Here are three planning problems currently crippling London – and some ideas on how to fix them

St. Paul’s, views of which matter more to policymakers than your ability to afford a house. Image: Getty.

London’s huge success as a place to live and do business has brought with it an overheated property market and sky high prices. And this has as much to do with planning as a lack of development.

London clearly needs to provide more residential and office space. Its housing market is already among the least affordable in the country (an average house is worth almost 17 times the average annual salary of a Londoner), and commercial prices are 2.3 times higher than the national average. And as the population is likely to grow by another 70,000 inhabitants each year, without speedy action, the situation will only get worse.

There are a number of planning policies that restrict urban growth and threaten the development of the capital. With demand to locate in London unlikely to abate any time soon, these policies increasingly threaten the economic success of the city, and the benefits they bring to the city are becoming questionable.

1. Protected sightlines

There are a number of protected panoramas, linear views, townscape views and other river prospects that cross over the capital. Many of them converge on St. Paul’s Cathedral and the Houses of Parliament, which makes tall buildings particularly difficult to develop in some parts of central London, despite being sought-after locations for businesses.

But protected sightlines can cause problems for development even beyond the restrictions. Last year locals from Richmond (south-west of London) protested against the approved building of a 42-storey tower in Stratford (which is about 18 miles away) on the grounds that, by appearing behind the Cathedral, it was damaging the protected view from Richmond Park to St. Paul’s.

2. The green belt

The green belt restricts land supply in the capital by preventing the city from expanding outwards. But containing growth within the city does not necessarily make it more compact. As our research shows, housing demand leapfrogs to the other side of the green belt, generating longer commutes and, ironically, higher environmental costs.

3. Permitted development rights

Contrary to the two previous points, permitted development rights (PDR) relax rather than restrict development, but this can create as many problems as it solves. Put simply, one feature of PDR is that it makes it easier to convert commercial space into residential units. In London, take-up of PDR has been high in some boroughs, with more than 10 per cent of the existing office stock in Sutton and Lewisham being converted into residential space since the policy was introduced in 2014, as shown on the map below.

Source: MHCLG, 2018; VOA, 2018. Note that the centre of London is exempted from permitted development rights which explains the null or low take up.

It is true that PDR conversions help to increase the housing stock in the capital. But where demand is very high, it can also threaten viable office space, in particular for smaller or more affordable premises, and can even disrupt the night time economy. That the London Plan encourages local authorities to apply for PDR exemption indicates the potential danger of the policy.

A fresh view on planning policies

Individually, all these policies have flaws, and collectively they make the task for London extremely difficult. The capital needs more houses and more offices, but there are no plans to build outwards – the mayor is committed not to build on the green belt – and there are restrictions on building upwards. This creates a shortage of available land for development, increasing land value and creating competition between residential and commercial uses – competition that usually flips in favour of residential use because of PDR.

In order to deliver more developments and respect its planning commitments, London’s current strategy is to densify and redevelop land, which in theory would allow the city to provide more houses and more office space without extending out. This is a welcome and necessary strategy, but it is unlikely to deliver enough to meet the urgent growth needs because densifying is difficult and takes time to achieve.


The best proof of this is that, by pursuing this strategy almost exclusively in recent years, London has consistently failed to meet its housing targets. Last year London boroughs delivered about 39,600 net new dwellings, below the existing target of 49,000 set up by the current London Plan, and significantly lower than the new 2017 Strategic Housing Market Assessment target of 66,000 per year.

So as pressure increases year on year, planning restrictions can no longer be preserved. The green belt and protected sightlines were created decades ago, at a time when land pressure was considerably less acute. But while the social and environmental considerations that led to implementing them at the time are more important than ever today, other social considerations – such as the right to affordable housing – must now be considered too.

In particular, strategic reviews of the green belt should be conducted with regards to current needs, and to subsequently release a limited, controlled amount of land for development. Given the context, this is the only way to provide enough new homes, preserve adequate commercial space and sustain London’s economy.

Hugo Bessis is a researcher for the Centre for Cities, on whose blog this article originally appeared.

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Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.