Why has Victoria tube station started to smell like roast meat?

The light at the end of the tunnel. Image: Nick Hilton.

About a year ago, the District line platform at Victoria tube station started to smell a little different. Some said the smell was burgers, some said it was steak. Some said garlic bread, some said onions. Some sick losers said it was burnt track grease or a dead rat. To me, it always smelled like the most delicious roast potatoes, cooked in goose fat.

It was one of those changes that 99 per cent of commuters took for granted, leaving a noble 1 per cent to be perplexed as to why they now left Victoria inexplicably famished. On the internet, the most popular theory was that the smell came from Burger King. Some people are apparently able to discern difference between high-street chains, and, to them, the smell was more Whopper than Big Mac. “To me it's the distinct smell of Burger King,” one said.

Meanwhile, others donned their tin hats. “I'm pretty sure Burger King vent their kitchens onto this platform intentionally and then put adverts up on the station.”

Whilst they’re wrong to point the blame at Burger King (whose nearest branch is some distance away in the station terminal), they did a better job at identifying the smell than me. It is burgers. First reports of the smell emerged on social media in early 2017, at the same time as Bleecker – a gourmet burger chain – opened premises on Buckingham Palace Road, directly over the underground station, and, more tellingly, the District line platform. The roast potatoes I have been smelling are, in fact, chips; the steak or dead rat, depending on your nose, a beef burger. 


To put it simply, the situation has arisen because the District line is a cut and cover line, which is to say that it was created by cutting a deep trench across London, and then covering it with roofing and structures, such as roads and buildings. It is not genuinely subterranean in the sense of its neighbour, the Victoria line. As such, at both the westbound and eastbound ends of the platform there is an exposed area, which, in this case, opens behind commercial premises. Simple.

Because I’m only an occasional visitor to the District Line platforms at Victoria, not to mention a meat eater and general enthusiast for fried goods, I have always enjoyed the smell and assumed that others felt the same. In reality, a lot of people think it smells not just bad, but unacceptably awful.

“The District Line is bad enough without it making your hair and clothes smell terrible,” says Jac, a District line commuter who has waged a one-woman war with TfL on Twitter over the issue. “Even if you are just on the train too near a door you can end up smelling like food for the rest of the day.”

Social media might amplify negative opinions, but there are quite a lot of people who agree with her. The smell has been branded “gross”, “horrendous” and “manky”, but it seems there’s nothing that can be done about it. A spokesperson from TfL told me that all the vents from local businesses and restaurants are legally compliant, and, given that the source is outside the station’s jurisdiction, there’s nothing else they can really comment on.

The basic problem is this: Bleecker ventilate by outputting smutty kitchen air, whilst Victoria ventilates by sucking fresh air down into the platform. The proximity of these two systems, brought together by incompetence rather than malice, means that neither party is culpable or responsible. In the end, it is, as Chris Christie might say, something of a nothing burger.

The air vent at Bleecker. Image: Nick Hilton.

Inside Bleecker, the old ventilation system has been repurposed and repainted into a hipster artefact. It might well be this exact pipe that is providing commuters with their olfactory curate’s egg.

Even though the chronology, geography and evidence of hundreds of noses point to Bleecker as the source, no one from Bleecker was available for comment, and it is impossible to entirely verify this solution without having terrorist-levels of access to the underground system. Either way, they’re unlikely to change this form of inadvertent viral marketing: as one former London Underground worker told me, “TfL could filter the shop vent, but that's a massive cost and pungent aromas are very hard to filter. They could filter their own vent, but again it may not be practical.” The only organisation which might make some headway over the stink are Westminster council, which confirmed it would investigate the situation.

For now, however, vegetarians ought to beware when exiting at Victoria. So long as Londoners maintain their enthusiasm for expensive, deep-fried fast food, the District line’s meaty stench isn’t going away.

 
 
 
 

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.