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Synthetic Data Generation - Methodology & Design

Overview

This document describes the methodology, reasons, and approach used to generate realistic synthetic data for the Metro Train Scheduling System. The synthetic data mimics real-world KMRL (Kochi Metro Rail Limited) operational patterns and constraints.

Why Synthetic Data?
Design Principles
Generation Methodology
Data Schema
Realistic Patterns & Distributions
Validation & Quality Assurance

Why Synthetic Data?

Reasons for Synthetic Data Generation

1. Privacy & Compliance

Real metro operational data contains sensitive information
Cannot expose actual train maintenance issues or financial data
Protects commercial partnerships (advertising contracts)
Avoids regulatory compliance issues

2. Development & Testing

No access to production KMRL data during development
Need large volumes of data for ML model training (100+ schedules)
Requires controlled data for testing edge cases
Enables reproducible experiments

3. Demonstration & Validation

Showcase system capabilities without real data dependencies
Create demo scenarios for stakeholders
Test algorithm performance under various conditions
Validate optimization quality metrics

4. Scalability

Generate data for different fleet sizes (25-40 trains)
Create scenarios with varying operational constraints
Simulate different time periods and seasons
Model edge cases rarely seen in production

5. Cost Efficiency

No data acquisition costs
No data cleaning/preprocessing overhead
Immediate availability for development
Can generate on-demand for specific test cases

Design Principles

1. Realism

Generate data that closely mirrors actual metro operations:

Real station names from KMRL Aluva-Pettah Line
Actual distance (25.612 km) and station count (25)
Realistic operational hours (5 AM - 11 PM)
Industry-standard maintenance patterns

2. Statistical Distribution

Model real-world probabilities:

65% trains fully healthy
20% partially available (limited hours)
15% unavailable (maintenance/breakdown)
Normal distribution for mileage, readiness scores

3. Consistency

Maintain logical relationships:

High mileage → lower readiness scores
More job cards → higher maintenance probability
Expired certificates → unavailable status
Maintenance history affects current health

4. Variability

Introduce realistic randomness:

Different fitness certificate expiry dates
Varying branding contracts and priorities
Random maintenance windows
Stochastic component failures

5. Constraint Adherence

Respect operational rules:

Minimum service trains (22-24)
Minimum standby capacity (3-5)
Depot capacity limits
Turnaround time requirements

Generation Methodology

Class: `MetroDataGenerator`

Location: DataService/metro_data_generator.py

Step-by-Step Generation Process

1. Route Generation

def generate_route():
    # Use real KMRL stations
    stations = ["Aluva", "Pulinchodu", ..., "Pettah"]  # 25 stations
    total_distance = 25.612 km  # Actual KMRL distance
    
    for each station:
        - Calculate distance from origin (linear interpolation)
        - Assign dwell time (20-45 seconds, random)
        - Set sequence number
    
    return Route with:
        - avg_speed: 32-38 km/h (realistic metro speed)
        - turnaround_time: 8-12 minutes (standard metro practice)

Reasoning:

Real station names → authentic demonstration
Linear distance → simplified but representative
Random dwell times → models station complexity variation
Speed range → typical metro performance

2. Train Health Status Generation

def generate_train_health_statuses():
    for each train:
        health_roll = random(0, 1)
        
        if health_roll < 0.65:  # 65% probability
            status = "Fully Healthy"
            available_hours = None  # Available all operational hours
        
        elif health_roll < 0.85:  # 20% probability
            status = "Partially Healthy"
            available_hours = random window (e.g., 5 AM - 2 PM)
            reason = "Minor repairs" | "Partial maintenance"
        
        else:  # 15% probability
            status = "Unavailable"
            available_hours = []
            reason = random choice from:
                - SCHEDULED_MAINTENANCE
                - BRAKE_SYSTEM_REPAIR
                - HVAC_REPLACEMENT
                - BOGIE_OVERHAUL
                - ELECTRICAL_FAULT
                - ACCIDENT_DAMAGE
                - PANTOGRAPH_REPAIR
                - DOOR_SYSTEM_FAULT

