Tag Archives: cars

What fax machines can teach us about electric cars

Jonathan Coopersmith, Texas A&M University

Imagine if you could gas up your GM car only at GM gas stations. Or if you had to find a gas station servicing cars made from 2005 to 2012 to fill up your 2011 vehicle. It would be inconvenient and frustrating, right? This is the problem electric vehicle owners face every day when trying to recharge their cars. The industry’s failure, so far, to create a universal charging system demonstrates why setting standards is so important – and so difficult.

When done right, standards can both be invisible and make our lives immeasurably easier and simpler. Any brand of toaster can plug into any electric outlet. Pulling up to a gas station, you can be confident that the pump’s filler gun will fit into your car’s fuel tank opening. When there are competing standards, users become afraid of choosing an obsolete or “losing” technology.

Most standards, like electrical plugs, are so simple we don’t even really notice them. And yet the stakes are high: Poor standards won’t be widely adopted, defeating the purpose of standardization in the first place. Good standards, by contrast, will ensure compatibility among competing firms and evolve as technology advances.

My own research into the history of fax machines illustrates this well, and provides a useful analogy for today’s development of electric cars. In the 1960s and 1970s, two poor standards for faxing resulted in a small market filled with machines that could not communicate with each other. In 1980, however, a new standard sparked two decades of rapid growth grounded in compatible machines built by competing manufacturers who battled for a share of an increasing market. Consumers benefited from better fax machines that seamlessly worked with each other, vastly expanding their utility.

At present, there is not a single standard for plugs to recharge electric vehicles. That means that people who drive electric cars can’t rely on refueling at any of a wide range of nearly ubiquitous stations on street corners the way gas-vehicle drivers can. This creates an additional barrier, slowing the adoption of electric cars unnecessarily. Several potential standards are competing in the marketplace now; as we saw with fax systems, the sooner one standard becomes dominant, the sooner the electric vehicle market will take off.

Making a new standard

The two basic approaches to creating standards involve letting the market decide or forging a consensus among participants. Both have benefits and risks. A free-market approach often splits a young market into several competing and incompatible systems. Believing in their technical or commercial superiority, firms gamble that they will create de facto standards by dominating the market.

In reality, as my research into the first two attempts at standards for fax machines in the 1960s and 1970s showed, competing incompatible equipment can slow the growth of an entire market. In the case of the fax, poorly written standards attempted to codify into common use certain fax machine manufacturers’ methods for connecting two machines and sending information between them. As a result, many firms sold machines that could not work with other companies’ devices. Some manufacturers even deliberately made their machines incompatible to lock their customers into their equipment.

No single firm dominated the marketplace, and nobody agreed to use a single common standard. As a result, the fax world consisted of several smaller self-contained markets, not one larger market. And many potential users didn’t use faxes at all, preferring to wait until an obvious winning standard emerged.

Third time’s the charm

Crowning that winner can take many years. So can creating standards by consensus. In the meantime, the spread of fax technology stagnated.

But then a force outside the marketplace began to call for a real fax standard. In 1977, the Japanese government pushed competing Japanese firms and telephone corporations to cooperate and create one standard. The government then convinced the International Telecommunications Union to adopt this as the worldwide standard in 1980. What ensued was the fax boom of the 1980s and 1990s.

This standard found two keys to its success. First, it was royalty-free, meaning any company could adhere to the standard without paying a fee to its creators. (A similar approach decades earlier proved essential for the adoption of standard dimensions for shipping containers.) The Japanese officials and companies calculated that the profits from a larger market would more than compensate for any lost income from the lack of licensing fees.

A modern fax machine.

Second, the standard was not so restrictive as to prevent fax machine manufacturers from introducing other features – such as faster transmission. That allowed companies to compete on more than just price. The result was a continued flow of new, more capable and cheaper machines that attracted new users.

The need for a standard for electric cars

Successfully commercializing electric vehicles will similarly depend on the development, acceptance and implementation of standards. So far, just as happened with fax machines, incompatible chargers have slowed the spread of electric cars.

Depending on the type of car and its age, it may have one of four incompatible chargers. If the charging station you pull up to lacks the appropriate charger for your car, you are out of luck.

People considering buying electric cars already worry about how far they could travel between recharge stops. Then they realize that they can’t use just any charging station – the way a gasoline-powered vehicle can use any gas station. That doesn’t relieve their concerns and dampens sales of electric vehicles.

