def setUp(self):
super(RandomTrafficGeneratorTest, self).setUp()
testdata_dir = './testdata'
self._output_dir = tempfile.mkdtemp(
dir=absltest.get_default_test_tmpdir())
sumo_net_file = 'mtv_tiny.net.xml'
map_file = _load_file(testdata_dir, sumo_net_file)
self._net = sumolib.net.readNet(map_file)
traffic_generator = random_traffic_generator.RandomTrafficGenerator(
self._net)
self._random_traffic_generator = traffic_generator
# The traffic generator uses numpy to draw random samples. The numpy random
# seed is set here to make the result replicable.
np.random.seed(0)
def generate_evacuation_taz_demands(self, residential_car_density,
serving_car_density, demand_mean_hours,
demand_stddev_hours,
population_portion):
"""Generates evacuation TAZ demands."""
# TODO(yusef): Fix map + total number of cars.
# To make the demands consistent, use the default map, paradise_type.net.xml
# as the input map instead of the reversed. For Paradise map, an easy way to
# check is that the total number of cars is 11072.
net = sumolib.net.readNet(self._sumo_net_file)
traffic_generator = random_traffic_generator.RandomTrafficGenerator(
net)
visualizer = map_visualizer.MapVisualizer(net)
print('Generating TAZ demands with STD: ', demand_stddev_hours,
' Portion: ', population_portion)
# Demands from residential roads.
residential_edge_type = ['highway.residential']
residential_edges = net.filterEdges(residential_edge_type)
demand_mean_seconds = demand_mean_hours * 60 * 60
demand_stddev_seconds = demand_stddev_hours * 60 * 60
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
demand_mean_seconds, demand_stddev_seconds)
car_per_meter_residential = residential_car_density * population_portion
np.random.seed(FLAGS.random_seed)
residential = traffic_generator.create_evacuation_auto_routing_demands(
residential_edges, time_sampler_parameters,
car_per_meter_residential)
# Demands from parking roads.
parking_edge_type = ['highway.service']
parking_edges = net.filterEdges(parking_edge_type)
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
demand_mean_seconds, demand_stddev_seconds)
car_per_meter_parking = serving_car_density * population_portion
parking = traffic_generator.create_evacuation_auto_routing_demands(
parking_edges, time_sampler_parameters, car_per_meter_parking)
all_demands = residential + parking
departure_time_points = [x.time for x in all_demands]
cars_per_time_point = [x.num_cars for x in all_demands]
departure_time_points = np.array(departure_time_points) / 3600
print('TAZ demands. Total vehicles: ', sum(cars_per_time_point))
# TODO(yusef): reconcile.
demands_dir = os.path.join(self._output_dir, _DEMANDS)
file_util.f_makedirs(demands_dir)
output_hist_figure_path = os.path.join(
demands_dir, 'departure_time_histogram_taz_std_%s_portion_%s.pdf' %
(demand_stddev_hours, population_portion))
output_cumulative_figure_path = os.path.join(
demands_dir,
'departure_time_cumulative_taz_std_%s_portion_%s.pdf' %
(demand_stddev_hours, population_portion))
pkl_file = os.path.join(
demands_dir, 'demands_taz_tuple_std_%s_portion_%s.pkl' %
(demand_stddev_hours, population_portion))
routes_file = os.path.join(
demands_dir, 'demands_taz_std_%s_portion_%s.rou.xml' %
(demand_stddev_hours, population_portion))
# Output the demand xml file.
visualizer.plot_demands_departure_time(
departure_time_points,
cars_per_time_point,
output_hist_figure_path=output_hist_figure_path,
output_cumulative_figure_path=output_cumulative_figure_path)
file_util.save_variable(pkl_file, all_demands)
exit_taz = 'exit_taz'
traffic_generator.write_evacuation_vehicle_auto_routing_demands(
all_demands, exit_taz, routes_file)
def generate_evacuation_shortest_path_demands(
self, residential_car_density, serving_car_density,
evacuation_edges, demand_mean_hours, demand_stddev_hours,
population_portion):
"""Generates evacuation demands."""
net = sumolib.net.readNet(self._sumo_net_file)
traffic_generator = random_traffic_generator.RandomTrafficGenerator(
net)
visualizer = map_visualizer.MapVisualizer(net)
print('Generating TAZ demands with STD: ', demand_stddev_hours,
' Portion: ', population_portion)
# Calculate the distance to the evacuation exits.
evacuation_path_trees = {}
evacuation_path_length = {}
for exit_edge in evacuation_edges:
evacuation_path_trees[exit_edge], evacuation_path_length[
exit_edge] = (
net.getRestrictedShortestPathsTreeToEdge(exit_edge))
# Demands from residential roads.
residential_edge_type = ['highway.residential']
residential_edges = net.filterEdges(residential_edge_type)
demand_mean_seconds = demand_mean_hours * 60 * 60
demand_stddev_seconds = demand_stddev_hours * 60 * 60
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
demand_mean_seconds, demand_stddev_seconds)
car_per_meter_residential = residential_car_density * population_portion
np.random.seed(FLAGS.random_seed)
residential = traffic_generator.create_evacuation_shortest_path_demands(
residential_edges, time_sampler_parameters,
car_per_meter_residential, evacuation_edges, evacuation_path_trees,
evacuation_path_length)
