“Greyer than John Major's underpants”: Manchester's new Metrolink map

Metrolink in action. Image: Getty.

“Metrolink is always looking at ways to improve information about services.”

Is it? That's good.

“A new-style Metrolink network map – designed to be more accessible, easy-to-understand and include more information – is now being rolled out to all tram stops.”

Exciting!

“As the tram network expands with more lines and services, the new map design will allow us to include more information for passengers.”

Oh, wow, we *love* information! I bet this new map is going to be better than ev-

“The name of stops is more prominent and – instead of using coloured lines – the map identifies services using a combination of letters and colours alongside arrows to show direction of travel-”

-What.

So it is that the new Metrolink map – actually, new is a misnomer; it's been out since August, it's just that we've only just noticed it – rather breaks with venerable metro map tradition.

Most such maps use a variety of bright colours to illustrate their different lines. Thus, you can see at a glance, say, that the District line heads east to Upminster, or that the A train goes from Harlem to Far Rockaway.

Until recently, Manchester's tram network followed a similar pattern. Here's the old map:

Click to expand.

Look at those calming pastel shades. Isn’t that lovely?

The new version, though, eschews this long established practice. And these various pastel shades have been replaced by, well, this:

Click to expand.

Grey. Grey, as far as the eye can see. Greyer than John Major's underpants on the morning of laundry day.

Metrolink say the new map is “more accessible for the people with colourblindness”. And making transport, and the information  that accompanies it, accessible to people regardless of disability is a noble aim.

But it's not entirely clear why this meant the colour had to go altogether. Couldn't these...

...simply have been added to the existing map, without losing the line colours?

One possible explanation for why they weren't: the changes aren't – or at least, aren’t exclusively – about accessibility after all. Unlike the trains on London's tube or New York's subway, all the trams on Manchester's Metrolink are crowded into a small number of routes across the city centre.

The colour scheme means you end up with a bit that looks like this:

Five coloured lines along the same stretch of track. As more branches have opened, more colours have been added, making the map prettier but increasingly unwieldy.


What impact the opening of the Second City Crossing through Exchange Square will have on all this remains to be seen. It’s not yet clear whether different routes will use different bits of track, or whether most will use both. (The two crossings are only a few hundred metres from each other.) If the latter, though, you’d end up with two adjacent multicoloured strips, making the map almost unreadable.

So, the colour scheme has gone, and all that is left is grey. Pity. 

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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.