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Merge pull request berkeleyflow#62 from berkeleyflow/baselines
Baselines
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‎flow/benchmarks/baselines/bottleneck0.py

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"""
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This script is used to quickly evaluate a baseline for bottleneck0.
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Baseline is no AVs.
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Bottleneck in which the actions are specifying a desired velocity in a segment
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of space. The autonomous penetration rate in this example is 10%.
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Action Dimension: (?, )
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Observation Dimension: (?, )
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Horizon: 1000 steps
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"""
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from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams, \
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InFlows
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from flow.core.traffic_lights import TrafficLights
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from flow.core.vehicles import Vehicles
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from flow.controllers import ContinuousRouter
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from flow.envs.bottleneck_env import DesiredVelocityEnv
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from flow.core.experiment import SumoExperiment
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from flow.scenarios.bottleneck.scenario import BottleneckScenario
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from flow.scenarios.bottleneck.gen import BottleneckGenerator
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import numpy as np
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# time horizon of a single rollout
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HORIZON = 1000
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SCALING = 1
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NUM_LANES = 4 * SCALING # number of lanes in the widest highway
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DISABLE_TB = True
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DISABLE_RAMP_METER = True
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AV_FRAC = 0.10
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vehicles = Vehicles()
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vehicles.add(veh_id="human",
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speed_mode=9,
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routing_controller=(ContinuousRouter, {}),
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lane_change_mode=0,
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num_vehicles=1 * SCALING)
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controlled_segments = [("1", 1, False), ("2", 2, True), ("3", 2, True),
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("4", 2, True), ("5", 1, False)]
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num_observed_segments = [("1", 1), ("2", 3), ("3", 3),
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("4", 3), ("5", 1)]
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additional_env_params = {
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"target_velocity": 40,
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"disable_tb": True,
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"disable_ramp_metering": True,
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"controlled_segments": controlled_segments,
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"symmetric": False,
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"observed_segments": num_observed_segments,
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"reset_inflow": False,
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"lane_change_duration": 5,
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"max_accel": 3,
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"max_decel": 3,
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"inflow_range": [1000, 2000]
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}
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# flow rate
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flow_rate = 1900 * SCALING
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# percentage of flow coming out of each lane
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inflow = InFlows()
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inflow.add(veh_type="human", edge="1",
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vehs_per_hour=flow_rate,
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departLane="random", departSpeed=10)
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traffic_lights = TrafficLights()
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if not DISABLE_TB:
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traffic_lights.add(node_id="2")
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if not DISABLE_RAMP_METER:
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traffic_lights.add(node_id="3")
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additional_net_params = {"scaling": SCALING}
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net_params = NetParams(in_flows=inflow,
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no_internal_links=False,
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additional_params=additional_net_params)
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sumo_params = SumoParams(
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sim_step=0.5,
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sumo_binary="sumo-gui",
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print_warnings=False,
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restart_instance=False,
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)
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env_params = EnvParams(
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evaluate=True, # Set to True to evaluate traffic metrics
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warmup_steps=40,
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sims_per_step=1,
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horizon=HORIZON,
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additional_params=additional_env_params,
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)
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initial_config = InitialConfig(
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spacing="uniform",
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min_gap=5,
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lanes_distribution=float("inf"),
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edges_distribution=["2", "3", "4", "5"],
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)
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scenario = BottleneckScenario(name="bay_bridge_toll",
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generator_class=BottleneckGenerator,
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vehicles=vehicles,
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net_params=net_params,
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initial_config=initial_config,
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traffic_lights=traffic_lights)
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env = DesiredVelocityEnv(env_params, sumo_params, scenario)
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exp = SumoExperiment(env, scenario)
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num_runs = 2
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results = exp.run(num_runs, HORIZON)
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avg_outflow = np.mean([outflow[-1] for outflow in results["per_step_returns"]])
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print('The average outflow over 500 seconds '
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'across {} runs is {}'.format(num_runs, avg_outflow))

