• Highlights
• Installation
• Usage
• Case Study Demonstrations
  • Ramp Metering
  • Signalised Intersection Management
  • Calibration / Fine-Tuning of Control Parameters
• Documentation
  • Control Context Objects
    • RampMetering
    • RampMeteringCoordinationGroup
    • Intersection
    • IntersectionGroup
  • Ramp Metering
    • ALINEA
    • HERO
    • METALINE
  • Signalised Intersection Management
    • Max-Pressure (Fixed-Cycle)
    • Max-Pressure (Flexible-Cycle)
    • SCOOT/SCATS
• Citations

Relevant demos include:

python demo_ALINEA.py
python demo_HERO.py
python demo_METALINE.py

Relevant demos include:

python demo_MAX_FIX.py
python demo_MAX_FLEX.py
python demo_SCOSCA.py

Relevant demos include:

python demo_optimisation.py

which calls the python script demo_optimisation_execute_script.py.

Control Context Objects

RampMetering

This is the example of RampMetering object specification. The sensors can be E2 sensors or E1 sensors.

ramp_meter = RampMeter(
    tl_id="J0",
    mainline_sensors=["e2_5", "e2_4"],
    queue_sensors=["e2_0"],
)

RampMeteringCoordinationGroup

This is the example of RampMeterCoordinationGroup object specification. The sensors can be E2 sensors or E1 sensors.

ramp_meter_group = RampMeterCoordinationGroup(
    ramp_meters_ordered=[
        RampMeter(
            tl_id="J12",
            mainline_sensors=["e1_13", "e1_14"],
            queue_sensors=["e2_1", "e2_2"],
            smoothening_factor=0.1,
            saturation_flow_veh_per_sec=0.5,
        ),
        RampMeter(
            tl_id="J11",
            mainline_sensors=["e1_2", "e1_3"],
            queue_sensors=["e2_3"],
            smoothening_factor=0.1,
            saturation_flow_veh_per_sec=0.5,
        ),
        RampMeter(
            tl_id="J0",
            mainline_sensors=["e2_5", "e2_4"],
            queue_sensors=["e2_0"],
            smoothening_factor=0.1,
            saturation_flow_veh_per_sec=0.5,
        ),
    ],
    ramp_meter_ids=["J12", "J11", "J0"],
)

Intersection

This is the example of Intersection object specification. You can either provide a list of sensors (E2 sensors) or a list of lanes (links) to measure traffic states (queue lengths, degree of saturation). If both are provided, sensors are taken first. Provision of green_states and yellow_states is only necessary for SCOOT/SCATS.

intersection2 = Intersection(
    tl_id="intersection2",
    phases=[0, 2, 4],
    # links = {0:["183049933#0_1", "-38361908#1_1"],
    #           2:["-38361908#1_1", "-38361908#1_2"],
    #           4:["-25973410#1_1", "758088375#0_1", "758088375#0_2"]},
    sensors={
        0: ["e2_183049933#0_1", "e2_-38361908#1_1"],
        2: ["e2_-38361908#1_1", "e2_-38361908#1_2"],
        4: ["e2_-25973410#1", "e2_758088375#0_1", "e2_758088375#0_2"],
    },
    green_states=["GGrrrGrr", "GGGrrrrr", "rrrGGrGG"],
    yellow_states=["yyrrryrr", "yyyrrrrr", "rrryyryy"],
)

IntersectionGroup

This is the example of IntersectionGroup object specification. You can either provide a list of sensors (E2 sensors) or a list of lanes (links) to measure traffic states (queue lengths, degree of saturation). If both are provided, sensors are taken first.

intersection1 = Intersection(
    tl_id="intersection1",
    ...
)
intersection2 = Intersection(
    tl_id="intersection2",
    ...
)
intersection3 = Intersection(
    tl_id="intersection3",
    ...
)
intersection4 = Intersection(
    tl_id="intersection4",
    ...
)
intersection5 = Intersection(
    tl_id="intersection5",
    ...
)
districts = {
    "front": ["intersection1", "intersection2"],
    "middle": ["intersection3", "intersection4"],
    "back": ["intersection5"],
}
critical_district_order = {
    "front": [
        "intersection1",
        "intersection2",
        "intersection3",
        "intersection4",
        "intersection5",
    ],
    "middle": [
        "intersection3",
        "intersection2",
        "intersection4",
        "intersection1",
        "intersection5",
    ],
    "back": [
        "intersection5",
        "intersection4",
        "intersection3",
        "intersection2",
        "intersection1",
    ],
}
connection_between_intersections = {
    "intersection1": ["183049934_1", "183049933#0_1", "1164287131#0_1"],  # To Int 2
    "intersection2": ["38361908#1_1", "E3_1"],  # To Int 3
    "intersection3": [
        "E1_1",
        "758088377#1_1",
        "758088377#2_1",
        "22889927#0_1",
    ],  # To Int 4
    "intersection4": [
        "22889927#2_1",
        "22889927#3_1",
        "22889927#4_1",
        "387296014#0_1",
        "387296014#1_1",
        "696225646#1_1",
        "696225646#2_1",
        "696225646#3_1",
        "130569446_1",
        "E5_1",
        "E6_1",
    ],  # To Int 5
}
intersection_group = IntersectionGroup(
    intersections=[
        intersection1,
        intersection2,
        intersection3,
        intersection4,
        intersection5,
    ],
    districts=districts,
    critical_district_order=critical_district_order,
    connection_between_intersections=connection_between_intersections,
)

