Traffic analysis is the foundation of every well-sized elevator system. This guide explains up-peak demand, interval targets, and how engineers validate designs before installation.
Elevator traffic analysis answers a simple but critical question: will the lift group cope when the building is busy? Whether you are designing a new tower or modernizing an existing installation, analytical methods help you justify car numbers, capacities, speeds and control strategies before steel goes into the shaft.
Why traffic analysis matters
Poorly sized systems create long average waiting times (AWT), crowded lobbies and dissatisfied tenants. Consultants and building owners increasingly expect quantified performance targets—often expressed as handling capacity during the morning up-peak and an acceptable interval between arriving cars.
- Up-peak — typically the worst case for office buildings when occupants arrive over 5–10 minutes.
- Lunch peak — two-way traffic that stresses group control.
- Down-peak & evening — relevant for hotels, hospitals and residential towers.
Key metrics engineers track
Industry practice focuses on a small set of KPIs that are comparable across projects:
- Average waiting time (AWT) — target often 25–35 s for grade-A offices.
- Interval — average time between successive arrivals at the main lobby.
- Handling capacity — percentage of population moved in a defined peak window.
- Car load factor — how fully cars are utilized during the peak.
From spreadsheet to simulation
Traditional Round Trip Time (RTT) calculations remain useful for early feasibility. Advanced workflows combine RTT with Monte Carlo or discrete-event simulation to model random passenger arrivals, door times, and group control behavior more realistically.
Modern platforms such as Liftaro Traffic Analysis let teams store project inputs, compare calculation methods, and export results for client reports—reducing rework when building population or floor usage changes late in design.
Practical workflow for consultants
Start with accurate floor population and usage coefficients, define the traffic pattern (office, hotel, residential), then iterate lift count and car size until KPIs meet the brief. Document assumptions clearly—traffic results are only as credible as the demographic data behind them.
Next step: Run your scenario in Liftaro’s traffic tool and compare Standard RTT, enhanced methods and simulation side by side.