Developing a standard

Like fax machines, electric vehicles’ incompatibility reflected both evolving technology and groups of manufacturers promoting their own systems in hopes of dominating the marketplace. Already, the first generation of chargers is essentially obsolete because they take so long to recharge a car battery.

The real battle is among the three incompatible fast charging systems available in the United States: the Japanese CHAdeMO, the European-American CCS and Tesla Supercharger. (China is developing its own standard.)

CHAdeMO works only with Japanese and Korean vehicles like the Nissan LEAF and Kia Soul. CCS works only with European and American cars like the BMW i3 and Chevy Spark. The third system, Tesla’s Supercharger, works only with Tesla’s own cars. Tesla sells its customers a US$450 adapter to use a CHAdeMO charger but does not offer adapters that would let CHAdeMO or CCS vehicles use Tesla charging stations.

The end of the battle?

This three-way split is changing. In the last few years, Tesla has veered from its initial exclusivity to cooperation. In 2014, Tesla announced it would share its patents royalty-free – including its charger and plug designs – to encourage the spread of electric vehicle technology. In 2015, the company agreed to make its cars and charging stations compatible with China’s new standard, possibly by using adapters at charging stations.

And in 2016, Tesla joined CharIN, an industry group promoting the CCS standard. That raised the tempting possibility that the company might allow CCS charging at Tesla stations, probably by providing adapters. It also threw Tesla’s significant support behind an effort to create a new standard for even faster charging. This could lead CCS to market dominance, effectively establishing a standard by out-competing CHAdeMO.

Fax machines needed three generations of standards before real compatibility emerged, thanks to Japanese government pressure to cooperate. For electric vehicles, Telsa’s embrace of CharIN may provide that needed pressure. The real winner would be the cause of electric vehicles.

Jonathan Coopersmith, Professor of History, Texas A&M University

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

Why UPS drivers don’t turn left and you probably shouldn’t either

Graham Kendall, University of Nottingham

It might seem strange, but UPS delivery vans don’t always take the shortest route between stops. The company gives each driver a specific route to follow and that includes a policy that drivers should never turn through oncoming traffic (that’s left in countries where they drive on the right and vice versa) unless absolutely necessary. This means that routes are sometimes longer than they have to be. So, why do they do it?

Every day, along with thousands of other companies, UPS solves versions of the vehicle routing problem. In these mathematical problems, you are given a set of points and the distances between them, and you have to find the best route(s) to travel through all of them. Best is usually defined as the route with the shortest overall distance.

Vehicle routing problems are used to organise many things, from coping with more delivery trucks in cities and hailing taxis to catching chickens on a farm. The concept was introduced by George Dantzig in 1959. Over 50 years later, and despite a large body of scientific research, scientists are still looking for new ways to tackle the problem.

Vehicle routing problems involve finding the best route between points.
Wikipedia Commons

UPS have moved away from trying to find the shortest route and now look at other criteria to optimise the journey. One of their methods is to try and avoid turning through oncoming traffic at a junction. Although this might be going in the opposite direction of the final destination, it reduces the chances of an accident and cuts delays caused by waiting for a gap in the traffic, which would also waste fuel.

UPS have designed their vehicle routing software to eliminate as many left-hand turns as possible (in countries with right-hand traffic). Typically, only 10% of the turns are left turns. As a result, the company claims it uses 10m gallons less fuel, emits 20,000 tonnes less carbon dioxide and delivers 350,000 more packages every year. The efficiency of planning routes with its navigation software this way has even helped the firm cut the number of trucks it uses by 1,100, bringing down the company’s total distance travelled by 28.5m miles – despite the longer routes.

It seems incredible that not turning left can lead to such significant savings. The TV series Mythbusters tested this idea and confirmed that, despite many more turns, the policy of only turning right does save fuel. In their one truck experiment they travelled further, but when you scale this up to a global level, UPS really does travel fewer miles in total.

The success of UPS’s policy raises the question, why don’t we all avoid turning left (or right, depending on what country we’re in), as we drive around cities on our daily commutes? If everyone did it, the carbon savings would be huge and there’d probably be far less congestion.

The problem is that not every journey would be made more efficient by following this strategy, and most people are likely only to change their driving style if they personally benefit.

Driver’s dilemma

As with anything related to reducing climate change, if everybody else did it then things would get better and you wouldn’t have to change your lifestyle at all to benefit. But it only needs a few people to not cooperate and the whole system breaks down.

