def setUpClass(cls):
# make Java connection
cls.connection = Java_Connection()
# create a static/bpr model managers
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles',
'seven_links.xml')
bpr_coefficients = {
0L: [1, 0, 0, 0, 1],
1L: [1, 0, 0, 0, 1],
2L: [5, 0, 0, 0, 5],
3L: [2, 0, 0, 0, 2],
4L: [2, 0, 0, 0, 2],
5L: [1, 0, 0, 0, 1],
6L: [5, 0, 0, 0, 5]
}
cls.model_manager = Link_Model_Manager_class(configfile, "static",
cls.connection, None,
"bpr", bpr_coefficients)
api = cls.model_manager.beats_api
time_period = 1 # Only have one time period for static model
paths_list = list(api.get_path_ids())
commodity_list = list(api.get_commodity_ids())
route_list = {}
for path_id in paths_list:
route_list[path_id] = api.get_subnetwork_with_id(
path_id).get_link_ids()
# Test used to validate the Demand_Assignment_Class
# Creating the demand assignment for initialization
cls.demand_assignments = Demand_Assignment_class(route_list,
commodity_list,
time_period,
dt=time_period)
demands = {}
demand_value = np.zeros(time_period)
demand_value1 = np.zeros(time_period)
demand_value[0] = 2
demand_value1[0] = 2
demands[(1L, 1L)] = demand_value
demands[(2L, 1L)] = demand_value1
demands[(3L, 1L)] = demand_value
cls.demand_assignments.set_all_demands(demands)
# create link states
cls.link_states = cls.model_manager.traffic_model.Run_Model(
cls.demand_assignments)
def setUpClass(cls):
cls.conn = Java_Connection()
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = os.path.join(this_folder, os.path.pardir, os.path.pardir,
'configfiles', 'seven_links.xml')
sim_dt = 2
coefficients = {
0L: [1, 0, 0, 0, 1],
1L: [1, 0, 0, 0, 1],
2L: [2, 0, 0, 0, 2],
3L: [1, 0, 0, 0, 1],
4L: [2, 0, 0, 0, 2],
5L: [1, 0, 0, 0, 1],
6L: [1, 0, 0, 0, 1]
}
cls.model_manager = Link_Model_Manager_class(configfile, "mn",
cls.conn.gateway, sim_dt,
"bpr", coefficients)
def setUpClass(cls):
# make Java connection
cls.connection = Java_Connection()
# create a static/bpr model manager
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles',
'seven_links.xml')
bpr_coefficients = {
0L: [1, 0, 0, 0, 1],
1L: [1, 0, 0, 0, 1],
2L: [5, 0, 0, 0, 5],
3L: [2, 0, 0, 0, 2],
4L: [2, 0, 0, 0, 2],
5L: [1, 0, 0, 0, 1],
6L: [5, 0, 0, 0, 5]
}
cls.model_manager = Link_Model_Manager_class(configfile, "static",
cls.connection, None,
"bpr", bpr_coefficients)
from Solvers.Solver_Class import Solver_class
from Model_Manager.Link_Model_Manager import Link_Model_Manager_class
from Java_Connection import Java_Connection
from copy import copy
import matplotlib.pyplot as plt
import os
import inspect
from Solvers.Path_Based_Frank_Wolfe_Solver import Path_Based_Frank_Wolfe_Solver
import csv
plt.rcParams.update({'font.size': 18})
# Flag that indicates whether we are doing decomposition or not
decompositio_flag = False
connection = Java_Connection()
if connection.pid is not None:
# Contains local path to input configfile, for the three_links.xml network
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles',
'seven_links.xml')
coefficients = {}
T = 1800 # Time horizon of interest
sim_dt = 0.0 # Duration of one time_step for the traffic model
sampling_dt = 300 # Duration of time_step for the solver, in this case it is equal to sim_dt
# from py4j.java_gateway import JavaGateway,GatewayParameters
from Model_Manager.Link_Model_Manager import Link_Model_Manager_class
from Model_Manager.BeATS_Model_Manager import BeATS_Model_Manager_class
from Java_Connection import Java_Connection
from Data_Types.Demand_Assignment_Class import Demand_Assignment_class
from Traffic_Models.MN_Model import MN_Model_Class
from copy import copy
import matplotlib.pyplot as plt
import os
import inspect
import csv
from Solvers.Path_Based_Frank_Wolfe_Solver import Path_Based_Frank_Wolfe_Solver
plt.rcParams.update({'font.size': 18})
connection = Java_Connection()
if connection.pid is not None:
this_folder = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles', 'cfg_vi_mn.xml')
coefficients = {}
T = 3600 # Time horizon of interest
sim_dt = 4.0 # Duration of one time_step for the traffic model
sampling_dt = 4 # Duration of time_step for the solver, in this case it is equal to sim_dt
model_manager = Link_Model_Manager_class(configfile, connection.gateway, "mn", sim_dt, "bpr", coefficients)
#Estimating bpr coefficients with beats
num_links = model_manager.beats_api.get_num_links()
avg_travel_time = np.zeros(num_links)
num_coeff = 5
def call_solver(args):
# Flag that indicates whether we are doing decomposition or not
decomposition_flag = args.decomposition_flag
conn = Java_Connection(decomposition_flag)
if conn.pid is not None:
sim_dt = 2.0
T = args.T # Time horizon of interest