‎flow/benchmarks/baselines/bottleneck1.py

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"""
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This script is used to quickly evaluate a baseline for bottleneck0.
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Baseline is no AVs.
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Bottleneck in which the actions are specifying a desired velocity in a segment
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of space. The autonomous penetration rate in this example is 25%.
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Humans are allowed to lane change
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Action Dimension: (?, )
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Observation Dimension: (?, )
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Horizon: 1000 steps
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"""
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from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams, \
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InFlows
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from flow.core.traffic_lights import TrafficLights
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from flow.core.vehicles import Vehicles
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from flow.controllers import ContinuousRouter
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from flow.envs.bottleneck_env import DesiredVelocityEnv
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from flow.core.experiment import SumoExperiment
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from flow.scenarios.bottleneck.scenario import BottleneckScenario
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from flow.scenarios.bottleneck.gen import BottleneckGenerator
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import numpy as np
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# time horizon of a single rollout
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HORIZON = 1000
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SCALING = 1
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NUM_LANES = 4 * SCALING # number of lanes in the widest highway
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DISABLE_TB = True
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DISABLE_RAMP_METER = True
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AV_FRAC = 0.25
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vehicles = Vehicles()
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vehicles.add(veh_id="human",
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speed_mode=9,
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routing_controller=(ContinuousRouter, {}),
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lane_change_mode=1621,
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num_vehicles=1 * SCALING)
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controlled_segments = [("1", 1, False), ("2", 2, True), ("3", 2, True),
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("4", 2, True), ("5", 1, False)]
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num_observed_segments = [("1", 1), ("2", 3), ("3", 3),
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("4", 3), ("5", 1)]
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additional_env_params = {
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"target_velocity": 40,
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"disable_tb": True,
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"disable_ramp_metering": True,
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"controlled_segments": controlled_segments,
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"symmetric": False,
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"observed_segments": num_observed_segments,
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"reset_inflow": False,
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"lane_change_duration": 5,
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"max_accel": 3,
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"max_decel": 3,
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"inflow_range": [1000, 2000]
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}
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# flow rate
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flow_rate = 1900 * SCALING
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# percentage of flow coming out of each lane
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inflow = InFlows()
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inflow.add(veh_type="human", edge="1",
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vehs_per_hour=flow_rate,
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departLane="random", departSpeed=10)
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traffic_lights = TrafficLights()
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if not DISABLE_TB:
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traffic_lights.add(node_id="2")
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if not DISABLE_RAMP_METER:
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traffic_lights.add(node_id="3")
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additional_net_params = {"scaling": SCALING}
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net_params = NetParams(in_flows=inflow,
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no_internal_links=False,
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additional_params=additional_net_params)
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sumo_params = SumoParams(
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sim_step=0.5,
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sumo_binary="sumo-gui",
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print_warnings=False,
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restart_instance=False,
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)
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env_params = EnvParams(
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evaluate=True, # Set to True to evaluate traffic metrics
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warmup_steps=40,
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sims_per_step=1,
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horizon=HORIZON,
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additional_params=additional_env_params,
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)
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initial_config = InitialConfig(
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spacing="uniform",
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min_gap=5,
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lanes_distribution=float("inf"),
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edges_distribution=["2", "3", "4", "5"],
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)
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scenario = BottleneckScenario(name="bay_bridge_toll",
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generator_class=BottleneckGenerator,
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vehicles=vehicles,
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net_params=net_params,
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initial_config=initial_config,
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traffic_lights=traffic_lights)
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env = DesiredVelocityEnv(env_params, sumo_params, scenario)
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exp = SumoExperiment(env, scenario)
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num_runs = 2
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results = exp.run(num_runs, HORIZON)
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avg_outflow = np.mean([outflow[-1] for outflow in results["per_step_returns"]])
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print('The average outflow over 500 seconds '
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'across {} runs is {}'.format(num_runs, avg_outflow))