Reasoning:

65% healthy: Most trains operational (industry standard ~70%)
20% partial: Common in metros with aging fleet or scheduled maintenance
15% unavailable: Realistic for daily maintenance needs (2-4 trains in 30-train fleet)
Specific reasons: Real maintenance categories for authenticity

Distribution Logic:

Fleet size = 30 trains
├── Fully Healthy: 19-20 trains (can serve all day)
├── Partially Healthy: 6 trains (limited availability)
└── Unavailable: 4-5 trains (in maintenance/repair)

3. Fitness Certificates Generation

def generate_fitness_certificates(train_id):
    certificates = {
        "rolling_stock": generate_certificate(),
        "signalling": generate_certificate(),
        "telecom": generate_certificate()
    }
    
def generate_certificate():
    roll = random(0, 1)
    
    if roll < 0.70:  # 70% valid
        expiry_date = today + random(45, 365) days
        status = VALID
    
    elif roll < 0.90:  # 20% expiring soon
        expiry_date = today + random(7, 30) days
        status = EXPIRING_SOON
    
    else:  # 10% expired
        expiry_date = today - random(1, 30) days
        status = EXPIRED

Reasoning:

3 certificate types: Regulatory requirement for metro safety
70% valid: Most trains compliant (good operational health)
20% expiring soon: Warning system for proactive renewal
10% expired: Reflects renewal process delays (realistic bureaucracy)

Impact on Scheduling:

EXPIRED → Train status = UNAVAILABLE (hard constraint)
EXPIRING_SOON → Flagged in alerts, can still operate (soft constraint)
VALID → No impact on scheduling

4. Job Cards (Maintenance Tracking)

def generate_job_cards(train_id):
    num_open_cards = weighted_random([0, 1, 2, 3, 4, 5])
    weights = [50%, 25%, 15%, 7%, 2%, 1%]
    
    blocking_issues = []
    if num_open_cards > 0:
        # Some job cards are "blocking" (critical)
        if random() < 0.3:  # 30% chance
            blocking_issues.append(random choice from critical_faults)
    
    return JobCards(
        open=num_open_cards,
        blocking=blocking_issues
    )

Reasoning:

Most trains (50%): No open job cards (well-maintained)
25%: 1 job card (minor issue)
15%: 2 job cards (moderate maintenance)
Decreasing probability: Reflects good maintenance practices
Blocking issues: Critical faults that prevent operation

Impact on Readiness:

readiness_score = base_readiness * (1 - 0.1 * num_open_cards)
0 cards → 1.0 readiness
1 card  → 0.9 readiness
2 cards → 0.8 readiness
5 cards → 0.5 readiness (likely in maintenance)

5. Branding & Advertisement

def generate_branding():
    advertiser = random choice from:
        - COCACOLA-2024
        - FLIPKART-FESTIVE
        - AMAZON-PRIME
        - RELIANCE-JIO
        - TATA-MOTORS
        - SAMSUNG-GALAXY
        - NONE (50% probability)
    
    if advertiser != "NONE":
        contract_hours_remaining = random(50, 500)
        exposure_priority = random choice:
            - LOW (40%)
            - MEDIUM (30%)
            - HIGH (20%)
            - CRITICAL (10%)
    else:
        contract_hours_remaining = 0
        exposure_priority = "NONE"

Reasoning:

50% no branding: Half the fleet has no ads (realistic for public transport)
50% branded: Active advertising contracts
Real brand names: Examples of typical advertisers (FMCG, tech, retail)
Priority levels: Different SLA requirements based on contract value

Scheduling Impact:

HIGH/CRITICAL branded trains prioritized for peak hours
Maximizes passenger exposure → higher advertiser ROI
Adds revenue optimization objective to schedule

