Documentation
VT Planner Analysis Interpretation Guide
Learn how to keep VT Planner scenarios comparable and interpret calculation, simulation, and multi-run evidence.
A useful comparison changes one intended variable while holding the other assumptions constant. Confirm the scenario before reading the headline result.
Match the Method to the Question
| Question | Use |
|---|---|
| Is the group broadly sized for the assumed demand? | Calculation for RTT, interval, and handling capacity. |
| How do queues, dispatch, loading, or transfers behave over time? | Simulation. |
| Would the conclusion change with another random realization? | Multi-run simulation. |
Calculation and Simulation answer related but distinct questions. Do not treat their metrics as interchangeable or expect a single simulated sample to reproduce an analytical value exactly.
Controls That Change the Scenario
| Control | Why it matters |
|---|---|
| Demand profile or schedule | Changes arrival intensity, direction, origins, destinations, or time periods. |
Dispatcher |
Changes how calls are assigned to cars. |
Duration (min) |
Changes the observation window and whether queue growth is visible. |
Seed |
Changes the random realization while leaving the modeled scenario unchanged. |
Capacity factor (%) |
Changes the usable passenger load of each car. |
Load bypass (%) |
Changes when loaded cars stop accepting additional calls. |
Stair factor (%) |
Changes how much short-trip demand is diverted to stairs before elevator service is simulated. |
| Settings by car | Change the capacity, motion, door, dwell, or transfer behavior of individual cars. |
| Energy model and inclusion | Change the electrical assumptions and whether energy is estimated for the run. |
| Service zones and transfers | Change direct service, passenger paths, and queue locations. |
Read Passenger Times Correctly
| Result | Interpretation |
|---|---|
Calculation — Average waiting (AWT) |
An analytical waiting estimate derived from interval and loading assumptions. |
Calculation — Average transit (ATT) |
An analytical estimate from entering the car to reaching the average destination, including modeled travel, stops, and passenger transfer time. |
Calculation — Time to destination (ATTD) |
AWT + ATT, ending when the destination doors begin opening. |
Calculation — Average journey (AJT) |
ATTD plus the final passenger-transfer allowance. |
Simulation — Average Waiting Time (AWT) |
Time from passenger arrival at each landing until the effective boarding door cycle begins opening, accumulated across transfer legs. |
Simulation — Time to Board |
Time from passenger arrival at each landing until actual boarding, accumulated across transfer legs. |
Simulation — Transit |
Time from actual boarding to alighting, accumulated across journey legs. |
Simulation — Journey |
Time from initial passenger arrival through final alighting, including waits, boarding, travel, and transfer legs. |
P90, P95, P99 |
The value at or below which 90%, 95%, or 99% of the observed passenger times fall. |
Calculation estimates and observed Simulation times are related but not interchangeable. Averages describe the center of a result; percentiles reveal the tail. Inspect both.
Check Stability
Use the result as a group of signals:
Completion rate: whether generated passengers completed their trips within the result horizon;- queue charts: whether waiting demand clears, stabilizes, or grows;
- passenger activity: whether the tested traffic is the traffic you intended;
- waiting and journey percentiles: whether a minority of passengers experience very long service;
- car loads and stops: whether performance relies on heavy loading or excessive stopping;
- main-terminal departure headway, when available: actual spacing between qualifying departures from the main terminal.
Single Run and Multi-run
Use the same explicit seed for a direct single-run A/B comparison. Use Multi-run simulation to examine variation across samples.
Multi-run repeats the scenario across seeds:
- the mean summarizes the central tendency across valid runs;
- pooled distributions and percentiles summarize passenger outcomes across the repeated study;
- a confidence interval expresses uncertainty in the estimated metric.
A narrow confidence interval indicates low sampling uncertainty under the modeled assumptions. Validate the demand model and equipment assumptions separately against project evidence.
Like-for-like Comparison
- Use the same saved building version.
- Confirm equivalent demand in each frozen scenario.
- Keep duration, dispatcher, capacity factor, load bypass, Stair factor, and zoning fixed unless one is the study variable.
- Use equivalent car-specific and energy assumptions, including the same energy-inclusion choice in every compared run.
- Use the same seed for single simulations or the same run count and seed policy for Multi-run.
- Compare runs with the same completed horizon; use full-duration runs for full-duration comparisons.
- Compare the same metric definitions and units.
- Read averages, tails, completion, and queues before deciding.
See Results for report actions, charts, replay, exports, and partial-run handling. See Operational Energy for the additional controls needed in an energy comparison.