def main():
parser = argparse.ArgumentParser(
description="Simulation of drivers' behavior")
parser.add_argument(
'-f',
'--fleet',
help=
'Fleet sizes to simulate, formatted as comma-separated list (i.e. "-f 250,275,300")'
)
parser.add_argument(
'-m',
'--multiplier',
help=
'Surge multiplier, formatted as comma-separated list (i.e. "-m 1,1.5,2")'
)
parser.add_argument('-b', '--bonus', type=int, help='Bonus')
parser.add_argument('-d', '--demand', help='Percent false demand ')
parser.add_argument(
'-k',
'--know',
help=
'Percent knowing fare, formatted as comma-separated list (i.e. "-m 1,1.5,2") '
)
parser.add_argument(
'-p',
'--pro',
help=
'Percent pro drivers, formatted as comma-separated list (i.e. "-m 1,1.5,2") '
)
parser.add_argument('-r',
'--replications',
help='number of times to run the simulation')
parser.add_argument('-bb', '--beta', help='BETA')
parser.add_argument('-b_policy', '--bonus_policy', help='bonus per zone ')
parser.add_argument('-budget', '--budget', help='budget ')
args = parser.parse_args()
# TODO: argpars should get the bonus policy as input
data_instance = Data()
if args.fleet:
fleet_sizes = [int(args.fleet)]
else:
fleet_sizes = data_instance.FLEET_SIZE
if args.multiplier:
# surge = args.multiplier
surges = [float(x) for x in args.multiplier.split(',')]
else:
surges = [data_instance.SURGE_MULTIPLIER]
if args.know:
perc_know = [float(args.know)]
else:
perc_know = [data_instance.PERCE_KNOW]
if args.bonus:
bonus = args.bonus
else:
bonus = data_instance.BONUS
if args.beta:
beta = float(args.beta)
else:
beta = configs_dict["BETA"]
if args.pro:
pro_share = [float(x) for x in args.pro.split(',')]
else:
pro_share = [data_instance.PRO_SHARE]
if args.demand:
percent_false_demand = float(args.demand)
else:
percent_false_demand = data_instance.PERCENT_FALSE_DEMAND
if args.replications:
n_rep = int(args.replications)
else:
n_rep = 1
if args.bonus_policy:
bonus_policy = args.bonus_policy
else:
bonus_policy = data_instance.BONUS_POLICY
if args.budget:
budget = args.budget
else:
budget = data_instance.BUDGET
# output_path = "./Outputs/avg_fare_info/" + str(beta) + "/"
for fleet_size in fleet_sizes:
for surge in surges:
for perc_k in perc_know:
for pro_s in pro_share:
for repl in range(n_rep):
output_path = "./Outputs/avg_fare_info/" + str(budget) + "_" + str(bonus_policy) + "_" + \
str(datetime.datetime.now()).split('.')[0] + "/"
if not os.path.exists(output_path):
os.makedirs(output_path)
print("iteration number ", repl)
print('Fleet size is {f}'.format(f=fleet_size))
print('Surge is {}'.format(surge))
print('Percentage knowing fares is {}'.format(perc_k))
print('Percentage of professional drivers {}'.format(
pro_s))
data_instance.FLEET_SIZE = fleet_size
data_instance.PRO_SHARE = pro_s
data_instance.SURGE_MULTIPLIER = surge
data_instance.BONUS = bonus
data_instance.PERCENT_FALSE_DEMAND = percent_false_demand
data_instance.PERCE_KNOW = perc_k
m = Model(data_instance, configs_dict, beta)
# start time
stime = time.time()
# # dispatch the system for T_TOTAL seconds, at the interval of INT_ASSIGN
for T in range(data_instance.WARMUP_TIME_SECONDS,
data_instance.T_TOTAL_SECONDS,
data_instance.INT_ASSIGN):
m.dispatch_at_time(T)
print('Total drivers: ', len(m.vehicles))
print(
'# of Pro drivers: ',
len([
v for v in m.vehicles
if v.driver_type == DriverType.PROFESSIONAL
]))
print(
'# of naive drivers: ',
len([
v for v in m.vehicles
if v.driver_type == DriverType.NAIVE
]))
print(
'# of inexperienced drivers: ',
len([
v for v in m.vehicles
if v.driver_type == DriverType.INEXPERIENCED
]))
# end time
etime = time.time()
# run time of this simulation
runtime = etime - stime
print("The run time was {runtime} minutes ".format(
runtime=runtime / 60))
report = m.get_service_rate_per_zone()
# So that it doesn't save a file with 1.5.py, rather 15.py
ss = str(surge).split('.')