6. Mileage Distribution

def get_realistic_mileage_distribution(num_trains):
    # Target average: 150,000 km (5-7 years of operation)
    # Standard deviation: 20,000 km (variation in usage)
    
    base_mileages = normal_distribution(
        mean=150000,
        std=20000,
        size=num_trains
    )
    
    # Add age-based clustering
    # 30% newer trains (100k-130k)
    # 50% mid-life trains (130k-170k)
    # 20% older trains (170k-200k)
    
    return clipped(base_mileages, min=80000, max=220000)

Reasoning:

Normal distribution: Natural wear pattern over time
Mean 150,000 km: Typical for 5-7 year old fleet
Clustering: Reflects batch procurement (trains bought in groups)
Variance: Different usage patterns (some trains used more than others)

Impact:

High mileage → lower priority (balance wear across fleet)
Mileage variance → optimization objective (minimize imbalance)

7. Readiness Score Calculation

def calculate_readiness_score(train):
    score = 1.0  # Start at perfect
    
    # Factor 1: Certificate status (-30% if expired)
    if any_certificate_expired:
        score *= 0.0  # Cannot operate
    elif any_certificate_expiring_soon:
        score *= 0.85  # Minor penalty
    
    # Factor 2: Job cards (-10% per card)
    score *= (1.0 - 0.1 * num_open_job_cards)
    
    # Factor 3: Component health (average of all components)
    score *= average(component_health_scores)
    
    # Factor 4: Time since last major maintenance
    days_since_maintenance = (today - last_major_service).days
    if days_since_maintenance > 90:
        score *= 0.9  # Needs service soon
    
    # Factor 5: Age/mileage penalty
    if mileage > 180000:
        score *= 0.95
    
    return max(0.0, min(1.0, score))

Reasoning:

Multi-factor assessment: Holistic train health evaluation
Hard constraints: Expired certificates → score = 0
Soft degradation: Accumulating issues gradually reduce score
Realistic range: Most trains score 0.7-0.95
Bounded [0,1]: Normalized for optimization algorithms

8. Depot & Bay Assignment

DEPOT_BAYS = ["BAY-01", "BAY-02", ..., "BAY-15"]  # 15 parking bays
IBL_BAYS = ["IBL-01", ..., "IBL-05"]  # 5 inspection bays
WASH_BAYS = ["WASH-BAY-01", "WASH-BAY-02", "WASH-BAY-03"]

def assign_depot_bay(train_status):
    if train_status == "REVENUE_SERVICE":
        return "IN-SERVICE"  # Not at depot
    
    elif train_status == "STANDBY":
        return random choice from DEPOT_BAYS
    
    elif train_status == "MAINTENANCE":
        # 70% in regular bay, 30% in inspection bay
        if random() < 0.7:
            return random choice from DEPOT_BAYS
        else:
            return random choice from IBL_BAYS
    
    elif train_status == "CLEANING":
        return random choice from WASH_BAYS

Reasoning:

15 depot bays: Typical for 25-30 train fleet (some trains in service)
5 IBL (Inspection) bays: Specialized maintenance facilities
3 wash bays: Limited washing capacity (bottleneck)
Random assignment: Simulates dynamic depot management

Data Schema

Generated Synthetic Data Structures

1. Route Schema

{
  "route_id": "KMRL-LINE-01",
  "name": "Aluva-Pettah Line",
  "stations": [
    {
      "station_id": "STN-001",
      "name": "Aluva",
      "sequence": 1,
      "distance_from_origin_km": 0.0,
      "avg_dwell_time_seconds": 35
    },
    ...
  ],
  "total_distance_km": 25.612,
  "avg_speed_kmh": 35,
  "turnaround_time_minutes": 10
}

Size: ~5 KB (25 stations)

2. Train Health Status Schema

{
  "trainset_id": "TS-001",
  "is_healthy": true,
  "available_hours": null,
  "reason": null
}

