def test_create_auto_routing_evacuation_demands(self):
# Settings for departure edges.
departure_edges = self._net.getEdges()
mean = 3 * 60 * 60
std = 0.7 * 60 * 60
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
mean, std)
car_per_meter_residential = 0.041666667
# Generate evacuation demands.
generator = self._random_traffic_generator
zipped_demands = generator.create_evacuation_auto_routing_demands(
departure_edges, time_sampler_parameters,
car_per_meter_residential)
actual_number_car_per_edge = [x.num_cars for x in zipped_demands]
target_number_car_per_edge = [
23, 1, 13, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1
]
self.assertListEqual(actual_number_car_per_edge[:25],
target_number_car_per_edge)
# Output the demand xml file.
output_file = os.path.join(self._output_dir, 'demands.rou.xml')
generator.write_evacuation_vehicle_auto_routing_demands(
zipped_demands, 'taz_exit', output_file)
# Test for output demands file.
with file_util.f_open(output_file, 'r') as f:
self.assertLen(f.readlines(), 427)
def test_generate_departure_time(self):
np.random.seed(123)
mean = 1000
std = 500
number = 200000
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
mean, std)
samples = self._random_traffic_generator.generate_departure_time(
time_sampler_parameters, number)
actual_mean = np.mean(samples)
actual_std = np.std(samples)
self.assertAlmostEqual(np.abs(mean - actual_mean) / mean, 0, places=2)
self.assertAlmostEqual(np.abs(std - actual_std) / std, 0, places=2)
def test_create_shortest_path_evacuation_demands(self):
# In the small map, some of the edges are not connected. So if the
# evacuation exit is set as one edge, then some of the edges may not have a
# path out. The problem can be caught in the warning or info log messages.
evacuation_edges = ['-415366780#0']
# Calculate the distance to the evacuation exits.
evacuation_path_trees = {}
evacuation_path_length = {}
for evacuation_edge in evacuation_edges:
(evacuation_path_trees[evacuation_edge],
evacuation_path_length[evacuation_edge]) = (
self._net.getRestrictedShortestPathsTreeToEdge(
evacuation_edge))
# Settings for departure edges.
departure_edges = self._net.getEdges()
mean = 3 * 60 * 60
std = 0.7 * 60 * 60
time_sampler_parameters = random_traffic_generator.TimeSamplerGammaMeanStd(
mean, std)
car_per_meter_residential = 0.041666667
# Generate evacuation demands.
generator = self._random_traffic_generator
zipped_demands = generator.create_evacuation_shortest_path_demands(
departure_edges, time_sampler_parameters,
car_per_meter_residential, evacuation_edges, evacuation_path_trees,
evacuation_path_length)
actual_destinations = [x.destination for x in zipped_demands]
actual_number_car_per_edge = [x.num_cars for x in zipped_demands]
target_destinations = [
None, '-415366780#0', '-415366780#0', '-415366780#0',
'-415366780#0', '-415366780#0', '-415366780#0', '-415366780#0',
'-415366780#0'
]
target_number_car_per_edge = [
23, 1, 13, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1
]
self.assertListEqual(actual_destinations[:9], target_destinations)
self.assertListEqual(actual_number_car_per_edge[:25],
target_number_car_per_edge)
# Output the demand xml file.
output_file = os.path.join(self._output_dir, 'demands.rou.xml')
generator.write_evacuation_vehicle_path_demands(
zipped_demands, output_file)
# Test for output demands file.
with file_util.f_open(output_file, 'r') as f:
self.assertLen(f.readlines(), 528)
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)