> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Transportation Template

> Four-agent infrastructure safety pipeline — LiDAR fusion, structural displacement analysis, safety heatmap generation, and predictive maintenance planning.

Process LiDAR scans of tunnels and bridges, calculate structural displacement in millimetres, generate safety heatmaps, and schedule maintenance — all in one workflow call.

Process LiDAR scans of tunnels and bridges, calculate structural displacement in millimetres, generate safety heatmaps, and schedule maintenance — all in one workflow call.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.transportation_template import infrastructure_safety_workflow

result = infrastructure_safety_workflow("Tunnel scan TS-401 — displacement 3.2mm at chainage 1200")
print(result)
```

The user submits LiDAR scan data; agents fuse sensors, score structural risk, and flag safety actions.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Infrastructure Monitoring Workflow"
        IN["📡 LiDAR Sensor"] --> LF["🤖 LiDARFusion"]
        LF --> DC["🤖 DisplacementCalculator"]
        DC --> HG["🤖 HeatmapGenerator"]
        HG --> MP["🤖 MaintenancePlanner"]
        MP --> OUT["✅ Safety Engineer"]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class IN input
    class LF,DC,HG,MP agent
    class OUT output

    classDef agent fill:#8B0000,color:#fff
    classDef tool fill:#189AB4,color:#fff
```

## Quick Start

<Steps>
  <Step title="Run the prebuilt monitoring workflow">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent, tool
    from examples.cookbooks.Industry_Templates.transportation_template import (
        infrastructure_monitoring_workflow,
        InfrastructureType
    )

    tunnels = ["TUN-001", "TUN-002", "TUN-003"]
    traffic = {"peak_volume": 3000, "off_peak_volume": 800, "peak_hours": [7, 9, 17, 19]}

    result = infrastructure_monitoring_workflow(tunnels, InfrastructureType.TUNNEL, traffic)

    print(result["safety_assessments"])
    print(result["emergency_alerts"])
    print(f"Network health score: {result['network_health_score']:.1f}/100")
    ```
  </Step>

  <Step title="Adapt for bridges, railways, or airports">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from examples.cookbooks.Industry_Templates.transportation_template import MultiModalTransportPatterns

    bridge_monitor  = MultiModalTransportPatterns.adapt_for_bridge_monitoring()
    railway_monitor = MultiModalTransportPatterns.adapt_for_railway_tracks()
    runway_monitor  = MultiModalTransportPatterns.adapt_for_airport_runways()

    result = bridge_monitor.start("Check deck deflection and cable tension for BRG-007")
    print(result)
    ```
  </Step>
</Steps>

***

## How It Works

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant Sensor as "LiDAR Sensor"
    participant LiDARFusion
    participant DisplacementCalculator
    participant HeatmapGenerator
    participant MaintenancePlanner
    participant Engineer as "Safety Engineer"