sampling_dt = args.sampling_dt # Duration of one time_step for the solver
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles',
args.configfile)
instantaneous = True # Indicates whether we are doing ctm predictive (True) or instantaneous (False)
model_manager = OTM_Model_Manager_class(configfile, args.model,
conn.gateway, sim_dt)
if (model_manager.is_valid()):
num_steps = int(T / sampling_dt)
# Get the OD Matrix form Model Manager
# OD Matrix can also be initialized from another source, as long as it fits the OD_Matrix class format
OD_Matrix = model_manager.get_OD_Matrix(num_steps, sampling_dt)
if OD_Matrix is not None:
# Algorithm to solve the problem
if args.solver_algorithm == "FW":
solver_algorithm = Path_Based_Frank_Wolfe_Solver
elif args.solver_algorithm == "MSA":
solver_algorithm = Method_of_Successive_Averages_Solver
else:
solver_algorithm = Extra_Projection_Method_Solver
scenario_solver = Solver_class(model_manager, solver_algorithm)
assignment, solver_run_time = scenario_solver.Solver_function(
T, sampling_dt, OD_Matrix, decomposition_flag)
if assignment is None:
print "Solver did not run"
else:
#Save assignment into a csv file
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(
inspect.currentframe())))
outputfile = os.path.join(this_folder, os.path.pardir,
'output',
args.configfile[:-4] + '.csv')
# # We first save in the paramenters of the scenario
# csv_file = open(outputfile, 'wb')
# writer = csv.writer(csv_file)
# # Saving the model type
# writer.writerow(['model type:','BeATS'])
# od = model_manager.otm_api.get_od_info()
# demand_api = [item * 3600 for item in od[0].get_total_demand_vps().getValues()]
# od_dt = od[0].get_total_demand_vps().getDt()
# if od_dt is None:
# od_dt = sampling_dt
#
# # Saving the demand per od and time horizon value
# writer.writerow(['demand dt (s)', 'od demand (vh)','T (s)'])
# writer.writerow([od_dt,demand_api,T])
#
# writer.writerow(['(path ID, commodity ID)', 'array of demand (vh) per dt on path'])
# # Now we save the assignment values to csv file
# for key, value in assignment.get_all_demands().items():
# writer.writerow([key, value])
#
# csv_file.close()
#print "\nDemand Assignment:"
#assignment.print_all()
path_costs = model_manager.evaluate(assignment,
T,
initial_state=None)
#print "\nPath costs in secons:"
#path_costs.print_all()
if decomposition_flag:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
else:
rank = 0
#Distance to Nash
error_percentage = scenario_solver.distance_to_Nash(
assignment, path_costs, sampling_dt, OD_Matrix)
if rank == 0:
print "\n"
print "%.02f" % error_percentage, "% vehicles from equilibrium"
'''
plt.rcParams.update({'font.size': 18})
# Function to save the output data
def save_ouput_to_picle(outputfile, assignment, path_costs, run_time, error):
# Write results to picke file
# The DTA assignment and corresponding path_cost from file
with open(outputfile, "wb") as f:
pickle.dump(assignment, f)
pickle.dump(path_costs, f)
pickle.dump(run_time, f)
pickle.dump(error, f)
# Flag that indicates whether we are doing decomposition or not
decompositio_flag = True
connection = Java_Connection(decompositio_flag)
if connection.pid is not None:
# Contains local path to input configfile, for the three_links.xml network
this_folder = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
scenario_name = 'seven_links' #Scenario name
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles', scenario_name+'.xml')
coefficients = {}
T = 3600 # Time horizon of interest
sim_dt = 0.1 # Duration of one time_step for the traffic model
sampling_dt = 1200 # Duration of time_step for the solver, in this case it is equal to sim_dt
model_manager = Link_Model_Manager_class(configfile, "static", connection.gateway, sim_dt, "bpr", coefficients)
def setUpClass(cls):
cls.conn = Java_Connection()
import sys
from Model_Manager.Link_Model_Manager import Link_Model_Manager_class
from Java_Connection import Java_Connection
from copy import copy
from Solvers.Frank_Wolfe_Solver_Static import construct_igraph
import os
import inspect
import argparse
# Flag that indicates whether we are doing decomposition or not
decompositio_flag = False
# Flag that indicates whether we are doing decomposition or not
connection = Java_Connection(decompositio_flag)
if connection.pid is not None:
# Contains local path to input configfile, for the three_links.xml network
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
scenario_name = 'scenario' #Scenario name
configfile = os.path.join(this_folder, os.path.pardir, 'configfiles',
scenario_name + '.xml')
print "Loading data for: ", scenario_name
# File where to save the pickled objects
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
