How have Low Traffic Neighbourhoods ignited a culture war on Twitter?
By: Jimmy Tidey
Tags: Sustainability, Cycling, Design, Urban Planning, Social Media

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.

An LTN in Oval, photo from Lambeth.gov.uk

When the #LTN hashtag appeared in my Twitter feed in July 2020, it caught my eye. It affects the area I live in, but I also noticed it because it was so acrimonious. So far, Low Traffic Neighbourhoods (LTNs) have caused protests across London, a campaign of vandalism, death threats to local politicians, and all the other familiar inhumanity of politics on Twitter. At the same time, there are also Twitter accounts committed to carefully arguing for their sincerely and deeply held views on LTNs. In the hope that the debate could be an interesting model of digital democracy, perhaps even a sandbox for thinking about ways to improve civic discussion on social media, I started collecting data about the LTN debate from the Twitter API.

A protest against LTNs in Ealing

In my previous research, which used the same methods I’ve deployed here, I looked at highly localised Twitter discussions, for example, around a single planning decision. Often, such small scale debates beg questions about how transferable any insights are. On the other hand, national politics on Twitter has so many participants that it is hard to do a proper analysis, because of the volume of data involved, and because of the lack of clear boundaries.

The LTN debate is a middle ground. It has thousands, rather than millions, of participants, but still concerns a substantial issue that we can all understand — LTNs profoundly affect transport and street space for millions of Londoners.

This post: How a handful of Twitter users drive the LTN debate

Second post: How black taxi drivers shape the debate

Third post: Speculative thoughts on tools for better civic participation

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.

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

LTNs reduce traffic flows by placing a barrier to cars across selected roads — leaving road access from both ends but preventing through traffic. These are called ‘modal filters’. LTNs are funded by Transport for London and make use of Experimental Traffic Orders, legislation which has been modified to assist local government in responding to covid.

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.

Vandalised planter, used to prevent through traffic in Ealing. Photo from David Millican.

The Borough of Wandsworth has removed LTNs as a result of the backlash, while Ealing, Lewisham and Kensington and Chelsea have also modified their schemes in the face of criticism.

The narrow issue of LTNs has become connected to the wider political landscape, with the Anti-LTN campaign casting those in favour LTNs as the ‘metropolitan elite’; likewise, Pro-LTN campaigners have linked the Anti-LTN campaign to regressive nativism, mirroring the rhetoric of the Brexit referendum.

LTNs as class war (I removed identities from Tweets of non-politicians)

LTNs are a short-term response to Covid, but they also respond to longer-term concerns. One is ‘rat-running’ — the practice of using residential roads as shortcuts. In the past, only a few locals or taxi drivers would be aware of these shortcuts. Now, sat-nav route planners algorithmically route large volumes of traffic down rat runs, and the resulting traffic can disrupt residents’ lives.

London Borough of Hackney Cabinet Member for Transport on Sat Nav and traffic

LTNs also serve borough councils’ long term planning ambitions to shift emphasis away from private cars. Critics of LTNs question the legitimacy of using emergency covid legislation to meet the objectives of non-covid transport plans, especially because LTNs have been installed so quickly.

Twitter is not the only place that LTNs have provoked online controversy. Facebook groups have also played a part, although Facebook’s platform makes analysis much harder. Commonplace, a site specifically designed for local planning issues, is another notable platform.

With this background in mind, here is my first finding.

The Twitter debate is highly concentrated

This graph shows the number of Twitter @mentions and retweets by the top 120 accounts using terms related to LTNs, excluding accounts belonging to borough councils or those of borough Mayors. Detailed methodology is given at the end of this article.

The top 20 Twitter users are responsible for half of the total activity. These accounts typically belong to campaigners. Subjectively, comparing Facebook and Twitter LTN discussions, the same individuals are often active on both platforms. This suggests that online debate may be influenced by just a hand full of individuals.

I was surprised by the sheer volume of interactions. Looking through the data, it became apparent that some Twitter users engage in long threads with up to 40 users tagged in, allowing them to generate huge numbers of connections. As a side note, many of the participants must invest huge amounts of time on this topic. On the one hand, much of this effort is poured into unproductive shouting matches; on the other, it does show Twitter’s capacity to drive civic engagement.

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.

It’s obvious that Twitter does not represent the community at large, but it’s also easy to forget that. Loss of perspective is always a danger — especially if you are in the throes of a Twitter spat. When dealing with citizens with trenchant or extreme views, it’s helpful to ask what fraction of the population they represent. Once you are interacting with more Twitter accounts than you can easily memorise, it can feel overwhelming. Our intuitions about group sizes stop working in the virtual world. Data that gives us a better picture the numbers of participants can help, perhaps even tempering Twitter’s capacity to dominate the attention of citizens and politicians alike.

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.

This handful of highly active individuals have the power to shape perceptions and disseminate information. While there is no democratic reason to privilege them in decision making, they represent an important channel for communication. They are also an obvious place to start in any interventions intended to guide a conversation towards creative engagement and away from futile hostility.

Perhaps Twitter is too effective at handing power to a few highly vocal individuals and should be sidelined as a forum for civic debate. Or it might be that we need to look deeper in the network to find the more interesting parts of the debate. The second post will do just this, including investigating how taxis have fuelled the ‘culture war’ aspects of the Twitter debate.

Second post: the role of black taxis in the LTN debate →

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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.

Step 1: Searched for any tweets containing “LTN”, “#LTN”, “LTNs” and “low traffic neighbourhood” within 10 miles of Central London. The search ran from September 12th. This created what I refer to as the search corpus.

Step 2: Looking at the search corpus, I selected the top 130 Twitter accounts with the most @mentions (inbound plus outbound). All tweets from the top 130 accounts were then collected from October 1st — this is the corpus of the account. The top 130 accounts were coded as follows:

Pro-LTN, anti-LTN or ambiguous (2 or more tweets strongly indicating a view either way)

This result in 55 anti-LTN accounts, 54 pro-LTN accounts and 11 ambiguous accounts. There were also 9 taxi drivers, all but 1 anti-LTN.

Step 3: All Twitter users in the accounts corpus each Twitter user was coded as follows (in addition to their already coded LTN views):

Taxi driver — if their bio states they are a taxi driver.

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.

In total, 99 pro-LTN accounts were recorded, and 98 anti-LTN accounts.

There were 49 cyclists, of whom 28 are also pro-LTN, 2 anti-LTN, and 19 coded as not having given an LTN view.

There are 59 black taxi drivers, 30 anti-LTN, 1 pro-LTN and 28 coded as not having a view.

Step 4: Collected the list of accounts each of the 130 top accounts followed. Looking at the whole accounts corpus, those with ‘Brexit’ in their bio were coded as ‘pro’ or ‘anti’ Brexit, if their bio made their position clear.

Step 5: Labeled “Institutional” Twitter accounts: official Twitter accounts of a borough council, borough mayor or mayor of London, or the official TFL account. These accounts are heavily mentioned but rarely reply, so could distort the network. They were excluded from the analysis.