This is a good example of the prisoner’s dilemma, the famous game theory problem. If everybody cooperated then the system as a whole would be much better, but the best thing for an individual when everyone else is cooperating is to be uncooperative and reap the rewards of everybody else’s sacrifices.

So, if you cannot persuade people to always turn right (or left) for the benefit of everyone, it might be down to governments to encourage or even enforce the strategy. For example, we could plan roads that make it more difficult to turn through the traffic. It would take a brave city planner to implement this, but if UPS can save 10m gallons of fuel, how much could a whole city or even a whole country save?

Graham Kendall, Professor of Computer Science and Provost/CEO/PVC, University of Nottingham

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

Helping autonomous vehicles and humans share the road

Jeffrey C. Peters, Stanford University

A common fantasy for transportation enthusiasts and technology optimists is for self-driving cars and trucks to form the basis of a safe, streamlined, almost choreographed dance. In this dream, every vehicle – and cyclist and pedestrian – proceeds unimpeded on any route, as the rest of the traffic skillfully avoids collisions and even eliminates stop-and-go traffic. It’s a lot like the synchronized traffic chaos in “Rush Hour,” a short movie by Black Sheep Films.

‘Rush Hour’ by Black Sheep Films.

Today, autonomous cars are becoming more common, but safety is still a question. More than 30,000 people die on U.S. roads every year – nearly 100 a day. That’s despite the best efforts of government regulators, car manufacturers and human drivers alike. Early statistics from autonomous driving suggest that widespread automation could drive the death toll down significantly.

There’s a key problem, though: Computers like rules – solid, hard-and-fast instructions to follow. How should we program them to handle difficult situations? The hypotheticals are countless: What if the car has to choose between hitting one cyclist or five pedestrians? What if the car must decide to crash into a wall and kill its occupant, or slam through a group of kindergartners? How do we decide? Who does the deciding?

So far, our transportation system has evolved to be operated by humans, who are good at following guidelines but often interpret them to properly handle ambiguity. We stop midblock and wave a pedestrian across, even though there’s no crosswalk. We cross the double yellow line to leave cyclists enough room on the shoulder.

Improving our transportation system to take advantage of the best of machines and humans alike will require melding ambiguity and rigid rules. It will require creating rules that are, in certain ways, even more complex than what we have today. But in other ways it will need to be simpler. The system will not only have to allow automated drivers to function well: It must be easily and clearly understood by the humans at its center.

Human decision-making

Google cars, Uber self-driving cars, autonomous taxis in Singapore, Tesla’s autonomous mode and even self-driving freight trucks are already on the road. Despite one fatal crash – of a Tesla on autopilot – autonomous vehicles are still safer than a normal human driver. Nevertheless, that crash attracted a lot of media attention.

Among the roughly 100 deaths a day on U.S. roads, this one stood out because people wondered: If the driver was not relying on the autonomous software, what would have happened? What might the human have done differently?

That specific fatal crash was actually fairly straightforward: The car didn’t see a truck in front of it and drove into it. But when people think about accidents, they often worry about having to make moral choices in an instant.

Philosophers call this the “trolley problem,” after a hypothetical example in which a trolley is hurtling down a track toward some people who cannot get out of the way in time. You have the option to switch the trolley onto a different track, where it will hit some other people.

Switch the trolley, or don’t?
McGeddon, CC BY-SA

There are an infinite number of variations on the problem, created by specifying the numbers and types of people, replacing them with animals, sending the trolley into a wall where its passengers die, and more. Would you, for example, save five children and let a senior citizen die? What about saving a dog versus killing a criminal? You can try out many of these variations – and make new ones – online in a fascinating “Moral Machine” game from which MIT researchers are gathering information on what decisions people make. They hope to find at least some human moral consensus, which can then inform autonomous vehicles and other intelligent machines.

The crux of the problem is whether you choose to switch the trolley or not. In one case, you make an active decision to intervene, deciding to save – and kill – certain groups. In the other, you choose not to act, effectively letting fate take its course. People who use the Moral Machine can see how their results compare to everyone else’s. So far the outcomes suggest that people intervene to save younger, fitter people with higher perceived social values (doctors over criminals, for example).

Human – and computer – preferences

To handle these relative preferences, we could equip people with beacons on their cellphones to signal nearby cars that they are a certain type of person (child, elderly, pedestrian, cyclist). Then programmers could instruct their autonomous systems to make decisions based on priorities from surveys or experiments like the Moral Machine.