# Demands from parking roads.
parking_edge_type = ['highway.service']
parking_edges = net.filterEdges(parking_edge_type)
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
demand_mean_seconds, demand_stddev_seconds)
car_per_meter_parking = serving_car_density * population_portion
parking = traffic_generator.create_evacuation_shortest_path_demands(
parking_edges, time_sampler_parameters, car_per_meter_parking,
evacuation_edges, evacuation_path_trees, evacuation_path_length)
all_demands = residential + parking
departure_time_points = [x.time for x in all_demands]
cars_per_time_point = [x.num_cars for x in all_demands]
departure_time_points = np.array(departure_time_points) / 3600
print('Shortest path demands. Total vehicles: ',
sum(cars_per_time_point))
# Output the demand xml file.
demands_dir = os.path.join(self._output_dir, _DEMANDS)
file_util.f_makedirs(demands_dir)
output_hist_figure_path = os.path.join(
demands_dir,
'departure_time_histogram_shortest_path_std_%s_portion_%s.pdf' %
(demand_stddev_hours, population_portion))
output_cumulative_figure_path = os.path.join(
demands_dir,
'departure_time_cumulative_shortest_path_std_%s_portion_%s.pdf' %
(demand_stddev_hours, population_portion))
pkl_file = os.path.join(
demands_dir, 'demands_shortest_path_tuple_std_%s_portion_%s.pkl' %
(demand_stddev_hours, population_portion))
routes_file = os.path.join(
demands_dir, 'demands_shortest_path_std_%s_portion_%s.rou.xml' %
(demand_stddev_hours, population_portion))
visualizer.plot_demands_departure_time(
departure_time_points,
cars_per_time_point,
output_hist_figure_path=output_hist_figure_path,
output_cumulative_figure_path=output_cumulative_figure_path)
file_util.save_variable(pkl_file, all_demands)
traffic_generator.write_evacuation_vehicle_path_demands(
all_demands, routes_file)
def generate_arterial_routes_demands_main(_):
"""This is an example of generating demands only on arterial and freeways.
The generated routes do no have the ones only on freeways.
"""
net = sumolib.net.readNet(FLAGS.sumo_net_file)
traffic_generator = random_traffic_generator.RandomTrafficGenerator(net)
routes_file = os.path.join(FLAGS.output_dir, 'arterial_routes_demands.xml')
token = '<routes>\n'
util.append_line_to_file(routes_file, token)
token = (' <vType id="Car" accel="0.8" decel="4.5" sigma="0.5" '
'length="5" minGap="2.5" maxSpeed="38" guiShape="passenger"/>\n')
util.append_line_to_file(routes_file, token)
# Setup freeway routes.
figure_path = os.path.join(FLAGS.output_dir, 'freeway_routes.pdf')
input_output = traffic_generator.get_freeway_input_output(
figure_path=figure_path)
token = ' <!-- freeway routes -->'
util.append_line_to_file(routes_file, token)
freeway_routes = traffic_generator.setup_shortest_routes(
input_output,
edge_type_list=random_traffic_generator.FREEWAY_EDGE_TYPES,
routes_file=routes_file,
figure_folder=None)
# Setup arterial roads routes.
figure_path = os.path.join(FLAGS.output_dir, 'arterial_routes.pdf')
input_output = traffic_generator.get_arterial_input_output(
figure_path=figure_path)
token = ' <!-- arterial routes -->'
util.append_line_to_file(routes_file, token)
arterial_routes = traffic_generator.setup_shortest_routes(
input_output,
edge_type_list=(random_traffic_generator.FREEWAY_EDGE_TYPES +
random_traffic_generator.ARTERIAL_EDGE_TYPES),
routes_file=routes_file,
figure_folder=None)
token = ' <!-- freeway + arterial roads demands -->'
util.append_line_to_file(routes_file, token)
time_step_size = 100
for time_point in range(0, FLAGS.simulation_duration, time_step_size):
# Create arbitrary
freeway_routes_demands = [(0, 0.5), (1, 0.5), (2, 0.5), (3, 0.5)]
traffic_generator.generate_routes_flow(time_point, time_step_size,
freeway_routes,
freeway_routes_demands,
routes_file)
arterial_routes_demands = []
# arterial_routes_demands = [(route_id, 0.002) for route_id in
# range(len(arterial_routes))]
for route_index, route in enumerate(arterial_routes):
if (route['edge_from'].getID() == '27628577#0'
and route['edge_to'].getID() == '23925644#3'):
arterial_routes_demands.append((route_index, 0.3))
elif (route['edge_from'].getID() == '17971093'
and route['edge_to'].getID() == '23925644#3'):
arterial_routes_demands.append((route_index, 0.3))
elif (route['edge_from'].getID() == '17971093'
and route['edge_to'].getID() == '-496364805#0'):
arterial_routes_demands.append((route_index, 0.3))
elif (route['edge_from'].getID() == '688239440'
and route['edge_to'].getID() == '23925644#3'):
arterial_routes_demands.append((route_index, 0.2))
else:
arterial_routes_demands.append((route_index, 0.01))
traffic_generator.generate_routes_flow(time_point, time_step_size,
arterial_routes,
arterial_routes_demands,
routes_file)
token = '\n</routes>'
util.append_line_to_file(routes_file, token)