2.3 Converting aggregated pupil travel distances into travel bands. 

In order to convert our aggregated known input on pupil travel distance bands for each school, for example "50% of pupils travel distance X to the school and 70% of pupils travel Y distance to the school",  we used linear modelling to convert these known aggregated data points into the numbers of pupils travelling under 1 mile, between 1-1.9 miles and 2 miles or more. 

 

The pupil travel distance bands are therefore intended to be a reasonable estimate for the purposes of better understanding pupil travel modes.  These distances are estimates, though for simplicity we have not labelled them as such in our dashboard analysis as we feel they will be fairly closely aligned to reality.

 

Caveats; we have had to use aggregated data sets e.g. 60% of pupils live within distance X from school, and applied on-the-road scale factors to that aggregate, which will differ from applying those factors to every individual pupil distance; the on-the-road scale factors used in our model is by cycle-travel on-the-road, however walking distances may sometimes be shorter e.g. walking through an alley between roads rather than cycling on-the-roads around;  we have taken aggregated data points & used linear modelling to convert those into travel bands, which again, will vary from exact distances.  We summarise an overall interpretation of the caveats in our model in a comparison to National Travel Survey data points below - Section 4.0. 

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2.4 Independent school pupil distributions  - estimated data

Independent schools don't disclose their pupil distributions publicly. In order to include them, we used the ratio of pupil distribution between a secondary state grammar school [admission process is academic selection] and a secondary state catchment school [admission process is usually closest straight line distance of pupil residence from school], and applied that to the average pupil distribution of a London catchment primary school to estimate the pupil distribution for London primary independent schools. In our model this resulted in 29% of pupils travelling under 1 mile, 29% travelling between 1-1.9 miles and 43% travelling 2 miles or more to their independent school. This compares to a  London state primary catchment where our model shows 79% of pupils travel under 1 mile, 16% travel 1-1.9 miles and 6% travel 2 miles or more to school.  Based  on our knowledge of Dulwich independent schools we believe this estimate has face validity. It is, however, an estimate and will vary by school.

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Caveats; this is an estimate that has been used across all independent schools. It will not take into account individual circumstances of schools and areas. For example there are a high number of Jewish independent schools in Hackney wards which are likely to have far smaller pupil distributions because they serve the Jewish communities in their localities, versus academically selective independent schools that recruit from 3+ mile radius.

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3.0 London primary pupil driving rates  

Data Source – DfT National Travel Survey [NTS] statistics. We have used London mode of travel data in the underlying NTS statistics. The years used were an average across 2017, 2018, 2019 and 2022. NTS collects travel diaries across a week for all members of participating households. These statistics are generated based on a total sample of 4,593 trips made for the purpose of 'education', by 435 children aged

5-10 living in London.

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This analysis of these underlying data tables for London showed the following pupil driving rates across distance bands which applied to the numbers of pupils in distance bands within our model. 

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Distance bands & % of pupil trips driven

Under 1 mile              7%

1 to under 2 miles   65%

2 to under 5 miles   67%

5  miles +                 58%

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4.0 Comparatives to NTS data. 

Across the NTS years analysed (2017, 2018, 2019, 2022),  which covered 4,593 trips made by 435 children, their analysis showed that 65% of pupils travelled under 1 mile to school and 35% over 1 mile. The blended pupil driving rate across these trips was 28%.  In our model,  we estimate that a slightly higher number of  70% of pupils are travelling under 1 mile to school and 29% over 1 mile. Our blended modelled driving rate across all distance bands is therefore lower, at 25%.  The reasons for the differences between these two estimated are explained further below in Section 5.0, but we think that they are sufficiently close for users to have  confidence in using the school, ward & borough level data in our model to further their campaigns for sustainable school travel. 

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5.0 Summary of caveats - reasons for variations between our modelled results & NTS data. 

 

5.1 Differences in profile of pupil trip lengths

As explained above the NTS has used a sample of 435 children for London,  and within that sample size, 65% of their school trips were under 1 mile. We have used catchment information inputs for almost every individual school in London & estimates for independent schools (see Section 2.0 for how these are developed) as a basis for our output that 70% of pupils travel under 1 mile.  Whilst the NTS survey is subject to the limitations of sampling approaches, our model is subject to the limitations set out in Section 2.0, namely, using aggregated catchment points to represent pupil distribution & using scaling factors to derive on the-road-travel-distances, rather than those actually reported by parents in the NTS survey.  

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5.2 Differences in pupil driving rates

The NTS data has a blended driving rate of 28% of primary pupils and our model calculates a modelled driving rate of 25%. The main reason for this variation is  due to the differences in the profile of pupil trip lengths, as explained in 5.1.  Since our model shows a higher proportion of pupils travelling shorter distances, it follows that using the NTS average driving rates within distance bands, so our blended pupil driving rate will also be lower. 

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However, NTS sample data does not of course show granular, school level, local insights. We think that our model is sufficiently close to NTS data for users to have confidence in using the localised school, ward and borough level data included.

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5.3 Caveats at local levels

Our model is an estimate using pupil catchment information and London average driving rates. It therefore won't account for key factors that will cause variation from average driving rates such as car ownership levels, safe availability of alternative walking & cycling routes, public transport accessibility, congestion zone, school, or community culture and so on. 

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6.0 Conclusion

We want to conclude by flagging that it is in theory absolutely  possible for the DfT and the DfE to track actual, individual pupil travel modes by school, and indeed by pupil distance travelled. However, the last year that school level travel mode information was collected nationally was 2011 when it was included in the school census. This is despite significant recent national efforts to improve pupil active travel to school. School run driving rates have remained almost unchanged for the last 20 years and we urgently need more publicly available, granular data on the school run to affect them.  We hope our model highlights how useful publicly available school, ward and borough level information on pupil travel modes can be & propels forward more systemic initiatives to collect it. As the saying goes "you can't manage what you can't measure".