But that raises serious problems. For example, would an autonomous car that noticed a child running in the middle of traffic decide to run over your grandmother on the sidewalk instead?

What should an autonomous car do here?
Kids on bikes via shutterstock.com

And what about groups of people? The Moral Machine’s creators and other researchers found that society as a whole has a strong preference for choosing to save more people. What if a negligent group of runners steered a car into your path while you walked alone?

The same study also showed that people would be less willing to purchase a vehicle that could include sacrificing the driver (themselves) as an option. If society as a whole is to benefit from the advantages of autonomous vehicles, we need people to buy the cars – so we need to make them more attractive to buyers. That might mean requiring cars to save drivers, as Mercedes has already decided to do.

Breaking the rules

Investigating the trolley problem reveals that “optimizing” for countless specific, but hypothetical, scenarios is not the solution. Further, if we allow autonomous vehicles to break the rules sometimes, under certain circumstances, perhaps malicious humans could game the system. For instance, a pedestrian could walk out in front of traffic without getting hit, but forcing cars to slam on the brakes. That one person might even cause multiple collisions, causing disruption without great risk to the disruptor.

Volvo has already noticed that some human drivers behave like bullies around autonomous cars. For example, a person might cut off an autonomous vehicle because he is confident the other car will avoid a collision itself. As a result, Volvo will not follow the currently common practice of clearly labeling autonomous cars on public roads. At least some of its test vehicles will remain unmarked, in hopes of measuring differences in human drivers’ behavior.

The Mercedes and Volvo developments are the first steps toward trying to clarify human expectations about autonomous cars. By standardizing people’s perceptions, it will be easier to predict what humans will do in different scenarios. That will help us engineer ways to keep everyone driving in harmony.

A common set of rules for all autonomous vehicles – whatever those are – will allow people to predict the cars’ behavior and adjust our behavior, policy and transportation infrastructure accordingly.

And if we’re going to make clearer rules, perhaps humans should follow them more closely too, as pedestrians, cyclists and drivers. In that world, we probably won’t find the perfect chaos of the “Rush Hour” short film. But it will be much more orderly – and safe and efficient – than today.

Jeffrey C. Peters, Postdoctoral Fellow in Studying Complex Systems, Stanford University

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

How maths and driverless cars could spell the end of traffic jams

Lorna Wilson, University of Bath

Being stuck in miles of halted traffic is not a relaxing way to start or finish a summer holiday. And as we crawl along the road, our views blocked by by slow-moving roofboxes and caravans, many of us will fantasise about a future free of traffic jams.

As a mathematician and motorist, I view traffic as a complex system, consisting of many interacting agents including cars, lorries, cyclists and pedestrians. Sometimes these agents interact in a free-flowing way and at other (infuriating) times they simply grind to a halt. All scenarios can be examined – and hopefully improved – using mathematical modelling, a way of describing the world in the language of maths.

Mathematical models tell us for instance that if drivers kept within the variable speed limits sometimes displayed on a motorway, traffic would flow consistently at, say, 50mph. Instead we tend to drive more aggressively, accelerating as soon as the opportunity arises – and being forced to brake moments later. The result is greater fuel consumption and a longer overall journey time. Cooperative driving seems to go against human nature when we get behind the wheel. But could this change if our roads were taken over by driverless cars?

Incorporating driverless cars into mathematical traffic models will prove key to improving traffic flow and assessing the various conditions in which traffic reaches a traffic jam threshold, or “jamming density”. The chances of reaching this point are affected by changes such as road layout, traffic volume and traffic light systems. And crucially, they are affected by whoever is in control of the vehicles.

In mathematical analysis, dense traffic can be treated as a flow and modelled using differential equations which describe the movement of fluids. Queuing models consider individual vehicles on a network of roads and the expected time they spend both in motion and waiting at junctions.

Another type of model consists of a grid in which cars’ positions are updated, according to certain rules, from one grid cell to the next. These rules can be based on their current velocity, acceleration and deceleration due to other vehicles and random events. This random deceleration is included to account for situations caused by something other than other vehicles – a pedestrian crossing the road for example, or a driver distracted by a passenger.

Adaptations to such models can take into account factors such as traffic light synchronisation or road closures, and they will need to be adapted further to take into account the movement of driverless cars.