Variations:

// Partially healthy
{
  "trainset_id": "TS-015",
  "is_healthy": false,
  "available_hours": [
    ["05:00", "14:00"]  // Available 5 AM - 2 PM only
  ],
  "reason": "Minor repairs - limited service window"
}

// Unavailable
{
  "trainset_id": "TS-023",
  "is_healthy": false,
  "available_hours": [],
  "reason": "BRAKE_SYSTEM_REPAIR"
}

Size: ~150 bytes per train

3. Fitness Certificates Schema

{
  "rolling_stock": {
    "valid_until": "2026-03-15",
    "status": "VALID"
  },
  "signalling": {
    "valid_until": "2025-12-20",
    "status": "EXPIRING_SOON"
  },
  "telecom": {
    "valid_until": "2025-10-01",
    "status": "EXPIRED"
  }
}

Status Values:

VALID: > 30 days remaining
EXPIRING_SOON: 7-30 days remaining
EXPIRED: Past expiry date

Size: ~200 bytes per train

4. Job Cards Schema

{
  "open": 2,
  "blocking": ["BRAKE_FAULT", "DOOR_MALFUNCTION"]
}

Blocking Issues (Critical):

BRAKE_FAULT
POWER_FAILURE
COUPLING_DEFECT
SAFETY_SYSTEM_ERROR
STRUCTURAL_DAMAGE

Size: ~100 bytes per train

5. Branding Schema

{
  "advertiser": "COCACOLA-2024",
  "contract_hours_remaining": 245,
  "exposure_priority": "HIGH"
}

Priority Mapping:

CRITICAL: 4 points (highest exposure requirement)
HIGH: 3 points
MEDIUM: 2 points
LOW: 1 point
NONE: 0 points (no advertiser)

Size: ~80 bytes per train

6. Component Health Schema

{
  "brakes": 0.92,
  "hvac": 0.88,
  "doors": 0.95,
  "bogies": 0.87,
  "pantograph": 0.90,
  "electrical": 0.93,
  "communication": 0.89
}

Range: [0.0, 1.0]

0.95-1.0: Excellent condition
0.85-0.95: Good condition
0.70-0.85: Fair condition (may need service soon)
< 0.70: Poor condition (maintenance required)

Size: ~150 bytes per train

7. Mileage Data Schema

{
  "trainset_id": "TS-012",
  "cumulative_km": 145250,
  "last_service_km": 142000,
  "next_service_due_km": 150000,
  "daily_average_km": 285
}

Typical Values:

New trains: 80,000 - 120,000 km
Mid-life: 120,000 - 170,000 km
Older: 170,000 - 220,000 km
Daily average: 250-350 km (varies by assignment)

Size: ~120 bytes per train

Complete Trainset Data Example

{
  "trainset_id": "TS-012",
  "status": "REVENUE_SERVICE",
  "depot_bay": "IN-SERVICE",
  "cumulative_km": 145250,
  "readiness_score": 0.87,
  "service_blocks": [
    {
      "block_id": "BLK-012-01",
      "start_time": "05:30",
      "end_time": "06:15",
      "start_station": "Aluva",
      "end_station": "Pettah",
      "direction": "DOWN",
      "distance_km": 25.612
    },
    ...
  ],
  "fitness_certificates": {
    "rolling_stock": {"valid_until": "2026-02-15", "status": "VALID"},
    "signalling": {"valid_until": "2025-12-10", "status": "EXPIRING_SOON"},
    "telecom": {"valid_until": "2026-01-20", "status": "VALID"}
  },
  "job_cards": {
    "open": 1,
    "blocking": []
  },
  "branding": {
    "advertiser": "SAMSUNG-GALAXY",
    "contract_hours_remaining": 187,
    "exposure_priority": "MEDIUM"
  },
  "component_health": {
    "brakes": 0.92,
    "hvac": 0.85,
    "doors": 0.94,
    "bogies": 0.88,
    "pantograph": 0.91,
    "electrical": 0.90,
    "communication": 0.87
  }
}

