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Running on Zero
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"""
Selects the best valid signal phase based on:
- traffic demand
- predicted traffic
- priority
- fairness
- risk
This module only makes a decision.
It does NOT directly control traffic signals.
"""
def __init__(
self,
demand_weight=0.35,
prediction_weight=0.25,
priority_weight=0.20,
fairness_weight=0.10,
risk_weight=0.10
):
self.weights = {
"demand": demand_weight,
"prediction": prediction_weight,
"priority": priority_weight,
"fairness": fairness_weight,
"risk": risk_weight
}
total = sum(self.weights.values())
if total <= 0:
raise ValueError(
"Decision weights must have a positive total"
)
# Normalize weights so they sum to 1
for key in self.weights:
self.weights[key] /= total
def calculate_phase_score(
self,
demand,
prediction,
priority,
fairness,
risk
):
"""
Calculate the score for one phase.
All inputs must be normalized to [0, 1].
Higher score = better phase.
"""
values = {
"demand": demand,
"prediction": prediction,
"priority": priority,
"fairness": fairness,
"risk": risk
}
for name, value in values.items():
if not 0 <= value <= 1:
raise ValueError(
f"{name} must be between 0 and 1"
)
score = (
self.weights["demand"] * demand
+ self.weights["prediction"] * prediction
+ self.weights["priority"] * priority
+ self.weights["fairness"] * fairness
+ self.weights["risk"] * risk
)
return score
def calculate_phase_prediction(
self,
predictions,
sensor_ids
):
"""
Convert GNN predictions for multiple sensors
into one prediction score for a signal phase.
Parameters
----------
predictions : array-like
GNN predictions for all sensors.
sensor_ids : list
Sensor IDs belonging to this phase.
Returns
-------
float
Normalized phase prediction score [0, 1].
"""
if predictions is None:
raise ValueError(
"predictions cannot be None"
)
if not sensor_ids:
raise ValueError(
"sensor_ids cannot be empty"
)
predictions = list(predictions)
# Validate sensor IDs
for sensor_id in sensor_ids:
if not isinstance(sensor_id, int):
raise ValueError(
"sensor IDs must be integers"
)
if sensor_id < 0 or sensor_id >= len(predictions):
raise ValueError(
f"Invalid sensor ID: {sensor_id}"
)
# Extract predictions for this phase
phase_predictions = [
float(predictions[sensor_id])
for sensor_id in sensor_ids
]
# GNN predictions represent predicted traffic demand.
#
# We clamp negative values to zero because traffic
# demand cannot be negative.
phase_predictions = [
max(0.0, value)
for value in phase_predictions
]
# Use the average predicted demand for the phase.
phase_prediction = (
sum(phase_predictions)
/ len(phase_predictions)
)
return phase_prediction
def select_best_phase(self, phase_scores):
"""
Select the phase with the highest score.
phase_scores:
{
phase_id: score
}
"""
if not phase_scores:
raise ValueError(
"No phase scores were provided"
)
best_phase = max(
phase_scores,
key=phase_scores.get
)
return {
"selected_phase": best_phase,
"score": phase_scores[best_phase],
"all_scores": phase_scores
}
if __name__ == "__main__":
engine = DecisionEngine()
# Example GNN prediction
predictions = [
0.8,
0.4,
0.2,
0.6
]
# Example phase prediction
phase_prediction = engine.calculate_phase_prediction(
predictions,
sensor_ids=[0, 1]
)
print("Phase prediction:")
print(phase_prediction)
# Example phase scores
phase_scores = {
"Phase_1": engine.calculate_phase_score(
demand=0.80,
prediction=phase_prediction,
priority=0.20,
fairness=0.60,
risk=0.10
),
"Phase_2": engine.calculate_phase_score(
demand=0.40,
prediction=0.50,
priority=0.90,
fairness=0.80,
risk=0.20
),
"Phase_3": engine.calculate_phase_score(
demand=0.60,
prediction=0.60,
priority=0.30,
fairness=0.40,
risk=0.10
)
}
result = engine.select_best_phase(
phase_scores
)
print()
print("Phase scores:")
print(phase_scores)
print()
print("Decision:")
print(result) |