🤖 AI Prediction Service

AI prediction technology — Traffic flow forecasting, flood warnings, cascade effects

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Purpose

AI Prediction Service provides intelligent forecasts for CivicTwin AI:

1. 🚗 Traffic Flow Prediction

   • Forecast 15–60 minutes ahead
   • Identify forming hotspots
   • Suggest traffic signal adjustments

2. 💧 Flood Warning

   • Combines weather data + water level sensors
   • Early warning 2–6 hours in advance
   • Evacuation recommendations, safe routing

3. 📊 Cascade Effects

   • Simulate how an incident propagates through the system
   • Forecast which areas will be affected

4. 💰 Socioeconomic Impact

   • Forecast GDP, employment, social equity
   • Used for scenario simulation

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Technology Stack

Component	Technology
AI Core	Amazon Bedrock + Amazon Nova
Time Series	TensorFlow/PyTorch (LSTM, Transformer)
Framework	Python FastAPI
ML Ops	Kubernetes, MLflow
Monitoring	Prometheus + Grafana

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Prediction Models

1. Traffic Flow Prediction (LSTM)

Input:

• Traffic history (past 7 days, hourly)
• Current state (from Digital Twin)
• Events (accidents, road works, etc.)
• Calendar features (day of week, holidays)

Output:

• Traffic flow forecast (15, 30, 60 minutes ahead)
• Confidence score (0–100%)

Accuracy: RMSE < 15% (with sufficient data)

2. Flooding Risk (Hybrid)

Input:

• Weather data (from OpenWeatherMap API)
• Water level sensors (real-time from IoT)
• Rainfall history (past 3 months)
• Geographic characteristics (elevation, gradient)

Output:

• Risk level: NONE, LOW, MEDIUM, HIGH, CRITICAL
• Affected zones (list of zone IDs)
• Recommended actions (evacuate, close roads, ...)

Accuracy: Precision > 85% (detecting true flooding)

3. Cascade Effects (Agent-Based)

Input:

• Initial incident (location, type)
• Current city state
• Network topology (graph)

Output:

• Predicted impact zones
• Timeline of effects
• Severity estimation

4. Socioeconomic Impact (Nova LLM)

Input:

• Scenario description (text)
• Context (city data, demographics)

Output:

• Impact assessment (structured JSON):

  {
    "economic": {
      "gdp_change": 2.5,
      "employment": 1200,
      "business_hours_saved": 45000
    },
    "social": {
      "accessibility": 35,
      "equity_score": 0.65,
      "job_creation": "medium"
    },
    "environmental": {
      "co2_reduction": 12.5,
      "air_quality": 18
    }
  }

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API Endpoints

Traffic Prediction

POST /api/predictions/traffic/{roadId}
{
  "horizonMinutes": 60
}

Response:
{
  "roadId": "...",
  "predictions": [
    { "timestamp": "2026-03-31T11:00:00Z", "flow": 1200, "confidence": 92 },
    { "timestamp": "2026-03-31T11:15:00Z", "flow": 1350, "confidence": 89 }
  ],
  "alert": "System overload expected at 11:00, recommend signal optimization"
}

Flooding Alert

GET /api/predictions/flooding/city

Response:
{
  "overallRisk": "MEDIUM",
  "affectedZones": [
    {
      "zoneId": "...",
      "riskLevel": "HIGH",
      "affectedArea": 1250,
      "recommendedActions": ["evacuate", "close_roads_3_4"],
      "timeToFlooding": 180
    }
  ],
  "weatherForecast": {
    "rainfall": 120,
    "intensity": "heavy",
    "duration": 240
  }
}

Cascade Effects

POST /api/predictions/cascade
{
  "incidentType": "traffic_accident",
  "location": { "lat": 16.0544, "lon": 108.2022 },
  "severity": "severe"
}

Response:
{
  "primaryImpactZones": [...],
  "secondaryImpactZones": [...],
  "timeline": {
    "5mins": "Traffic congestion at direct roads",
    "15mins": "Spread to neighboring intersections",
    "30mins": "Alternative routes overwhelmed"
  }
}

Socioeconomic Impact

POST /api/predictions/impact
{
  "scenarioId": "...",
  "timeframe": 10
}

Response:
{
  "economic": { "gdp_change": 2.5 },
  "social": { "accessibility": 35 },
  "environmental": { "co2_reduction": 12.5 },
  "explanation": "This project improves healthcare accessibility by 35% because... [AI generated]"
}

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Training & Optimization

Data Pipeline

Raw Data (IoT, APIs)
    ↓
Data Validation & Cleaning
    ↓
Feature Engineering
    ↓
Train/Test Split (80/20)
    ↓
Model Training (LSTM, etc.)
    ↓
Model Validation & Evaluation
    ↓
Hyperparameter Tuning
    ↓
Production Deployment

Retraining Schedule

• Traffic Model: Daily (12 AM UTC)
• Flooding Model: When rainfall > threshold
• Cascade Model: Weekly (Sundays 2 AM)

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Usage Examples

From Dashboard Service

// Get traffic forecast to display warnings
const predictions = await predictionService.getTrafficPrediction(roadId);
if (predictions.alert) {
  displayWarning(predictions.alert);
}

From Notification Service

// Send flood warnings to citizens
const flooding = await predictionService.getFloodingAlert();
if (flooding.overallRisk === 'CRITICAL') {
  notificationService.broadcastAlert({
    title: "Emergency Flood Warning",
    zones: flooding.affectedZones,
    actions: ["Evacuate", "Avoid these roads"]
  });
}

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Deployment

Docker

FROM python:3.11-slim
WORKDIR /app
RUN pip install fastapi uvicorn tensorflow torch
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8002
CMD ["uvicorn", "main:app", "--host", "0.0.0.0"]

Environment Variables

AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
DIGITAL_TWIN_API=http://digital-twin:3001
KAFKA_BROKER=kafka:9092
MODEL_PATH=/models

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Performance Targets

Metric	Target
Traffic prediction RMSE	< 15%
Flooding alert precision	> 85%
Cascade prediction latency	< 30 seconds
API response time	< 500ms
Model inference time	< 1 second

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Related Services

• Digital Twin Service – Data source
• Simulation Service – Uses forecasts for simulation
• Notification Service – Distributes alerts