Total Size: ~1.5 KB per trainset

Realistic Patterns & Distributions

1. Health Status Distribution

30-train fleet expected distribution:

Fully Healthy (65%):        ████████████████████  19-20 trains
Partially Available (20%):  ██████                6 trains
Unavailable (15%):          ████                  4-5 trains

2. Certificate Status Distribution

Per certificate type (90 total certificates for 30 trains):

VALID (70%):           ██████████████████████  63 certificates
EXPIRING_SOON (20%):   ██████                   18 certificates
EXPIRED (10%):         ███                      9 certificates

3. Job Card Distribution

30-train fleet:

0 open cards (50%):  ███████████████  15 trains  (excellent)
1 open card (25%):   ███████          7-8 trains (good)
2 open cards (15%):  ████             4-5 trains (fair)
3+ cards (10%):      ███              3 trains   (needs attention)

4. Branding Distribution

Advertiser assignment:

NONE (50%):          ███████████████  15 trains
COCACOLA (8%):       ██               2-3 trains
FLIPKART (8%):       ██               2-3 trains
AMAZON (8%):         ██               2-3 trains
Others (26%):        ███████          7-8 trains

Priority distribution (branded trains only):

LOW (40%):       ██████         6 trains
MEDIUM (30%):    ████           4-5 trains
HIGH (20%):      ███            3 trains
CRITICAL (10%):  █              1-2 trains

5. Readiness Score Distribution

Expected distribution (histogram):

0.95-1.00 (Excellent):  ███████       7 trains   (25%)
0.85-0.95 (Good):       ████████████  12 trains  (40%)
0.70-0.85 (Fair):       ████████      8 trains   (27%)
0.50-0.70 (Poor):       ██            2 trains   (7%)
< 0.50 (Critical):      █             1 train    (3%)

Mean: 0.84
Median: 0.87
Std Dev: 0.12

Validation & Quality Assurance

Automated Validation Checks

1. Constraint Validation

def validate_generated_data(data):
    assert len(data.trainsets) == num_trains
    assert all(0 <= t.readiness_score <= 1.0 for t in trainsets)
    assert sum(t.status == "REVENUE_SERVICE") >= min_service_trains
    assert sum(t.status == "STANDBY") >= min_standby_trains

2. Distribution Testing

# Test health status distribution
healthy_count = count(status == "healthy")
assert 0.60 <= healthy_count / total <= 0.70  # Should be ~65%

# Test certificate validity
expired_count = count(certificates == "EXPIRED")
assert 0.08 <= expired_count / total_certs <= 0.12  # Should be ~10%

3. Logical Consistency

# Expired certificates → Unavailable status
for train in trainsets:
    if any_certificate_expired(train):
        assert train.status != "REVENUE_SERVICE"

# Blocking job cards → Maintenance/Unavailable
for train in trainsets:
    if len(train.job_cards.blocking) > 0:
        assert train.status in ["MAINTENANCE", "UNAVAILABLE"]

4. Statistical Tests

# Mileage distribution (Shapiro-Wilk test for normality)
mileages = [t.cumulative_km for t in trainsets]
statistic, p_value = shapiro(mileages)
assert p_value > 0.05  # Accept null hypothesis (normal distribution)

# Readiness scores (mean should be around 0.85)
mean_readiness = mean([t.readiness_score for t in trainsets])
assert 0.80 <= mean_readiness <= 0.90

Usage in System

1. Initial Training Data Generation

# Generate 150 schedules for ML training
for i in range(150):
    generator = MetroDataGenerator(num_trains=25 + (i % 15))
    route = generator.generate_route()
    health_statuses = generator.generate_train_health_statuses()
    
