When we think about gate and resource planning, it’s easy to focus on the aircraft side of things; tight schedules, turnaround times, parking spots. But what happens at the gate, and check-in and baggage reclaim, affects passengers just as much as operations.
This is the second article in my series on how smarter resource management can improve airport outcomes. In the first, I looked at how tools can boost airside efficiency. Now, I want to talk about how those same decisions impact the people moving through your terminal and how you can plan for both at once.
From queue chaos to calm check-ins
We’ve all seen it. Long lines stretching through check-in areas during busy mornings or holiday spikes. The Resource Management System says there are enough desks open, but reality says otherwise. That’s because traditional plans based on rules like “five desks for three hours before departure” don’t reflect when people actually show up. They also don’t help when there’s a crash on the motorway or a train delay.
This is where AI and machine learning can really help. Not as buzzwords or pilots, but as tools that forecast actual show-up patterns based on season, flight type, and even demographics. You get smarter counter allocations that better match the likely passenger flow.
And when the day throws you a curveball, live data feeds update those predictions in real time and suggest or automate desk changes to keep things moving. For example, a premium airline might open extra counters to speed up the process, while a low-cost carrier might extend desk hours to absorb late arrivals.
We’ve also seen these tools used to rethink check-in layouts. Small adjustments in where kiosks and queues are placed can ease congestion and smooth passenger flow before it even becomes a problem.
Smarter gate planning means smoother journeys
Gates might just look like parking spots for planes, but they’re a big part of the passenger experience. When a flight’s delayed because the gate isn’t ready or there’s a last-minute gate change, it’s not just an operational hiccup , it’s a frustrating moment for travellers.
By linking live passenger data with prediction models, airports get better at planning gates. That means knowing how long people usually take to reach their gate, spotting which gates tend to get crowded, and identifying where connections might be tight.
With that insight, planners can avoid scrambling to move flights around at the last minute, keep departures on time, and give ground staff the space to work smoothly. So there is less crowding at gates, fewer missed connections and flights leaving when they should.
Thinking beyond the stand for transfers and arrivals
Transfers can be tricky. Gate allocations become critical when a passenger has only 40 minutes to get across the terminal. Even without full transfer manifests, machine learning can flag likely tight connections and recommend better gate assignments to cut down travel times, helping both punctuality and passenger experience.
Arrivals bring their own challenges. Forecasting when passengers will reach baggage claim by flight, plus understanding how long bags take from plane to belt, lets planners time belt assignments and ground handlers prioritise their bag handling. This means bags are ready when passengers arrive, not waiting around.
It’s not about choosing between efficiency and experience
At Veovo, we’re helping airports connect live data. flights, passenger movement, wait times, with dynamic forecasting, and putting that insight right within our resource management system. The aim is to enable plans to flex from seasonal schedules right through to day-of operations, continually balancing capacity, operational needs, and passenger comfort.
It’s not a trade-off between on-time performance and experience. It’s about making informed decisions, knowing the trade-offs, and adjusting based on what matters most that day.
If you’re working on making the shift from traditional RMS to something more predictive and people-focused, I’d love to hear how it’s going at your airport.