In theory, autonomous cars will typically drive within the speed limits, have faster reaction times allowing them to drive closer together and will behave less randomly than humans, who tend to overreact in certain situations. On a tactical level, choosing the optimum route, accounting for obstacles and traffic density, driverless cars will behave in a more rational way, as they can communicate with other cars and quickly change route or driving behaviour.

It all adds up

So driverless cars may well make the mathematician’s job easier. Randomness is often introduced into models in order to incorporate unpredictable human behaviour. A system of driverless cars should be simpler to model than the equivalent human-driven traffic because there is less uncertainty. We could predict exactly how individual vehicles respond to events.

In a world with only driverless cars on the roads, computers would have full control of traffic. But for the time being, to avoid traffic jams we need to understand how autonomous and human-driven vehicles will interact together.

Of course, even with the best modelling, cooperative behaviour from driverless cars is not guaranteed. Different manufacturers might compete to come up with the best traffic-controlling software to ensure their cars get from A to B faster than their rivals. And, like the behaviour of individual human drivers, this could negatively affect everyone’s journey time.

But even supposing we managed to implement rules that optimised traffic flow for everyone, we could still get to the point where there are simply too many cars on the road, and jamming density is reached. Yet there is still potential for self-driving cars to help in this scenario.

Are we nearly at a mathematical solution yet?

Some car makers expect that eventually we will stop viewing cars as possessions and instead simply treat them as a transport service. Again, by applying mathematical techniques and modelling, we could optimise how this shared autonomous vehicle service could operate most efficiently, reducing the overall number of cars on the road. So while driverless cars alone might not rid us of traffic jams completely by themselves, an injection of mathematics into future policy could help navigate a smoother journey ahead.

Lorna Wilson, Commercial Research Associate, University of Bath

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

Driverless cars will change the way we think of car ownership

Hussein Dia, Swinburne University of Technology

The transition to fully driverless cars is still several years away, but vehicle automation has already started to change the way we are thinking about transportation, and it is set to disrupt business models throughout the automotive industry.

Driverless cars are also likely to create new business opportunities and have a broad reach, touching companies and industries beyond the automotive industry and giving rise to a wide range of products and services.

The introduction of autonomous driving technology will be gradual.
Mojomotors, Author provided

New business models

We currently have Uber developing a driverless vehicle, and Google advancing its driverless car and investigating a ridesharing model.

Meanwhile, Apple is reportedly gearing up to challenge Telsa in electric cars and Silicon Valley is extending its reach into the auto industry.

These developments signal the creation of an entirely new shared economy businesses that will tap into a new market that could see smart mobility seamlessly integrated in our lives.

Consider, for example, the opportunity to provide mobility as a service using shared on-demand driverless vehicle fleets. Research by Deloitte shows that car ownership is increasingly making less sense to many people, especially in urban areas.

Individuals are finding it difficult to justify tying up capital in an under-utilised asset that stays idle for 20 to 22 hours every day. Driverless on-demand shared vehicles provide a sensible option as a second car for many people and as the trend becomes more widespread, it may also begin to challenge the first car.

Results from a recent study by the International Transport Forum that modelled the impacts of shared driverless vehicle fleets for the city of Lisbon in Portugal demonstrates the impacts. It showed that the city’s mobility needs can be delivered with only 35% of vehicles during peak hours, when using shared driverless vehicles complementing high capacity rail. Over 24 hours, the city would need only 10% of the existing cars to meet its transportation needs.

The Lisbon study also found that while the overall volume of car travel would likely increase (because the vehicles will need to re-position after they drop off passengers), the driverless vehicles could still be turned into a major positive in the fight against air pollution if they were all-electric.

It also found that a shared self-driving fleet that replaces cars and buses is also likely to remove the need for all on-street parking, freeing an area equivalent to 210 soccer fields, or almost 20% of the total kerb-to-kerb street space.

Other studies have also shown that dynamic ridesharing using driverless vehicles will increase vehicle utilisation up to eight hours per day.

Car insurance

A recent study by McKinsey on disruptive technologies suggests that up to 90% of all accidents could be prevented by driverless vehicles. So why buy insurance if automation makes accidents far less likely?

“The truth is, if it’s a safer way of driving, it’s good for society and it’s bad for our insurance business,” the US business magnate Warren Buffet said recently when asked about the impact driverless vehicles may have on his GEICO car insurance subsidiary.

“Anything that cuts accidents by 30%, 40%, 50% would be wonderful, but we won’t be holding a party at our insurance company.”

