Every transport decision depends on an invisible question: who travels from where to where? This study builds a detailed answer by combining the broad movement picture from mobile phones with the human details surveys can provide. Transport planning depends on origin–destination matrices: records of how many trips run between each pair of places.

They support decisions about accessibility, new services and development. But local decisions need up-to-date information at small-area resolution, separated by travel mode and purpose, and that information remains inaccessible or unavailable.

This study presents detailed matrices for England and Wales, covering travel modes, time periods and trip purposes for the weekday morning peak. The core idea is like combining a city-wide security camera with a carefully designed household questionnaire.

The mobile records show the broad pattern of movement, while surveys add details the mobile totals do not carry. The mobile records provide the spatial structure of movement at a resolution no survey sample can reach. The surveys and population information add travel by children, travel mode and trip purpose.

The two sources are combined in stages, and every stage is included in the published processing pipeline, so the evidence and assumptions behind a value can be followed in the code that produced it. The build uses five input streams. Only the mobile network aggregate is commercially licensed; the other four are open or available to researchers under standard licences.

Because only the mobile base is licensed, the processing pipeline can be rerun outside the original project by anyone holding a licence for a compatible feed. The processing pipeline runs through four sequential steps. Each step adds a dimension the mobile aggregates do not carry, or connects the result to survey evidence, and each step can be changed for a different planning assumption.

The work keeps track of trip volumes between places by travel mode and time period, along with the region where a trip begins and the distance band it falls into. There is no independent small-area measurement for England and Wales that can serve as a complete ground truth for these matrices.

So the evaluation uses consistency checks rather than claiming a verdict on accuracy. The checks compare the result with survey evidence and ask whether it behaves as intended. The checks are regenerated whenever the dataset is rebuilt, giving users comparable evidence for their own assumptions.

The weekday morning peak purpose mix changes substantially after it is adjusted toward the survey controls. Before the adjustment, commuting takes a larger share than in the survey, while personal business and holiday travel take smaller shares.

After the adjustment, every control group matches its survey share to within 0.01 percentage points, while the mix still varies between starting places according to the local information. Purpose is inferred rather than directly observed, so users whose questions depend on purpose should inspect the local assumptions and controls against local evidence.

The visual makes the contrast clear: calibration brings walking and private-car shares toward their survey levels, while raking pulls several travel purposes noticeably into line. The practical payoff is that an analyst can extract the flows a question needs instead of using one undivided total: morning education trips for school access, morning car flows for congestion, or bus and rail volumes for public-transport decisions.

But these matrices are synthetic planning estimates, not measurements of individual travel. They can still carry biases that the compared dimensions do not reveal, and mobile network data are not representative of the whole population.

The Welsh matrices do not receive survey-based travel-mode adjustment, child travel inherits the adult travel pattern, and trip purpose is inferred rather than observed. That is why the processing pipeline is released as a configurable, auditable process: someone who distrusts an assumption can change it and rebuild the estimates for the decision in front of them.

The result is a planning map that can separate journeys by place, time, purpose and travel mode. It can help local decisions ask a more useful question than simply how much people travel: which journeys need support, and where?