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.

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.

Quick Start

1. Run the prebuilt monitoring workflow

   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")
   
2. Adapt for bridges, railways, or airports

   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)
   

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How It Works

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

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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

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Common Patterns

Predict structural failure probability from displacement history

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

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

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"])

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Best Practices

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.

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.

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.

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.

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