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An elevator traffic study is only as good as its inputs and only as useful as its targets. Get the population wrong and every downstream number is wrong; skip the targets and you have a report nobody can act on. This is the working method, in order, for producing an elevator traffic study that holds up.
The steps below take you from a blank page to a recommendation a client or planning authority can accept. None of them is hard in isolation; the discipline is doing them in the right order and not skipping the unglamorous early ones, because an elevator traffic study lives or dies on its inputs.
Every metric in the study derives from how many people use the building, so this is the input that matters most. Population is usually estimated from net internal area and an occupancy density appropriate to the tenancy. A dense single-tenant office packs more people per floor than a multi-let building with circulation losses.
Resist the temptation to use a round number. A 10% error in population can move handling capacity across the pass/fail line, so base the figure on the actual floor areas and a defensible density, and record the assumption so it can be challenged.
Who occupies the building determines how they travel. A single-tenant headquarters produces a sharp morning up-peak as everyone arrives in a tight window. A multi-tenant building spreads arrivals. A hotel mixes guest and service traffic; a hospital adds beds and trolleys to the flow.
Choose the demand profile that matches the tenancy, because it sets the shape of the arrival pattern the elevator traffic study will model. The up-peak is the usual governing case, but for some buildings lunch-time two-way traffic is harder to serve and must be checked.
Before running anything, decide what “good” means for this building. Targets are drawn from the building’s grade and the client’s brief, expressed as ceilings on average waiting time and interval and a floor on handling capacity. A prestige office might set an interval target near 25 seconds; a budget block accepts more.
Without targets, the elevator traffic study produces numbers with no verdict. With them, every result reads immediately as a pass or a shortfall.
Now describe the installation to be tested: the number of cars, the rated load and resulting capacity, the rated speed, and the dispatch strategy, conventional collective or destination dispatch. These are the levers you will adjust if the design fails its targets.
For a straightforward up-peak in a conventional building, a round trip time calculation gives a fast and transparent first answer. For anything with destination dispatch, multiple zones, sky lobbies or significant interfloor traffic, use simulation, because calculation cannot represent how those systems actually behave.
Most real studies use both: calculation to get into the right region quickly, simulation to confirm the final specification and to produce the client-facing evidence.
With a calculator, you compute round trip time and derive interval and handling capacity. With simulation, you run many seeded repetitions so the randomness in passenger arrivals averages out, and you read the distribution rather than a single run.
This is where automation earns its place. Tools such as AdSimulo invert the workflow: instead of testing one configuration at a time, you state the target level of service and its Expert System runs thousands of simulations to find the configuration that meets it. An the analysis that once took a junior engineer most of a week collapses into a set of runs and an afternoon of interpretation.
Line the output up against the targets from Step 3. If average waiting time, interval and handling capacity all sit inside their limits, the design passes. If one fails, the result tells you which lever to pull: a faster car cuts journey time, a larger car lifts handling capacity, an extra shaft shortens interval.
A 3D visualization helps here. Watching passengers queue and cars bunch during the up-peak makes the cause of a failure obvious in a way a table of numbers does not, and it is far more persuasive when you explain the fix to a client.
Adjust the candidate group, rerun, and confirm the passing design. Then produce the report. The deliverable should show the inputs, the targets, the results and the recommended configuration in a form a client or planning authority can read without translation. AdSimulo’s twenty-three report types cover this, including handling capacity, peak comparison and an executive scorecard that grades the design against the targets you set.
The weakest link in most studies is the population estimate, which rests on occupancy density assumptions. Independent research on how buildings are actually occupied, such as the work published by the US National Institute of Standards and Technology, is a useful reference point when justifying the density you choose, particularly for unusual building types where standard figures may not apply.
Work the steps in order and the this study almost writes itself: population, tenancy, targets, candidate group, method, run, read, report. The discipline is in the inputs, not the arithmetic. To see how the automated workflow handles steps five through eight in one pass, explore the lift traffic analysis page.






At minimum: the building population, the tenancy type, the floor count and heights, and a candidate elevator group (number of cars, capacity, speed and dispatch type). Service targets must also be agreed before results can be judged.
Enough that the averaged result is stable. Because each run contains random arrivals, a single run is unreliable; a credible the study uses multiple seeded repetitions and reports the average and spread.
Usually the morning up-peak, but not always. Lunch-time two-way traffic governs some buildings, and down-peak matters where evening egress is critical. Check the periods relevant to the tenancy rather than assuming up-peak.
The method is identical; only terminology and the referenced codes differ. A study can report in either convention as long as the targets and standards cited match the building’s jurisdiction.
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