Are we nearly there yet? Four years of the Northern Powerhouse

Remember him? Ex-chancellor George Osborne launches his Northern Powerhouse Partnership in autumn 2016. Image: Getty.

Saturday 23 June marks a significant anniversary in British political history. No, not that one: it’s four years since George Osborne, in a speech at Manchester’s Museum of Science & Industry, first coined the phrase “Northern Powerhouse”.

Osborne’s speech prompted equal parts intrigue and scepticism amongst certain sections of the Northern intelligentsia. Following the abolition of regional development agencies in 2010, and the quiet death of Labour’s now largely forgotten Northern Way agenda, regional policy for the North had lacked an overarching theme. Local Enterprise Partnerships, constrained by austerity and with few formal powers, struggled to make much of an impact. City-region devolution was (and remains) uneven and confused.

The Conservative-led government needed to reframe the regional policy debate, and the Chancellor desired an electoral strategy that would enable the Tories to compete in key Northern marginals like Bolton West and Hazel Grove. And so, the Northern Powerhouse was born.

What is the Northern Powerhouse?

In that 2014 speech, Osborne described four ‘ingredients’ for building a more prosperous North: transport; devolution; science & innovation; and culture.

Science and culture have since largely fallen from the radar, aside from a handful of investments in the likes of Manchester’s new Factory theatre and the upcoming Great Exhibition of the North. What remains is fundamentally a regional development project with transport planning as the central policy lever, with the goal of creating a region with “not one city, but a collection of Northern cities – sufficiently close to each other that combined they can take on the world”.

Right now though, Osborne’s promise of improving infrastructure to the point where traversing the North is the “equivalent of travelling around a single global city” appears laughable – especially given the recent well-publicised rail meltdown. The gap between rhetoric and reality for stranded commuters seems wider than ever.

A new civil service for the North

Nevertheless, it would be a mistake to dismiss the Northern Powerhouse project as a failure already. Its most significant achievement is the creation of Transport for the North (TfN), the UK’s first ever pan-Northern government body. Established in 2015 and granted statutory powers in April this year, TfN can now be regarded as the Powerhouse project’s civil service.

These are very early days, but there are signs that having a proper Northern institution with real, if limited, powers has helped shift the terms of the agenda somewhat. Osborne’s early vision was criticised in some quarters for its over-emphasis on the North’s largest cities, and Manchester in particular.

Where the magic happens. Click to expand. Image: TfN.

By contrast, TfN’s recently published draft Strategic Transport Plan provides a welcome focus on the assets of smaller cities and towns. It leans heavily on evidence from 2016’s Northern Powerhouse Independent Economic Review, which identified the four most important sectors, or ‘prime capabilities’ for the North: energy; digital; health innovation; and advanced manufacturing. The plan then identifies seven ‘growth corridors’ where transport infrastructure requires improvement to better connect the key businesses working in these areas.

Interestingly, the plan is not based on the existing transport network; nor does it simply aim to connect the North’s most populous cities. As such, it challenges the concept of the Northern Powerhouse as an overly urban-centric model that risks turning Manchester into a London of the North and ignores other parts of the region.

The role of high speed rail within the Powerhouse agenda reflects this. The “high speed rail connection from from Manchester to Leeds” described by Osborne in 2014 has morphed into Northern Powerhouse Rail (NPR), a less grandiose plan combining new lines, improvements to existing infrastructure and, crucially, a new station at Bradford, a city too often ignored in previous attempts at regional development.

The proposed corridors. Click to expand. Image: TfN.

HS2, meanwhile, is increasingly regarded by many Northern politicians as an opportunity for urban regeneration rather than a transformational infrastructure project, with the biggest improvements to connectivity likely to be felt more in Birmingham than Manchester or Leeds.


What happens next?

Of course, this is only a plan, and one at a very strategic level. As yet, there is no confirmed funding for NPR. Few of the proposed schemes have planning permission yet. Battles over Green Belt and compulsory purchases are some years off.

But the act of moving some power out of Whitehall to a new, independent, sub-national government body is significant and, given the UK’s long-standing reluctance to devolve governing capacity from the centre can be regarded as an achievement. The momentum of the Northern Powerhouse project can only be maintained if it is run from the North.

The Northern Powerhouse probably isn’t what George Osborne thought it would be, and by itself the project won’t reverse 100 years of relative decline in Northern England. But it is something, and unlike previous attempts at regional development will increasingly be driven by an organisation outside the Whitehall bubble. The current rail debacle is a major test – but it need not signal the end of the line for the Northern Powerhouse.

Tom Arnold is a PhD Researcher in the Department of Planning & Environmental Management at the University of Manchester. He tweets as @tj_arnold.

 
 
 
 

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.