How have Low Traffic Neighbourhoods ignited a culture war on Twitter?

London’s new traffic reduction policies have caused protests, vandalism and furious online arguments. This is the first of three posts analysing the Twitter debate around London’s contentious Low Traffic Neighbourhoods.

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An LTN in Oval, photo from Lambeth.gov.uk
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A protest against LTNs in Ealing

What are LTNs?

Low Traffic Neighbourhoods (LTNs) are interventions to reduce private vehicle traffic and encourage pedestrians and cyclists to use the roads. Cycling and walking are intended to substitute for busses and the tube; use of which is discouraged because it is a potential site of covid transmission. LTNs also fit into a wider policy of encouraging walking and cycling.

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Modal filter in Powell Road, Hackney. Photo from Dezeen.

How have LTNs been received?

A survey by YouGov, the only methodologically valid poll I’m aware of, shows strong support for the measures overall. However, some citizens forcefully object to LTNs, arguing that they increase traffic jams, reduce air quality by making car journeys longer, are only installed where they benefit wealthy people, and block emergency vehicles. Often, anti-LTN campaigners couch their concerns in terms of their right to drive, or authoritarian threats to their freedom of movement.

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Vandalised planter, used to prevent through traffic in Ealing. Photo from David Millican.
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LTNs as class war (I removed identities from Tweets of non-politicians)
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London Borough of Hackney Cabinet Member for Transport on Sat Nav and traffic

The Twitter debate is highly concentrated

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Keeping things in perspective

Part of the answer to the question of how LTNs have been so controversial is that a small number of people can make a lot of noise.

A communication channel

One benefit of analysing the Twitter data is identifying these highly active accounts — applying the term ‘influencer’ seems inappropriate, but it does convey the idea.

Appendix: how data was collected

This appendix describes my method in more detail. This method was used to gather the data for all three posts in this series. My LocalNets software was used to gather all the data, which collects tweets using search terms, or from specified list of Twitter users.

  • Cyclist — if their bio lists cycling as their only or primary interest.
  • Cyclists and taxi drivers were also coded as pro or anti-LTN if they had at least two tweets strongly stating a position.

Written by

PhD on digital systems for collective action and social network analysis. jimmytidey.co.uk/blog

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