def figure_eight_example(render=None):
"""Perform a simulation of vehicles on a figure eight.
Parameters
----------
render: bool, optional
specifies whether to use the gui during execution
Returns
-------
exp: flow.core.experiment.Experiment
A non-rl experiment demonstrating the performance of human-driven
vehicles on a figure eight.
"""
sim_params = AimsunParams(sim_step=0.5, render=False, emission_path='data')
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {}),
num_vehicles=14)
env_params = EnvParams()
net_params = NetParams(additional_params=ADDITIONAL_NET_PARAMS.copy())
scenario = Figure8Scenario(name="figure8",
vehicles=vehicles,
net_params=net_params)
env = TestEnv(env_params, sim_params, scenario, simulator='aimsun')
return Experiment(env)
def sugiyama_example(render=None):
"""Perform a simulation of vehicles on a ring road.
Parameters
----------
render : bool, optional
specifies whether to use the gui during execution
Returns
-------
exp: flow.core.experiment.Experiment
A non-rl experiment demonstrating the performance of human-driven
vehicles on a ring road.
"""
sim_params = AimsunParams(sim_step=0.5, render=True, emission_path='data')
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
num_vehicles=22)
env_params = EnvParams()
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
net_params = NetParams(additional_params=additional_net_params)
initial_config = InitialConfig(bunching=20)
network = RingNetwork(name="sugiyama",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = TestEnv(env_params, sim_params, network, simulator='aimsun')
return Experiment(env)
def grid_example(render=None):
"""
Perform a simulation of vehicles on a grid.
Parameters
----------
render: bool, optional
specifies whether to use the gui during execution
Returns
-------
exp: flow.core.experiment.Experiment
A non-rl experiment demonstrating the performance of human-driven
vehicles and balanced traffic lights on a grid.
"""
inner_length = 300
long_length = 500
short_length = 300
N_ROWS = 2
N_COLUMNS = 3
num_cars_left = 20
num_cars_right = 20
num_cars_top = 20
num_cars_bot = 20
tot_cars = (num_cars_left + num_cars_right) * N_COLUMNS \
+ (num_cars_top + num_cars_bot) * N_ROWS
grid_array = {
"short_length": short_length,
"inner_length": inner_length,
"long_length": long_length,
"row_num": N_ROWS,
"col_num": N_COLUMNS,
"cars_left": num_cars_left,
"cars_right": num_cars_right,
"cars_top": num_cars_top,
"cars_bot": num_cars_bot
}
sim_params = AimsunParams(sim_step=0.5, render=True)
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(veh_id="human", num_vehicles=tot_cars)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS)
tl_logic = TrafficLightParams(baseline=False)
phases = [{
"duration": "31",
"minDur": "8",
"maxDur": "45",
"yellow": "3",
"state": "GGGrrrGGGrrr"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"yellow": "3",
"state": "yyyrrryyyrrr"
}, {
"duration": "31",
"minDur": "8",
"maxDur": "45",
"yellow": "3",
"state": "rrrGGGrrrGGG"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"yellow": "3",
"state": "rrryyyrrryyy"
}]
tl_logic.add("center0", phases=phases, programID=1)
tl_logic.add("center1", phases=phases, programID=1)
tl_logic.add("center2", tls_type="actuated", phases=phases, programID=1)
tl_logic.add("center3", phases=phases, programID=1)
tl_logic.add("center4", phases=phases, programID=1)
tl_logic.add("center5", tls_type="actuated", phases=phases, programID=1)
additional_net_params = {
"grid_array": grid_array,
"speed_limit": 35,
"horizontal_lanes": 1,
"vertical_lanes": 1
}
net_params = NetParams(no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing='custom')
scenario = SimpleGridScenario(name="grid-intersection",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=tl_logic)
env = AccelEnv(env_params, sim_params, scenario, simulator='aimsun')
return Experiment(env)
