AI Workflow for Automated Airport Terminal Passenger Flow Simulation

Static BIM models fail to predict operational bottlenecks, leading to costly post-construction retrofits. This custom workflow automates dynamic passenger flow simulation by ingesting flight schedules and design models to generate thousands of unique passenger itineraries. AI agents simulate check-in, security, and boarding behaviors, identifying congestion points and quantifying their impact on dwell time and retail potential. The result is data-driven design validation that reduces passenger dissatisfaction risk and informs revenue-generating space allocation before breaking ground.

Implementation integrates with Autodesk Revit or Navisworks via API, using a LangGraph-based orchestrator to manage agent logic and simulation cycles. The workflow includes approval gates for major design changes triggered by simulation results, ensuring human oversight. Outputs are visual heatmaps and congestion reports fed directly into the CDE, enabling architects to iterate on lounge sizing, checkpoint staffing, and retail placement. This turns BIM from a geometric repository into a predictive operations tool, directly linking design decisions to passenger throughput and non-aeronautical revenue forecasts.

This workflow automates the bottleneck analysis of terminal designs by simulating thousands of passenger itineraries. It replaces manual, spreadsheet-based modeling that cannot scale to evaluate dynamic interactions between security queues, retail dwell times, and gate boarding. The operational upside comes from reducing passenger congestion by 15-25%, which directly improves passenger satisfaction scores and non-aero revenue capture by optimizing retail placement and dwell zones based on simulated foot traffic patterns.

Implementation requires integrating the orchestrator with the BIM Common Data Environment (CDE), such as Autodesk BIM 360, via its API to pull live geometry. Passenger agents are instantiated with behavioral parameters from historical airport data. The simulation outputs visual heatmaps and KPIs like average wait time, which are routed through a human-in-the-loop approval gate in a dashboard like Power BI before any design recommendations are committed. Governance controls include simulating edge cases (e.g., flight cancellations) and validating agent behavior against real-world sensor data post-construction.

This workflow automates the bottleneck of manually modeling thousands of passenger itineraries to predict terminal congestion. It directly informs architectural decisions on checkpoint sizing, gate placement, and retail positioning, translating to improved passenger throughput and non-aeronautical revenue. Savings come from avoiding costly post-construction retrofits and from data-driven design that maximizes commercial yield per square meter. Implementation requires integrating the BIM model (Revit, Archicad) with live flight schedule APIs and agent-based simulation engines like AnyLogic or Mesa.

Phase 1 establishes core simulation and BIM integration, delivering baseline congestion analysis. Phase 2 adds real-time data feeds and retail revenue modeling, while Phase 3 implements continuous validation against operational benchmarks. Controls include human review gates for major design changes, anomaly detection for outlier simulations, and audit trails linking simulation parameters to final design decisions. The architecture must be deployed within the project's Common Data Environment (CDE), such as BIM 360, to ensure model synchronization and team access to live insights.

This workflow automates the bottleneck of manually evaluating terminal designs against dynamic passenger scenarios. By simulating thousands of agent-based itineraries against the live BIM, it identifies congestion points at security, retail, and gates, directly informing capital investments to improve passenger throughput and non-aero revenue. The operational upside comes from compressing design validation cycles from months to days, enabling data-driven trade-offs between architectural intent and operational performance before construction begins.

Implementation requires integrating the simulation engine (e.g., AnyLogic, Pathfinder) with the CDE (like BIM 360) via API. A controlled rollout starts with a single terminal module, where design teams validate simulation outputs against known historical pinch points. Governance is enforced through approval gates in the orchestrator, mandating human review of scenario parameters and result anomalies before findings are committed to the master model. Observability dashboards track simulation accuracy over time, triggering model retraining when passenger behavior patterns drift.