The latest update for comprehensive journey costing

TimeTravel is our dataset of drive times and distances between any postcode sector or district. It provides a way to accurately plan and measure multiple journeys, whether that is for fleet, field team, or customer travel around the UK.

Working at postcode sector level means we can pre-calculate every possible sector-to-sector journey across the whole country. This removes the long calculation times required for point-to-point calculations and makes it practical to implement in systems where a quick response is required for multiple users.

One of the main strengths of TimeTravel is that it uses up-to-date data to make realistic journey times. With the road network and postcode geography constantly changing, our clients should ensure that they update the data they use every year, to get the most accurate results from the tool.

So how does TimeTravel work and how does the latest update improve it?

Travel distance and time radii on map.

How does TimeTravel calculate drive times?

To make otherwise complex calculations simple to the end user, we use the following process for each update:

  • We locate the population weighted centroid of each sector to use as a drive start point. This means that the most frequent journeys are represented because most start points will be from the major population centre in the sector.
  • For each source sector, calculate the time to drive to every piece of road that is navigable - usually every junction on the same landmass.
  • In every destination sector, we calculate the drive time to all the road junctions in the sector and average the time taken.
  • We then calculate the fastest (shortest time) journey time and distance using peak, off-peak and HGV speed tables.

The journey distance is the distance of the fastest route rather than the shortest distance. This is because the shortest distance is the absolute minimum distance possible and does not take into account different road types or conditions. However, the shortest distance is available within the data to those for whom mileage is more important than time.

With ongoing development to the UK’s road network, it is vital to use up-to-date data for these calculations which is why we update TimeTravel regularly. The road network we currently use for the mainland UK is supplied by Ordnance Survey (OS) and contains more than 1.2 million road links. The latest update also includes improved classification and detail of the road network, particularly in remote highland sectors.

What about ferry crossings?

Of course the road network can’t take you everywhere, especially on a series of islands like the UK. To allow for ferry crossings we assign each sector to its appropriate landmass (e.g. mainland UK, Northern Ireland, or Mull). Then, using published ferry routes we work out the journey options between each landmass. We calculate the best route to use depending on the start point of the journey. For example, the Isle of Wight has three possible ferry routes and those travelling from Bournemouth would use a different ferry to those travelling from Chichester.

This is another area where the latest TimeTravel update has had a major impact. Previous versions used the “crow-fly” distance of a ferry journey and applied a standard ferry crossing time. For the latest update, we’ve fully researched ferry voyage times and added extra data fields for published ferry times and crossing distance. These new fields work with the existing peak, off-peak, and HGV road calculations to add greater accuracy to final journey time and distance data. This means that calculations can select the best ferry route depending on whether the shortest journey time or shortest distance is preferred.

It also allows our calculations for shortest time and shortest distance to take into account the ferries available and choose the most appropriate depending on journey start and end points.



Case Study: Calculating time and mileage required on site visits

McGinley Support Services needed detailed time and distance data as a means of verification for their automated expenses system.

What else has changed in the latest update?

In addition to the transport network, the UK’s fundamental data geography changes every year. Royal Mail are constantly changing their postcode sectors and boundaries to rationalise this long-standing system. They need to add in new sectors to allow for new housing and business developments, whilst removing retired postcodes as larger businesses close or areas are reclassified. The latest TimeTravel data includes about 12,000 changes that have taken place since the previous update.

How is TimeTravel data supplied?

The full TimeTravel dataset for the whole of the UK and maximum detail comes in at more than 120 million data rows in the latest version and is almost 4 gigabytes in size. However, TimeTravel data is scalable to meet your specific geographic and detail needs, so the version you receive is likely to be a compressed .txt or .csv file via FTP or Google Drive transfer. The data is too large to see in its entirety in Excel or Access and is more likely to be integrated into an enterprise database in a CRM or website for use in staff or customer-facing travel tools.

If you prefer, we host the data on a server with an API interface to allow it to be available 24/7 to support your staff or customers. The API provides TimeTravel data online with the convenience of avoiding large downloads, database loading, and annual updates. In addition, it can provide a richer and more efficient set of common request types. We have a demo account to showcase this functionality at work, so get in touch if you’d like a demonstration.

Our existing TimeTravel clients benefit from an annual TimeTravel update. So, if you are an existing client, we will be in touch very soon about how you can get hold of the latest version. If you are considering TimeTravel for your own business, please get in touch and we can talk you through your requirements and the data’s capabilities in detail.

We've spent more than 25 years visualising complex data and making it easy to understand.
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