ss = ''.join(ss)
report.to_csv(output_path + "report for fleet size " +
str(fleet_size) + " surge " + str(ss) +
"fdemand= " + str(percent_false_demand) +
"perc_k " + str(perc_k) + "pro_s " +
str(pro_s) + " repl" + str(repl) +
".csv")
def main():
parser = argparse.ArgumentParser(
description="Simulation of drivers' behavior")
# from lib.Constants import PERCE_KNOW
parser.add_argument(
'-f',
'--fleet',
help=
'Fleet sizes to simulate, formatted as comma-separated list (i.e. "-f 250,275,300")'
)
parser.add_argument(
'-m',
'--multiplier',
help=
'Surge multiplier, formatted as comma-separated list (i.e. "-m 1,1.5,2")'
)
parser.add_argument('-b', '--bonus', type=int, help='Bonus')
parser.add_argument('-d', '--demand', help='Percent false demand ')
parser.add_argument('-AV',
'--AV_fleet_size',
help="Number of Naive drivers ")
parser.add_argument('-NAIVE',
'--NAIVE_fleet_size',
help="Number of Naive drivers ")
parser.add_argument('-PRO',
'--PRO_fleet_size',
help="Number of Professional drivers ")
parser.add_argument(
'-BH',
'--behavioral_opt',
help="Perform behavioral optimization, pass 'yes' or 'no' ")
parser.add_argument('-SURGE',
'--surge_pricing',
help="should do surge pricing, pass 'yes' or 'no' ")
parser.add_argument(
'-k',
'--know',
help=
'Percent knowing fare, formatted as comma-separated list (i.e. "-m 1,1.5,2") '
)
parser.add_argument(
'-p',
'--pro',
help=
'Percent pro drivers, formatted as comma-separated list (i.e. "-m 1,1.5,2") '
)
parser.add_argument('-r',
'--replications',
help='number of times to run the simulation')
parser.add_argument('-bb', '--beta', help='BETA')
parser.add_argument('-b_policy', '--bonus_policy', help='bonus per zone ')
parser.add_argument('-budget', '--budget', help='budget ')
args = parser.parse_args()
# TODO: argpars should get the bonus policy as input
data_instance = Data()
if args.fleet:
fleet_sizes = [int(args.fleet)]
else:
fleet_sizes = data_instance.FLEET_SIZE
if args.behavioral_opt:
if args.behavioral_opt.lower() in ('yes', 'true'):
do_behavioral_opt = True
else:
do_behavioral_opt = False
else:
do_behavioral_opt = data_instance.do_behavioral_opt
if args.surge_pricing:
if args.surge_pricing.lower() in ('yes', 'true'):
do_surge_pricing = True
else:
do_surge_pricing = False
else:
do_surge_pricing = data_instance.do_surge_pricing
if args.PRO_fleet_size:
set_of_NUM_OF_PRO_DRIVERS = [int(args.PRO_fleet_size)]
else:
set_of_NUM_OF_PRO_DRIVERS = [data_instance.PRO_FLEET_SIZE]
if args.NAIVE_fleet_size:
set_of_NUM_OF_NAIVE_DRIVERS = [int(args.NAIVE_fleet_size)]
else:
set_of_NUM_OF_NAIVE_DRIVERS = [data_instance.NAIVE_FLEET_SIZE]
if args.AV_fleet_size:
set_of_NUM_OF_AV_DRIVERS = [int(args.AV_fleet_size)]
else:
set_of_NUM_OF_AV_DRIVERS = [data_instance.AV_FLEET_SIZE]
if args.multiplier:
# surge = args.multiplier
surges = [float(x) for x in args.multiplier.split(',')]
else:
surges = [data_instance.SURGE_MULTIPLIER]
if args.know:
perc_know = [float(args.know)]
else:
perc_know = [data_instance.PERCE_KNOW]
if args.bonus:
bonus = args.bonus
else:
bonus = data_instance.BONUS
if args.beta:
beta = float(args.beta)
else:
beta = configs_dict["BETA"]
if args.pro:
pro_share = [float(x) for x in args.pro.split(',')]
else:
pro_share = [data_instance.PRO_SHARE]
if args.demand:
percent_false_demand = float(args.demand)
else:
percent_false_demand = data_instance.PERCENT_FALSE_DEMAND
if args.replications:
n_rep = int(args.replications)
else:
n_rep = 1
if args.bonus_policy:
bonus_policy = args.bonus_policy
else:
bonus_policy = data_instance.BONUS_POLICY
if args.budget:
budget = args.budget
else:
budget = data_instance.BUDGET
# output_path = "./Outputs/avg_fare_info/" + str(beta) + "/"
from lib.rebalancing_optimizer import RebalancingOpt
for num_pros in set_of_NUM_OF_PRO_DRIVERS:
for num_naives in set_of_NUM_OF_NAIVE_DRIVERS:
for num_avs in set_of_NUM_OF_AV_DRIVERS:
for surge in surges:
for repl in range(n_rep):
TOTAL_FLEET_SIZE = 2500
num_naives = TOTAL_FLEET_SIZE - num_pros
data_instance.AV_FLEET_SIZE = num_avs
data_instance.NAIVE_FLEET_SIZE = num_naives