    Sensor->>LiDARFusion: Point cloud data
    LiDARFusion-->>DisplacementCalculator: LiDARScan schema
    DisplacementCalculator-->>HeatmapGenerator: DisplacementAnalysis schema
    HeatmapGenerator-->>MaintenancePlanner: SafetyHeatmap schema
    MaintenancePlanner-->>Engineer: MaintenanceSchedule schema
```

| Agent                    | Responsibility                                                   | SLA     |
| ------------------------ | ---------------------------------------------------------------- | ------- |
| `LiDARFusion`            | Process 5M-point scans from mobile and terrestrial LiDAR         | ≤ 5 min |
| `DisplacementCalculator` | Compare current vs. baseline scan; calculate mm-level settlement | ≤ 2 min |
| `HeatmapGenerator`       | Generate 2D safety grid highlighting critical zones              | ≤ 1 min |
| `MaintenancePlanner`     | Plan preventive, corrective, or emergency maintenance            | ≤ 3 min |

***

## Configuration Options

Pydantic I/O schemas used by this template:

| Schema                 | Key Fields                                                                                                                                          |
| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| `LiDARScan`            | `scan_id`, `infrastructure_id`, `point_cloud_size`, `scan_resolution`, `coverage_area`, `anomaly_points`, `reference_baseline`                      |
| `DisplacementAnalysis` | `analysis_id`, `infrastructure_id`, `max_displacement`, `displacement_vector`, `critical_zones`, `settlement_rate`, `tilt_angle`, `safety_factor`   |
| `SafetyHeatmap`        | `heatmap_id`, `infrastructure_id`, `grid_resolution`, `safety_scores`, `critical_areas`, `overall_safety`, `risk_trends`                            |
| `MaintenanceSchedule`  | `schedule_id`, `infrastructure_id`, `maintenance_type`, `priority_level`, `scheduled_date`, `estimated_duration`, `traffic_impact`, `cost_estimate` |

**Safety levels** (used in `SafetyHeatmap.overall_safety`)

| Level      | `safety_factor` range | Action                                |
| ---------- | --------------------- | ------------------------------------- |
| `safe`     | ≥ 0.9                 | Routine monitoring                    |
| `caution`  | 0.8 – 0.9             | Preventive maintenance within 30 days |
| `warning`  | 0.7 – 0.8             | Corrective maintenance within 7 days  |
| `danger`   | 0.3 – 0.7             | Emergency maintenance within 24 h     |
| `critical` | \< 0.3                | Immediate closure                     |

***

## Common Patterns

**Predict structural failure probability from displacement history**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.transportation_template import PredictiveMaintenancePatterns

displacement_history = [2.0, 2.3, 2.8, 3.5, 4.3]  # mm over past 5 months
environment = {"temp_variation": 30, "humidity": 75}

prob = PredictiveMaintenancePatterns.predict_failure_probability(
    displacement_history, environment
)
print(f"Failure probability: {prob:.2%}")
```

**Optimise maintenance budget across a network**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.transportation_template import PredictiveMaintenancePatterns

conditions = [
    {"id": "TUN-001", "safety_factor": 0.65, "cost": 80000, "recommended_date": "2024-04-01"},
    {"id": "TUN-002", "safety_factor": 0.92, "cost": 30000, "recommended_date": "2024-06-01"},
]

schedule = PredictiveMaintenancePatterns.optimize_maintenance_schedule(
    infrastructure_conditions=conditions,
    budget_constraint=100000.0
)
print(schedule)
```

**Plan traffic flow during maintenance**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from examples.cookbooks.Industry_Templates.transportation_template import TrafficManagementPatterns

traffic_plan = TrafficManagementPatterns.optimize_traffic_flow(
    maintenance_window={"start_time": "22:00", "duration": 8},
    traffic_patterns={"peak_hours": [7, 9, 17, 19]}
)
print(traffic_plan["diversion_routes"])
```

***

## Best Practices

<AccordionGroup>
  <Accordion title="Establish a baseline scan before going live">
    `DisplacementCalculator` computes change relative to a `reference_baseline`. Capture this baseline during construction or last major rehabilitation, and store it in a versioned database alongside each new scan.
  </Accordion>

  <Accordion title="Close to traffic when overall_safety reaches 'danger'">
    The workflow automatically raises an `emergency_alert` with `immediate_action: close_to_traffic` for danger/critical safety levels. Wire this alert to your traffic management system to trigger lane closures without human delay.
  </Accordion>

  <Accordion title="Schedule LiDAR scans during low-traffic windows">
    Scan accuracy degrades when vehicles pass through the scan envelope. Use `TrafficManagementPatterns.optimize_traffic_flow()` to identify overnight windows with minimal traffic before dispatching the LiDAR crew.
  </Accordion>

  <Accordion title="Use network_health_score for KPI reporting">
    The `network_health_score` (0–100) aggregates safety factors across all monitored structures. Report this monthly to asset managers — a score below 70 should trigger a portfolio-level maintenance budget review.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Industry Templates Overview" icon="building-2" href="/docs/features/industry-templates/overview">
    Hub page — choose the right template and understand cross-industry reuse.
  </Card>

  <Card title="Manufacturing Template" icon="factory" href="/docs/features/industry-templates/manufacturing">
    Order processing, inventory management, and quality inspection agents.
  </Card>
</CardGroup>