I’ve updated my usual Google Fusion Table of the DfT’s Annual Average Daily Flow data to use the updated data up to 2016. As of this year, they new also include the estimation method for each count, which makes the data a lot easier to understand.
Note: If you’ve got a query about this data or you want to try and do something serious without please just contact me.
Data Points Map
The map has a very simple purpose – to easily display the traffic data available from the DfT as processed into Annual Average Daily Flow (AADF). The main map display has some basic features to achieve this.
For the main tab of the map, called ‘Data Points’ there is a range of marker styles used for each count point. This is to make it clear which data is current, and how outdated the other data is. The key for this is to the left. The intention of the design is to make those counts with a flow figure for 2016 to be most prominent.
Note: Sometimes points overlap, but change their reference (Count Point number). I’m not entirely clear why this happens but the practical outcome is you may find a 2016 marker overlaid on a previous one. Just remember that only those AADF published for 2016 are considered to be current by the DfT.
That there is an AADF figure however, does not mean that there has been a count for the current or indeed that year. As of 2017 DfT now explain the method by which each AADF has been reached and I now include this in the table available when you click on each data point.
The key to the method field is as follows:
- C.m – Manual Count
- C.a – Automatic Count
- E.p – Estimated from a previous year
- E.n – Estimated from a nearby count
- D.n – Dependent on a neighbouring count
Just above the method field you will also see a field named Count which tells you the date of the count based upon the data in the raw count data and also tells you the day of the week. This may help explain some fluctuations in the data. I don’t consider the historical data of the AADF to be particularly reliable, but as it has been cited I believe it is useful to show it clearly along with this information to help explain what issues it may have.
For the manual counts, I have also mapped and separately made available the raw data behind these counts. I consider this data to be relatively more useful than the AADF data.
All that said, there is still some useful data here. Here is one example table (cut down to fit):
|Count Point 18468 on A3211, a Class A Principal road in Urban area|
|Located in Farringdon Without Ward in City and County of the City of London|
Top row gives us the count point reference, and confirms the road name and designation – do check this as sometimes the map location is not entirely accurate. In addition some basic information about which ward and borough or constituency it is in is provided (though not full administrative data as yet).
Then there is the data table itself. Remember all of this is a projected flow based upon some other data, in this case we can see that these are all manual counts bar 2014 which was based on a previous count, and mostly they’ve been in May of late but in the past they have been in the autumn as well.
Those caveats on board we can then look at the data which shows us the changes over the years with cycling appearing to grow from 5000 a day to 11,000 whilst motor traffic has nearly halved from 62,000 to 32,000. Yes, those are broader figures but taking the fine detail here too seriously is probably a mistake as there is a lot of extrapolation going on.
The splits of vehicle are defined as follows:
- Pedal cycles: Includes all non-motorised cycles
- motors: All vehicles except pedal cycles.
- cars (and taxis): Includes passenger vehicles with nine or fewer seats, three-wheeled cars and four wheel-drive ‘sports utility vehicles’ (SUV). Cars towing caravans or trailers are counted as one vehicle.
- p2w: Includes motorcycles, scooters and mopeds and all motorcycle or scooter combinations.
- Buses (and coaches): Includes all public service vehicles and works buses which have a gross weight greater than 3.5 tonnes.
- LGVs (Light vans): Goods vehicles not exceeding 3.5 tonnes gross vehicle weight. Includes all car- based vans and those of the next largest carrying capacity such as transit vans. Also included are ambulances, pickups and milk floats.
- Medium goods vehicles (mgvs): Rigid HGV with two axles: Includes all rigid heavy goods vehicles with two axles. Includes tractors (without trailers), road rollers, box vans and similar large vans. A two axle motor tractive unit without trailer is also included.
- Heavy goods vehicles (HGV): Includes all other goods vehicles over 3.5 tonnes gross vehicle weight.
From these we can then derive the PCU (Passenger Car Units) which are a standard modelling tool for evaluating the road space use of vehicles. For PCU calculations I have used the table printed in TfL’s own modelling guidelines (page 67):
|Vehicle Type||PCU Value|
|Motor Cycle (Powered 2 Wheelers)||0.4|
|Light Goods Vehicle (LGV) – under 3.5 tonnes||1.0|
|Medium Goods Vehicle (MGV) – over 3.5 tonnes||1.5|
|Buses & Coaches||2.0|
|Heavy Goods Vehicle (HGV) – over 3.5 tonnes with 3 or more axles||2.3|
And that’s how the main map view works.
Space for Cycling Map
As a further view there is a tab called S4C Map which provides a graphical explanation of how each count does against the Space for Cycling campaign threshold of 2000PCU per day for environments where cycling can be reasonably pleasant if shared with motor vehicles. Rachel Aldred explains the principles of this well on her blog.
The key for this map is at the left. And the idea is to show you easily which locations already meet the 2000 PCU per day threshold and which are relatively close to it. Of course this data set does not include speeds and it should be remembered that speeds on the road must be 20mph or less and there should preferably not be a dominance of larger vehicles.
Cycle Width, PCU Shade
This is a quick experiment in the tab called ‘Cycle Width, PCU Shade’ to see if using the provided highways network shape (which is simplified and thus rather jagged) can provide any use in more rural areas. Again, as speed data is not supplied those places shaded green are not necessarily good for cycling but it can highlight by colour (green good to red bad) and width (narrow for less cycling, wide for more) how conditions and cycling levels match up to a limited degree.
Don’t Forget I have also mapped and separately made available the raw data behind these counts, and if you’ve got a query about this data or you want to try and do something serious without please just contact me.