data_instance.PRO_FLEET_SIZE = num_pros
data_instance.do_behavioral_opt = do_behavioral_opt
data_instance.do_surge_pricing = do_surge_pricing
if do_behavioral_opt:
st = '/with_behavioral_opt/'
else:
st = '/no_behavioral_opt/'
output_path = "./Outputs/" + datetime.datetime.now().strftime("%Y-%m-%d-%H-%M") + \
st + str('Pro_') + str(num_pros) \
+ str('NAIVE_') + str(num_naives) \
+ str('AV_') + str(num_avs) \
+ str('budget_') + str(budget) + "_" \
+ str("bonus_policy") + "_" + str(bonus_policy) + "_" \
+ str('do_surge') + "_" + str(data_instance.do_surge_pricing) + "_" \
+ str('do_opt') + "_" + str(data_instance.do_behavioral_opt) + "_" \
+ str(datetime.datetime.now()).split('.')[0] + "/"
if not os.path.exists(output_path):
os.makedirs(output_path)
print("iteration number ", repl)
print('Surge is {}'.format(surge))
data_instance.SURGE_MULTIPLIER = surge
data_instance.BONUS = bonus
data_instance.output_path = output_path
# data_instance.do_behavioral_opt = False
m = Model(data_instance, configs_dict, beta,
output_path)
# start time
stime = time.time()
# dispatch the system for T_TOTAL seconds, at the interval of INT_ASSIGN
# TODO: every run should in include the policy from the start
# TODO: process Feb's month as well.
months = [1, 2]
days = [30, 15]
stop_month = months[-1]
for ix, month in enumerate(months):
for d_idx in range(1, days[ix]):
stop_day = days[ix]
if month == 1 and d_idx >= 15:
# NOTE: THIS WILL NOT HAVE THE DESIRED EFFECT, BC OPERATOR has attribute set in the beginning
data_instance.do_behavioral_opt = True
m.operator.do_behavioral_opt = True
# data_instance.do_surge_pricing = True
# m.operator.do_surge_pricing = True
for T in range(
data_instance.WARMUP_TIME_SECONDS,
data_instance.T_TOTAL_SECONDS,
data_instance.INT_ASSIGN):
m.dispatch_at_time(T, day_idx=d_idx)
m.get_service_rate_per_zone(d_idx, month)
m.get_drivers_earnings_for_one_day(
d_idx, month)
m.get_operators_earnings_for_one_day(
d_idx, month)
print(
f"Starting a new day, finished day number {d_idx + 1} of month {month}"
)
print(f"it took {(time.time() - stime) / 60}")
m.reset_after_one_day_of_operation(
stop_month, stop_day)
if num_pros > 0:
all_dfs = pd.concat([
v.report_learning_rates() for v in m.vehicles
if v.driver_type == DriverType.PROFESSIONAL
],
ignore_index=True)
all_dfs.to_csv(output_path +
"fmean for all drivers.csv")
all_fare_reliability_dfs = pd.concat(
[
v.report_fare_reliability_evolution()
for v in m.vehicles
if v.driver_type == DriverType.PROFESSIONAL
],
ignore_index=True)
all_fare_reliability_dfs.to_csv(
output_path +
"fare reliability for all drivers.csv")
all_m_reliability_dfs = pd.concat(
[
v.report_matching_reliability_evolution()
for v in m.vehicles
if v.driver_type == DriverType.PROFESSIONAL
],
ignore_index=True)
all_m_reliability_dfs.to_csv(
output_path +
"matching reliability for all drivers.csv")
all_fare_reliability_dfs = pd.concat(
[
v.report_surge_bonus_behavior()
for v in m.vehicles
if v.driver_type == DriverType.PROFESSIONAL
],
ignore_index=True)
all_fare_reliability_dfs.to_csv(
output_path +
"surge behavior for all drivers.csv")
all_earning_dfs = pd.concat(
[v.report_final_earnings() for v in m.vehicles],
ignore_index=True)
all_earning_dfs.to_csv(output_path +
"earnings for all drivers.csv")
operators_revenue = m.operator.report_final_revenue()
operators_revenue.to_csv(output_path +
"operators_revenue.csv")
print('Total drivers: ', len(m.vehicles))
print(
'# of Pro drivers: ',
len([
v for v in m.vehicles
if v.driver_type == DriverType.PROFESSIONAL
]))
print(
'# of naive drivers: ',
len([
v for v in m.vehicles
if v.driver_type == DriverType.NAIVE
]))
print(
'# of inexperienced drivers: ',
len([
v for v in m.vehicles
if v.driver_type == DriverType.INEXPERIENCED
]))
# end time
etime = time.time()
# run time of this simulation
runtime = etime - stime
print("The run time was {runtime} minutes ".format(
runtime=runtime / 60))
report = m.report_final_performance()
# So that it doesn't save a file with 1.5.py, rather 15.py
ss = str(surge).split('.')
