Driving in London has been falling since 1990. Has the city passed "peak car"?

Which lane is the future? Image: Getty.

Cars are one of the biggest threats to the planet. The transport sector accounts for more than 60 per cent of global oil consumption and about a quarter of energy-related carbon emissions.

It's also seen as harder to decarbonise than other parts of the economy. Typical forecasts of future world vehicle ownership point to substantial increases, particularly in the developing economies.

But the problem of transport-related greenhouse gases may be less than generally thought. There is emerging evidence that individual car use, as measured by the average annual distance travelled, has ceased to grow in most of the developed economies – a phenomenon that started well before the recent recession. In some countries, it may already be declining, a phenomenon known as “peak car”.

A number of factors could could contribute to this trend. Suggestions have included a decline in the number of younger people holding driver’s licences, changes to company car taxation and the technological constraints that stop us travelling faster on roads. It may also be that we have simply sufficient daily travel to meet our needs.

There has also been a shift away from car use in urban areas. This could be particularly important in a world where future population growth will be mainly urban, and where densely populated cities are seen as a driver for economic growth.

For example, over the past 20 years the population of London has been growing and incomes have been rising – yet car use has held steady at about 10m trips a day. This is mainly because the city has not increased road capacity but instead has invested in public transport.

Most importantly, rail offers speedy and reliable travel for work journeys compared with the car on congested roads. This gets business and professional people out of their cars, which makes the city a less congested and more agreeable place to be.

With a growing population but static car use, London has seen a marked decline in the share of journeys by car, from 50 per cent of all trips in 1990 to 37 per cent currently. With continued population growth projected and more investment in rail planned, the share of trips by car could fall to 27 per cent by mid-century. There is every reason to suppose that London will continue to thrive as car use declines – and perhaps because car use declines.

This decrease in car use from 1990 was preceded by a 40-year period of growth from 1950. That was the result of rising incomes, leading to increased car ownership – and, at the same time, a falling population, as people left an overcrowded damaged city for new towns, garden cities and greener surroundings. So we see a marked peak in car use at around 1990, the time when the population of London was at a minimum, which was when attitudes to city living began to change.

Screenshot from David Metz's 2015 paper, "Peak Car in the Big City: Reducing London's transport greenhouse gas emissions".

This phenomenon of peak car in big cities is not unique to London, although this is the city for which we have the best data. There is evidence for something similar happening in Birmingham, Manchester and other British cities, as well as those in other developed countries. The shift in economies from manufacturing to services is an important driver, as is the growth of higher education located in city centres, attracting young people for whom the car is not part of their lifestyle.

If car use has really peaked, both in the sense of national per capita figures and the share of trips in cities, it should help mitigate greenhouse gas emissions from transport. I have estimated that these changes in behaviour, taken together with expected developments of low-emission vehicles, could by 2050 reduce UK surface transport greenhouse gas emissions by 60 per cent of their 1990 level. This falls short of the overall target of an 80 per cent reduction, but it's a good deal better than conventional projections.

Peak car is not just an emerging phenomenon to be investigated. It is a helpful trend to be encouraged, to achieve both successful, sustainable cities and national reduction of transport greenhouse gas emissions. The Conversation

David Metz is a visiting professor in transport studies at University College London.

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

 
 
 
 

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.