Ramp Metering

ALINEA

This is the example of ALINEA object specification.

controller = ALINEA(
    params={
        "target_occupancy": 10,
        "K_P": 30,
        "K_I": 0,
        "cycle_duration": 60,
        "measurement_period": int(
            60 / 0.5
        ),  # int(cycle_duration / simulation.time_step)
        "min_rate": 5,
        "max_rate": 100,
    },
    ramp_meter=ramp_meter,
)

HERO

This is the example of HERO object specification.

controller = HERO(
    params={
        "hero_cycle_duration": 60,  # similar to ALINEA cycle duration
        "queue_activation_threshold_m": 15.0,  # master queue trigger
        "queue_release_threshold_m": 2.5,  # dissolve cluster
        "min_queue_setpoint_m": 5.0,  # for slaves
        "anticipation_factor": 1.0,  # factor to obtain nonconservative prediction of demand to come in next control period
        "avg_vehicle_spacing": 7.5,  # average vehicle spacing to convert meters to vehicles and vice versa, from queue length measurements
    },
    coordination_group=ramp_meter_group,
    alinea_controllers={
        "J12": alinea_controller1,
        "J11": alinea_controller2,
        "J0": alinea_controller3,
    },
)

METALINE

This is the example of METALINE object specification.

controller = METALINE(
    params={
        "cycle_duration": 60,  # control cycle
        "measurement_period": int(
            60 / 0.5
        ),  # int(cycle_duration / simulation.time_step)
        "min_rate": 5,
        "max_rate": 100,
    },
    coordination_group=ramp_meter_group,
    target_occupancies=[10, 10, 10],
    # Interaction gain matrix (3x3)
    K_P=np.array(
        [
            [30, -5, 0],  # ramp 1 influenced negatively by ramp 2
            [-3, 25, -2],  # ramp 2 influenced by neighbors
            [0, -4, 20],
        ]
    ),
    # Optional integral gain matrix
    K_I=np.zeros(shape=(3, 3)),
)

Signalised Intersection Management

Max-Pressure (Fixed-Cycle)

This is the example of MaxPressure_Fix object specification.

controller = MaxPressure_Fix(
    params={
        "T_L": 3,  # Yellow Time
        "G_T_MIN": 5,  # Min Greentime (used for Max. Pressure)
        "G_T_MAX": 50,  # Max Greentime (used for Max. Pressure)
        "measurement_period": int(1 / 0.25),  # int(1 / simulation.time_step)
        "cycle_duration": 120,
    }, 
    intersection=intersection1
)

Max-Pressure (Flexible-Cycle)

This is the example of MaxPressure_Flex object specification.

controller = MaxPressure_Flex(
    params={
        "T_A": 5, # Recheck-Pressure Time (used for Max.Pressure)
        "T_L": 3,  # Yellow Time
        "G_T_MIN": 5,  # Min Greentime (used for Max. Pressure)
        "G_T_MAX": 50,  # Max Greentime (used for Max. Pressure)
        "measurement_period": int(1 / 0.25),  # int(1 / simulation.time_step)
        "cycle_duration": 120,
    }, 
    intersection=intersection1
)

SCOOT/SCATS

This is the example of ScootScats object specification.

controller = ScootScats(
    scosca_params = {
        "adaptation_cycle": 30,
        "adaptation_green": 10,
        "green_thresh": 2,
        "adaptation_offset": 1,
        "offset_thresh": 0.5,
        "min_cycle_length": 50,
        "max_cycle_length": 180,
        "ds_upper_val": 0.925,
        "ds_lower_val": 0.875,
        "measurement_period": int(1 / 0.25),  # 1 / simulation_step_size
        "travel_time_adjustments": {
            "intersection1": ["183049934_1", 2],
            "intersection3": ["E1_1", 3],
            "intersection4": ["22889927#3_1", 9],
        },
        "intersection_offset_rules": {
            "intersection2": {
                "base_offset_from": None,
                "travel_time_from": "intersection2",
            },
            "intersection4": {
                "base_offset_from": None,
                "travel_time_from": "intersection3",
            },
            "intersection1": {
                "base_offset_from": "intersection2",
                "travel_time_from": "intersection1",
            },
            "intersection3": {
                "base_offset_from": "intersection4",
                "travel_time_from": "intersection4",
            },
            "default": {
                "base_offset_from": "intersection4",
                "travel_time_from": "intersection4",
            },
        },
    }, 
    intersection_group=intersection_group1, 
    initial_greentimes={
        "intersection1": [30, 30, 21],
        "intersection2": [30, 30, 21],
        "intersection3": [30, 30, 21],
        "intersection4": [40, 30],
        "intersection5": [30, 30, 21],
    },
    initial_cycle_length=120,
)