ss = ''.join(ss)
fleet_size = num_avs + num_pros + num_naives
report.to_csv(output_path + "report for fleet size " +
str(fleet_size) + " surge " + str(ss) +
"pro_ " + str(num_pros) + "naive_ " +
str(num_naives) + "AV_" + str(num_avs) +
" repl" + str(repl) + ".csv")
def main():
print("Start of main()")
# TODO: all these should be cleaned up like this:
# https://github.com/sisl/MADRL/blob/master/madrl_environments/walker/train_multi_walker.py
parser = argparse.ArgumentParser(description="Simulation of drivers' behavior")
parser.add_argument('-f', '--fleet',
help='Fleet sizes to simulate, formatted as comma-separated list (i.e. "-f 250,275,300")')
parser.add_argument('-m', '--multiplier',
help='Surge multiplier, formatted as comma-separated list (i.e. "-m 1,1.5,2")')
parser.add_argument('-b', '--bonus', type=int,
help='Bonus')
parser.add_argument('-d', '--demand',
help='Percent false demand ')
parser.add_argument('-k', '--know',
help='Percent knowing fare, formatted as comma-separated list (i.e. "-m 1,1.5,2") ')
parser.add_argument('-p', '--pro',
help='Percent pro drivers, formatted as comma-separated list (i.e. "-m 1,1.5,2") ')
parser.add_argument('-r', '--replications',
help='number of times to run the simulation')
parser.add_argument('-bb', '--beta',
help='BETA')
parser.add_argument('-b_policy', '--bonus_policy',
help='bonus per zone ')
parser.add_argument('-budget', '--budget',
help='budget ')
args = parser.parse_args()
# TODO: argpars should get the bonus policy as input
print("instantiate Data object")
data_instance = Data()
if args.fleet:
fleet_sizes = [int(args.fleet)]
else:
fleet_sizes = data_instance.FLEET_SIZE
if args.multiplier:
# surge = args.multiplier
surges = [float(x) for x in args.multiplier.split(',')]
else:
surges = [data_instance.SURGE_MULTIPLIER]
if args.know:
perc_know = [float(args.know)]
else:
perc_know = [data_instance.PERCE_KNOW]
if args.bonus:
bonus = args.bonus
else:
bonus = data_instance.BONUS
if args.beta:
beta = float(args.beta)
else:
beta = config_dict["BETA"]
if args.pro:
pro_share = [float(x) for x in args.pro.split(',')]
else:
pro_share = [data_instance.PRO_SHARE]
if args.demand:
percent_false_demand = float(args.demand)
else:
percent_false_demand = data_instance.PERCENT_FALSE_DEMAND
if args.replications:
n_rep = int(args.replications)
else:
n_rep = 5
if args.bonus_policy:
bonus_policy = args.bonus_policy
else:
bonus_policy = data_instance.BONUS_POLICY
if args.budget:
budget = args.budget
else:
budget = data_instance.BUDGET
# output_path = "./Outputs/avg_fare_info/" + str(beta) + "/"
for fleet_size in fleet_sizes:
for surge in surges:
for perc_k in perc_know:
for pro_s in pro_share:
for repl in range(n_rep):
# output_path = "./Outputs/avg_fare_info/" + str(budget) + "_" + str(bonus_policy) + "_" + \
# str(datetime.datetime.now()).split('.')[0] + "/"
# if not os.path.exists(output_path):
# os.makedirs(output_path)
print("iteration number ", repl)
print('Fleet size is {f}'.format(f=fleet_size))
print('Surge is {}'.format(surge))
print('Percentage knowing fares is {}'.format(perc_k))
print('Percentage of professional drivers {}'.format(pro_s))
data_instance.FLEET_SIZE = fleet_size
data_instance.PRO_SHARE = pro_s
data_instance.SURGE_MULTIPLIER = surge
data_instance.BONUS = bonus
data_instance.PERCENT_FALSE_DEMAND = percent_false_demand
data_instance.PERCE_KNOW = perc_k
print("Instantiated the model")
m = Model(data_instance, beta)
# start time
stime = time.time()
# # dispatch the system for T_TOTAL seconds, at the interval of INT_ASSIGN
for T in range(data_instance.WARMUP_TIME_SECONDS,
data_instance.T_TOTAL_SECONDS,
data_instance.INT_ASSIGN):
m.dispatch_at_time(T)
# end time
etime = time.time()
# run time of this simulation
runtime = etime - stime
print("The run time was {runtime} minutes ".format(runtime=runtime / 60))
m.save_zonal_stats("../performance_stats/")