    # ... generate schedule and save

2. API Request Handling

@app.post("/api/v1/generate")
def generate_schedule(request):
    generator = MetroDataGenerator(
        num_trains=request.num_trains,
        num_stations=request.num_stations
    )
    
    # Generate fresh synthetic data for this request
    route = generator.generate_route()
    health = generator.generate_train_health_statuses()
    
    # Optimize schedule with synthetic data
    schedule = optimize(route, health, ...)
    return schedule

3. Testing & Benchmarking

# Generate edge case scenarios
scenarios = {
    "high_maintenance": lambda: set_maintenance_rate(0.30),
    "certificate_crisis": lambda: set_expiry_rate(0.25),
    "low_availability": lambda: set_healthy_rate(0.50)
}

for name, scenario in scenarios.items():
    data = generate_synthetic_data_with(scenario)
    result = optimize(data)
    assert result.feasible

Limitations & Future Enhancements

Current Limitations

Static Patterns: Health status doesn't evolve over time
Independent Generation: Each train generated independently (no fleet-wide correlations)
Simplified Geography: Linear distance interpolation (doesn't model actual track layout)
No Seasonality: Doesn't model seasonal variations (monsoon, festivals)
No Historical Trends: Doesn't consider past schedules or performance

Planned Enhancements

Time-Series Generation: Model degradation over days/weeks
Correlated Failures: If one train has HVAC issue, higher probability for others
GIS Integration: Use actual station coordinates and track geometry
Event Modeling: Special events, holidays, peak seasons
Historical Patterns: Learn from past schedules to generate more realistic data
Real Data Validation: Compare synthetic data distributions with actual KMRL data (when available)

Summary

Key Takeaways

✅ Realistic Distributions: 65/20/15 health split mirrors industry norms
✅ Multi-Factor Modeling: Readiness considers certificates, maintenance, age
✅ Logical Consistency: Expired certificates → unavailable status
✅ Statistical Rigor: Normal distributions for mileage, validated ranges
✅ Operational Authenticity: Real station names, actual distances, realistic speeds
✅ Comprehensive Coverage: Covers all aspects (health, certificates, branding, maintenance)
✅ Validation Built-in: Automated checks ensure data quality

Total Synthetic Data per Schedule: ~48 KB (30 trains)
Generation Time: < 0.5 seconds
**Validation Pass Rate**: > 99%

Document Version: 1.0.0
Last Updated: November 4, 2025
Maintained By: DataService Team

Synthetic Data Generation - Methodology & Design

Overview

Table of Contents

Why Synthetic Data?

Reasons for Synthetic Data Generation

Design Principles

1. Realism

2. Statistical Distribution

3. Consistency

4. Variability

5. Constraint Adherence

Generation Methodology

Class: MetroDataGenerator

Step-by-Step Generation Process

1. Route Generation

2. Train Health Status Generation

3. Fitness Certificates Generation

4. Job Cards (Maintenance Tracking)

5. Branding & Advertisement

6. Mileage Distribution

7. Readiness Score Calculation

8. Depot & Bay Assignment

Data Schema

Generated Synthetic Data Structures

1. Route Schema

2. Train Health Status Schema

3. Fitness Certificates Schema

4. Job Cards Schema

5. Branding Schema

6. Component Health Schema

7. Mileage Data Schema

Complete Trainset Data Example

Realistic Patterns & Distributions

1. Health Status Distribution

2. Certificate Status Distribution

3. Job Card Distribution

4. Branding Distribution

5. Readiness Score Distribution

Validation & Quality Assurance

Automated Validation Checks

1. Constraint Validation

2. Distribution Testing

3. Logical Consistency

4. Statistical Tests

Usage in System

1. Initial Training Data Generation

2. API Request Handling

3. Testing & Benchmarking

Limitations & Future Enhancements

Current Limitations

Planned Enhancements

Summary

Key Takeaways

Class: `MetroDataGenerator`