Surveys give snapshots but cap out; modelling covers the full workforce for planning, budgeting, and audit-ready Scope 3 reporting.
If you need commute data for 100% of staff, surveys usually stop short. They help you hear from employees directly, but they depend on who replies, when they reply, and whether the data is still current a few months later.
Here’s the short version:
If we were choosing a base for planning, we’d use it like this:
That matters when you need to answer questions like:
Bottom line: surveys help, but they often level off before they cover the whole company. A model fills that gap.
| Criteria | Commute survey | Commute model |
|---|---|---|
| Workforce coverage | Only respondents | Full workforce estimate |
| Data age | Can go out of date after one survey round | Updated from current inputs |
| Employee input | Direct self-report | Indirect estimate |
| Planning use | Limited when response stalls | Better for route, budget, and demand planning |
| Scope 3 Category 7 | Harder when data is partial | Clearer input trail and repeat use |
| Best role | Support tool | Main planning base |
So if your response rate is 40%, 50%, or even 70%, you still have a gap. And for a large employer, that gap can be too big to ignore.
You can start with a survey. That makes sense. But it stops being a solid planning base once response rate, recency, and audit-readiness start to matter.
Response patterns often vary by shift and site. And that can skew load factor and cost per boarded rider estimates.
The problem is nonresponse bias. In plain terms, the data reflects the people who answered, not the full workforce. If one site responds more than another, or day-shift staff reply more often than night-shift staff, your picture of demand can drift off course.
Even a well-run survey has a shelf life.
If shift patterns, hybrid attendance, hiring, or site assignments change, the data no longer reflects current commute behaviour. It reflects the day you asked, not the day you plan for.
That gap matters. Outdated demand data distorts both load factor and cost per boarded rider. So a plan that looked fine a few months ago can start to miss the mark once workforce patterns move.
Self-reported data is also harder to defend for repeatable, audit-ready Scope 3 Category 7 reporting, especially when responses are partial across sites and periods.
A documented model gives you a stable trail of inputs and assumptions. That makes the planning process easier to trace, check, and reuse across reporting cycles.
That is where modelling takes over.
Once response rates level off and survey data starts to age, modelling becomes the main tool. A commute model doesn't rely on employee replies. It uses data you already have: postal code-level residence data, worksite locations, employment patterns, and on-site schedules. With that, you can estimate commute behaviour across your whole workforce. That's why the model becomes the base for planning, not just reporting.
For a multi-site employer, this means every employee in your HR system can be included in the model, not just the people who filled out a survey.
When headcount increases, shifts change, or a new hybrid attendance policy starts, you update the model inputs instead of running another survey cycle. That keeps your planning inputs tied to current workforce patterns.
This matters when you're sizing a new shuttle route, changing a public transport subsidy, or putting together a budget submission. A model gives you a current basis to work from. A survey gives you a snapshot, not a live planning base.
Model-based commute estimates rely on clear, traceable inputs. Every assumption is recorded, which gives you an audit-ready, repeatable basis for Scope 3 Category 7 reporting.
Surveys can still help, but they should support the model, not replace it. If you run a survey, use it to fine-tune assumptions. The model holds the reporting logic, so Scope 3 Category 7 figures stay usable even when response rates fall or a survey cycle gets skipped. This is general information, not legal or tax advice.
The comparison below shows where that shift changes the decision.
Once response rates level off, the question shifts. It’s no longer about whether surveys still help. It’s about where modelling should step in.
This isn’t a case of replacing surveys. It’s about using surveys to inform the work, then using modelling to cover the full workforce.
| Criterion | Commute survey | Commute model |
|---|---|---|
| Workforce coverage | Limited to respondents; full coverage is rarely achieved | Covers the full workforce using minimal inputs |
| Recency and coverage | Can become stale as shifts, work patterns, and home locations change; gaps appear when response rates plateau | Faster to refresh as inputs change; not dependent on response rates |
| Direct employee input | Strong; captures what employees report directly | Limited; estimates patterns rather than collecting individual responses |
A simple way to think about it: use surveys to tune the inputs, and use modelling as the operating base.
| Criterion | Commute survey | Commute model |
|---|---|---|
| Documented assumptions | Assumptions depend on what respondents report; gaps appear when response rates are low | Assumptions are recorded and traceable regardless of response rates |
| Audit trail | Harder to defend when responses are partial across sites and periods | Stable trail of inputs and assumptions that can be checked and reused |
| Cycle-to-cycle repeatability | Tied to survey cycles; a skipped cycle creates a gap in reporting | Repeatable across reporting cycles even when no survey is run |
Next, triply shows how to turn those inputs into a repeatable model for planning and Scope 3 Category 7 reporting.
Once response rates level off, it makes sense to shift your planning base to modelling and keep surveys for targeted validation. Surveys can still help you check specific assumptions, but they shouldn’t be the system of record for workforce-wide planning, budgeting, or Scope 3 Category 7 reporting.
A focused survey still has its place. It can add detail on behaviour or help you check whether a mobility measure is working the way you expected.
triply builds a full commute model from postal codes and shift patterns, without running a survey cycle. That model becomes a practical base for analysis, consolidation, simulation, and reporting:
Because the model covers the full workforce, scenario outputs reflect total demand, not just the people who answered a survey.
Surveys tend to hit a ceiling once response rates flatten, data gets old, and audit trails become harder to defend. Modelling deals with all three. It gives operations, finance, and sustainability one shared basis for decisions.
Book a demo with triply to see how commute modelling works in practice for your sites.
