VT Planner helps professionals model and compare vertical traffic scenarios with explicit assumptions. It supports analytical sizing, passenger simulation, statistical validation, dispatch comparison, zoning evaluation, and report generation.
Calculation and simulation serve different purposes
Analytical calculation is usually the right starting point. Metrics such as Round Trip Time (RTT), interval, and handling capacity provide a fast way to understand the dominant sizing factors of an elevator group, especially in simpler up-peak cases.
Simulation becomes more valuable when the project includes conditions that average calculations do not represent well:
- mixed, interfloor, down-peak, or midday traffic;
- multiple entrance levels, basements, or transfer floors;
- non-uniform population by floor;
- service zoning or sectoring;
- modernization studies or near-capacity conditions;
- destination dispatch, algorithm comparison, queue behavior, and passenger experience metrics.
What simulation adds
An elevator traffic simulator can model details that are difficult to capture in a single aggregate formula: passenger origin-destination demand, stochastic arrivals, loading limits, door timing, elevator motion profiles, service zones, dispatch rules, queues, waiting times, transit times, journey times, charts, histograms, and percentile results.
This matters because elevator performance is not defined by one average alone. Two systems can have similar interval or handling capacity while producing different passenger waiting times, queue behavior, journey times, or tail-percentile experiences.
The limits of a single simulation run
Passenger arrivals and destination choices can vary from one run to another. A single simulation may be unusually favorable or unusually severe depending on the random demand sequence. Treating one run as the final answer can therefore lead to weak conclusions.
Multiple simulation runs help estimate:
- average performance and dispersion between runs;
- confidence intervals and percentile behavior;
- sensitivity to demand variation;
- whether the difference between two alternatives is meaningful or mostly random noise.
Metrics that matter
The most useful traffic study usually combines capacity metrics and passenger experience metrics. Core capacity metrics include RTT, interval, and handling capacity. Passenger experience metrics include waiting time, transit time, time to destination, journey time, and p90 or p95 percentiles that show tail behavior beyond the average.
For destination control and destination-based dispatch, interval alone can be less representative of passenger experience. Waiting time, time to destination, journey time, queue behavior, and percentiles often provide a clearer view of service quality.
Dispatch, destination control, and zoning
Dispatch strategy can materially affect results. Hall-call dispatch and destination-based dispatch optimize different information sets. In destination dispatch, the system knows the passenger destination before boarding, assigns a car, and can group passengers with compatible destinations.
The effect is not universal. Benefits depend on traffic pattern, demand intensity, building layout, and algorithm behavior. Zoning and sectoring should be evaluated the same way, because the outcome depends on population distribution, demand pattern, transfer behavior, and elevator group configuration.
How to interpret simulation results
Good simulation output should be read as decision support, not as an automatic certificate of compliance. A useful report should make clear the building model, elevator group, traffic pattern assumptions, dispatch strategy, service zones, simulation duration, random seed or multiple-run setup, average metrics, percentile metrics, confidence intervals where applicable, and known scenario assumptions.
A practical position
- Use analytical calculation to establish an initial size and understand the main capacity drivers.
- Use simulation to evaluate dynamic behavior, complex scenarios, dispatch logic, queues, and passenger experience.
- Use multiple runs when stochastic demand can influence the conclusion.
- Compare alternatives using the same assumptions so differences are meaningful.
- Interpret results as professional decision support, not as a substitute for engineering review.
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