time, duration, polygon
#repeat nine times
#Document, more comments/report
#Slides M2 meeting: dont explain all the variabels. But more inro the demand level literature Aprrox: 3/4 slides
#Report