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)
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
model_manager = Link_Model_Manager_class(configfile, "static",
connection.gateway, sim_dt, "bpr",
coefficients)
#Estimating bpr coefficients with beats
num_links = model_manager.otm_api.scenario().get_num_links()
avg_travel_time = np.zeros(num_links)
num_coeff = 5
for i in range(num_links):
link_info = model_manager.otm_api.scenario().get_link_with_id(long(i))
fft = (link_info.getFull_length() / 1000 / link_info.get_ffspeed_kph())
coefficients[long(i)] = np.zeros(num_coeff)
coefficients[i][0] = copy(fft)
coefficients[i][4] = copy(fft * 0.15)
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
for i in range(num_links):
fft= (model_manager.beats_api.get_link_with_id(long(i)).getFull_length() \
/ model_manager.beats_api.get_link_with_id(long(i)).get_ffspeed_mps())/3600
coefficients[long(i)] = np.zeros(num_coeff)
coefficients[i][0] = copy(fft)
coefficients[i][4] = copy(fft*0.15)
route = {}
route['1'] = [0, 3, 4, 5]
plt.rcParams.update({'font.size': 18})
connection = Java_Connection()
demand_range = [0, 2, 4, 6, 8, 10, 12, 14, 15, 16, 18, 20]
bpr_param = [0., 0.3, 0.4, 0.6, 0.8, 1.]
for b in bpr_param:
for d in demand_range:
if connection.pid is not None:
this_folder = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
configfile = this_folder +'\\configfiles\\cfg\\Total20\\cfg_vi_static_10.xml'
coefficients = {}
T = 90 # Time horizon of interest
sim_dt = 90.0 # Duration of one time_step for the traffic model
sampling_dt = 90 # 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, "static", sim_dt, "bpr", coefficients)
model_manager.beats_api.set_stochastic_process('deterministic')
#Estimating bpr coefficients with beats
num_links = model_manager.beats_api.get_num_links()
avg_travel_time = np.zeros(num_links)
num_coeff = 5
for i in range(num_links):
fft= (model_manager.beats_api.get_link_with_id(long(i)).getFull_length() \
/ model_manager.beats_api.get_link_with_id(long(i)).get_ffspeed_mps())
coefficients[long(i)] = np.zeros(num_coeff)
coefficients[i][0] = copy(fft)
coefficients[i][4] = copy(fft*b)
route = {}
route['1'] = [0, 3, 4, 5]
# File where to save the pickled objects
this_folder = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
outputfile = os.path.join(this_folder, os.path.pardir, 'output',
scenario_name + '.pickle')
coefficients = {} #BPR Coefficients
T = 3600 # Time horizon of interest
sim_dt = 0.0 # Duration of one time_step for the traffic model
sampling_dt = 600 # 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)
#Estimating bpr coefficients with beats
num_links = model_manager.otm_api.get_num_links()
avg_travel_time = np.zeros(num_links)
num_coeff = 5
for i in range(num_links):
fft= (model_manager.otm_api.get_link_with_id(long(i)).getFull_length() \
/ model_manager.otm_api.get_link_with_id(long(i)).get_ffspeed_mps()) / 3600
coefficients[long(i)] = np.zeros(num_coeff)
coefficients[i][0] = copy(fft)
coefficients[i][4] = copy(fft * 0.15)