# Passenger Flow Optimization Passenger flow is the terminal-side equivalent of [[Aircraft Turnaround]] on the airside. The unit of analysis shifts from aircraft to people, but the underlying physics are the same: constrained capacity, variable demand, precedence dependencies, and multi-stakeholder coordination. Four levers for optimizing passenger flow: **Prediction.** Using historical data and airline load information to forecast demand at each touchpoint (check-in, security, immigration, gates) by hour. AI models predict surges; simulation models test resource allocation scenarios. The bottleneck is data quality: airlines sharing accurate passenger load data is still inconsistent. **Real-time visibility.** Shifts in flight timings create unplanned demand surges. People-counting sensors, CCTV analytics, and mobile device signals provide real-time demand information. The challenge: converting raw sensor data into actionable queue length and wait time estimates fast enough to respond. **Dynamic resource allocation.** With demand predictions and real-time visibility, the system recommends how many security lanes, check-in counters, and immigration desks to staff per time window. Static allocation (fixed resources all day) wastes capacity during troughs and creates bottlenecks during peaks. Dynamic allocation captures 20-30% more throughput from the same physical infrastructure. **Arrival staggering.** The newest lever: working with airlines to recommend optimal passenger arrival times, spreading demand more evenly across the day. This inverts the traditional model where airports passively accept whatever arrival pattern passengers choose. The commercial link: faster processing through touchpoints means more time in the retail and F&B zones. Airports that reduce security wait times by 10 minutes gain 10 minutes of concession browsing per passenger. At scale, this is worth millions in additional revenue. See [[The Airport Concession Economy]]. Related: [[Airport Operations MOC]], [[Total Airport Management]], [[Human-in-the-Loop Systems]], [[Simulation-Based Optimization]] --- Tags: #deeptech