‎flow/benchmarks/baselines/bottleneck2.py

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"""
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Script used to evaluate uncontrolled outflow performance of bottleneck2.
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Baseline is no AV control.
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Bottleneck in which the actions are specifying a desired velocity in a segment
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of space. The autonomous penetration rate in this example is 10%.
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Action Dimension: (40, )
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Observation Dimension: (281, )
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Horizon: 1000 steps
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"""
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from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams, \
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InFlows
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from flow.core.traffic_lights import TrafficLights
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from flow.core.vehicles import Vehicles
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from flow.controllers import ContinuousRouter
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from flow.envs.bottleneck_env import DesiredVelocityEnv
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from flow.core.experiment import SumoExperiment
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from flow.scenarios.bottleneck.scenario import BottleneckScenario
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from flow.scenarios.bottleneck.gen import BottleneckGenerator
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import numpy as np
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# time horizon of a single rollout
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HORIZON = 1000
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SCALING = 2
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NUM_LANES = 4 * SCALING # number of lanes in the widest highway
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DISABLE_TB = True
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DISABLE_RAMP_METER = True
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AV_FRAC = .10
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vehicles = Vehicles()
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vehicles.add(veh_id="human",
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speed_mode=9,
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routing_controller=(ContinuousRouter, {}),
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lane_change_mode=0,
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num_vehicles=1 * SCALING)
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controlled_segments = [("1", 1, False), ("2", 2, True), ("3", 2, True),
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("4", 2, True), ("5", 1, False)]
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num_observed_segments = [("1", 1), ("2", 3), ("3", 3),
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("4", 3), ("5", 1)]
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additional_env_params = {
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"target_velocity": 40,
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"disable_tb": True,
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"disable_ramp_metering": True,
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"controlled_segments": controlled_segments,
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"symmetric": False,
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"observed_segments": num_observed_segments,
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"reset_inflow": False,
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"lane_change_duration": 5,
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"max_accel": 3,
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"max_decel": 3,
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"inflow_range": [1000, 2000]
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}
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# flow rate
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flow_rate = 1900 * SCALING
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# percentage of flow coming out of each lane
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inflow = InFlows()
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inflow.add(veh_type="human", edge="1",
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vehs_per_hour=flow_rate,
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departLane="random", departSpeed=10)
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traffic_lights = TrafficLights()
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if not DISABLE_TB:
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traffic_lights.add(node_id="2")
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if not DISABLE_RAMP_METER:
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traffic_lights.add(node_id="3")
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additional_net_params = {"scaling": SCALING}
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net_params = NetParams(in_flows=inflow,
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no_internal_links=False,
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additional_params=additional_net_params)
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sumo_params = SumoParams(
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sim_step=0.5,
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sumo_binary="sumo-gui",
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print_warnings=False,
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restart_instance=False,
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)
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env_params = EnvParams(
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evaluate=True, # Set to True to evaluate traffic metrics
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warmup_steps=40,
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sims_per_step=1,
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horizon=HORIZON,
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additional_params=additional_env_params,
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)
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initial_config = InitialConfig(
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spacing="uniform",
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min_gap=5,
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lanes_distribution=float("inf"),
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edges_distribution=["2", "3", "4", "5"],
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)
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scenario = BottleneckScenario(name="bay_bridge_toll",
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generator_class=BottleneckGenerator,
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vehicles=vehicles,
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net_params=net_params,
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initial_config=initial_config,
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traffic_lights=traffic_lights)
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env = DesiredVelocityEnv(env_params, sumo_params, scenario)
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exp = SumoExperiment(env, scenario)
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num_runs = 2
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results = exp.run(num_runs, HORIZON)
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avg_outflow = np.mean([outflow[-1] for outflow in results["per_step_returns"]])
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print('The average outflow over 500 seconds '
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'across {} runs is {}'.format(num_runs, avg_outflow))

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