Developers shouldn’t just treat canals as an aesthetic bonus. It’s time to use waterways for construction again

A disappointingly tiny proportion of the materials used building the 2012 Olympic park were transported via canals. Image: Getty

While London’s canals have seen a great resurgence in the last forty years, they’ve also witnessed a drastic move away from their originally intended purpose.

Once employed to ferry freight to and from the capital’s docklands, canal boats are now mainly used for leisure and alternative living.

It’s easy to put this down to the ongoing housing crisis, which has made many aspiring property owners view setting up home in a floating sardine as a viable option, but the truth is it's a vicious circle, with canals – or to be more specific, their misuse – playing a part in the capital’s housing woes.

As ex-industrial areas, many of which proudly sport a canal or river, continue to be developed, barges are being overlooked as a viable way to transport away construction waste and bring in materials.

Two prime examples of this are the Enfield Meridian Water Development and west London’s Old Oak Park Royal Development Corporation, two large canal-side development projects that could easily incorporate the waterways into their efforts.


The Meridian Water development plans proudly boast of its canal-side location.

With HGVs causing a vastly disproportionate amount of cyclist road deaths, getting freight off the roads would be safer, as well as reducing traffic and environmental impact. Transport via water uses around a quarter of the energy of an equivalent road journey. What’s more, any additional costs incurred by transporting freight by water are negated thanks to government backed grants.

Advocates of this mode of transport saw a brief glimmer of hope when Stratford was identified as the site for the 2012 Olympics. The area around the proposed park is riddled with canals and backwaters, perfect for heavy freight. Despite promising noises and the building of a new lock at Three Mills, which opened up a route to processing plants along the Thames Estuary, this option was not engaged with in any meaningful way.

Because while the Olympic Delivery Authority (ODA) moved an impressive 63.5 per cent of the materials used in and out of the park off-road, only a tiny proportion of this was via canal. The long hoped-for revival of waterways freight never happened and with the privatisation of the canals, it seems even further away.


The Canal and River Trust (CRT), the charity that now manages England and Wales’s canals, does little to encourage waterborne freight. Its website advises planners that “local staff may be able to put you in touch with companies potentially able to help” – which is quite simply a whole load of vagueness. While its predecessor, the government-run British Waterways, had a dedicated sustainable transport manager, CRT’s answer to this, the Freight Advisory Group, hasn’t met for almost five years.

A concerted EU effort has seen a great resurgence in freight borne on inland waterways in mainland Europe, but unfortunately nothing comparable is happening on this side of the Channel – but not due to a lack of options. The UK has the infrastructure in place already. It is just a matter of using it.

Having overcome their decline, canals are now seen as a great feature of modern cities. They pass through the centre of hundreds of towns and cities across the UK such as Birmingham, Glasgow, Nottingham and Manchester. Yet developments, despite being very willing to boast their canal-side credentials, are far less interested in using the waterways. Instead developers clog the roads with HGVs, blind to the fact the old-fashioned way just might be the best option for the future.

 
 
 
 

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.