Elevator traffic studies should not stop at capacity. Waiting time, journey time, queues, and tail behavior help explain what passengers experience when traffic patterns, dispatch, zoning, or demand intensity change.
Why capacity metrics are not enough
RTT, interval, and handling capacity help explain throughput and service frequency. They are important for traffic analysis, but they do not fully describe what passengers experience during a dynamic scenario.
A configuration can look acceptable through a capacity lens while producing long waits, growing queues, or poor tail behavior. That is why passenger-experience metrics should be reviewed alongside capacity metrics.
Metrics that describe passenger service
Average waiting time
The average time passengers wait before boarding or being assigned service, depending on the control and reporting context.
Transit time
The time spent travelling in the elevator after boarding, separate from the time spent waiting.
Journey time
The broader passenger trip time, often read together with waiting time and transit time.
Queues
Queue behavior helps show when demand is approaching or exceeding what the elevator group can serve smoothly.
Percentiles and thresholds
Tail metrics and threshold-style measures help reveal whether a small group of passengers experiences much worse service than the average.
Interval, capacity, and waiting time
Each metric answers a different question. The useful result is not one number in isolation, but a reading of capacity, service frequency, passenger waiting, journey time, queues, and scenario assumptions together.
Interval
How often service is available under the assumptions?
Useful for capacity review, but not enough to describe passenger experience.
Handling capacity
How many passengers can be transported in a defined period?
Useful for throughput, but it does not show queueing or tail waiting behavior by itself.
Average waiting time
How long do passengers wait on average?
Important, but should be read with journey time, queues, and tail behavior.
Journey time
How long does the passenger trip take in context?
Can reveal trade-offs when dispatch or zoning changes reduce one metric but increase another.
Where simulation helps
Simulation can show how passengers arrive, queue, board, travel, and complete trips over time. This is especially useful when demand patterns, multiple entrances, dispatch logic, zoning, or near-capacity conditions affect passenger outcomes.
When alternatives are close, multi-run simulation can also help reveal whether a difference looks consistent across runs or depends heavily on one random demand sequence.
Read passenger metrics in reports
VT Planner results can bring together capacity metrics, passenger-experience metrics, charts, assumptions, and completed runs. This helps teams compare alternatives without separating the numbers from the building, traffic pattern, elevator setup, and analysis method used to produce them.
Related passenger-metric resources
Connect service metrics with results documentation, technical definitions, capacity metrics, method selection, simulation context, and report examples.
Passenger experience metrics FAQ
Is interval the same as waiting time?
No. Interval describes service frequency under assumptions. Waiting time describes how long passengers wait. They can move differently, especially in complex or near-capacity scenarios.
Why review journey time as well as waiting time?
Waiting time only covers part of the passenger experience. Journey time helps show the broader trip outcome, including travel and service behavior after the passenger is served.
Why do percentiles matter?
Average metrics can hide poor tail behavior. Percentile-style or threshold metrics help show whether some passengers experience much longer waits or journeys.
Can a capacity result look acceptable while passenger service is poor?
Yes. Capacity metrics should be reviewed alongside waiting time, journey time, queues, and scenario assumptions before drawing conclusions.
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