Other studies have speculated that premiums could be reduced by 75%, especially if drivers are no longer required to get coverage, and liability is shifted from drivers to manufacturers and technology companies.

Under this scenario, insurers might move away from covering private customers from risk tied to “human error” to covering manufacturers and mobility providers against technical failure.

A Rand Corporation report also predicts that drivers might end up covering themselves with health insurance instead of vehicle insurance.

Will driverless vehicles destroy the very idea of ownership?

Does all this mean car ownership is passé? In some ways, you may not own every facet of your driveless car anyway. Vehicle manufacturers are arguing that since they own the software that runs a connected vehicle, they also own the machine that runs that program.

In comments submitted to the US Copyright Office, vehicle manufacturers argue that purchasers are only licensing the product and it would be unsafe for them to modify the vehicle programming or even make a repair. The Copyright Office is currently holding a hearing on the issue. If it rules in favour of the manufacturers, it will set a precedent that can change the whole landscape of vehicle ownership.

Not everyone will be excited by this vision, and many would be sceptical and disagree that we are at the cusp of a transformation in mobility. Others still want to drive and not everyone is likely to want to rideshare on a daily basis. Many might also argue that better investment in public transport would achieve similar outcomes.

Whether you embrace or object to these scenarios, the reality is driverless vehicles are coming and they will have socio-economic impacts and other effects on our society – some good and some bad.

I see them, along with urban transport technologies, as having a role in delivering new mobility solutions as part of a holistic approach to improve road safety and promote low carbon mobility. The market will ultimately determine whether they can succeed.

Hussein Dia, Associate professor, Swinburne University of Technology

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

Stuck in traffic? Maths can get you on your way

Tim Garoni, Monash University

Mathematics may not be the first thing your mind turns to when you are caught in a traffic jam. Yet mathematics holds the key to understanding how traffic congestion develops, and how to prevent it.

Perhaps one of the best known (and most surprising) mathematical results concerning how traffic flows around a network is Braess’s paradox. In the context of a road network, this is the seemingly counter-intuitive result that even without an increase in traffic, building a new road can actually make every single journey slower.

This can arise because the distribution of cars in a network is determined by the individual decisions of many drivers, each acting to reduce their own personal travel time instead of working as a group to reduce travelling times overall. Such behaviour often results in suboptimal use of the road network.

In fact, closing roads can even improve traffic congestion.

This highlights that there is a lot of very interesting, challenging, and important mathematics that is involved in efficiently transporting human beings around a modern city.

For example, how should public transport be scheduled to minimise people’s travel times? How should traffic signals be operated to reduce congestion? When and where should we build new roads?

Maths holds the answers


As populations continue to increase, tackling the transportation problem becomes ever more challenging. Mathematics has recently yielded some significant wins, however.

For example, mathematical control of the traffic lights at the entrances to Melbourne’s M1 freeway has already increased travel speeds on the M1 by 25% during the morning peak hour. And this was achieved without the cost of building extra lanes.

As regular commuters know all too well however, significant challenges remain. The term “rush hour” now seems quaintly old-fashioned; morning traffic congestion in Melbourne lasts from 6.30am until 9.30am.

The annual cost of congestion to Victoria is estimated to rise from A$3 billion to A$6 billion by 2020. In addition to these economic costs, there is also the negative impact congestion has on the environment, and on people’s quality of life.

Luckily, there are researchers around the world working on these issues.

A new field: jamology

One of these researchers is Katsuhiro Nishinari from the University of Tokyo. Nishinari played a pioneering role in trying to understand the fundamental behaviour of traffic flows from the perspective of theoretical physics and coined the term “jamology”.

His recent work focuses on studying flows of “self-driven particles”. Self-driven particles are individual, autonomous agents who each follow simple rules, governing their behaviour. Understanding the kinds of behaviour that can emerge from large collections of self-driven particles is an active area of theoretical physics, which can be applied from ants to motorists.

It has been found that there are many common features in the way jams form in flows of such self-driven particles, regardless of whether the particles are insects or people. To physicists, the onset of jamming is an example of a “phase transition”, similar in many ways to the phase transition that a pot of water undergoes when it turns to steam.

You can watch a presentation on jamology by Nishinari in the video below.


Dive into a carpool

On a more immediately practical level, much recent effort within the mathematical community has been expended on improving the efficiency of the transport networks we currently have. Breakthroughs in this direction are due in part to technological advances, which allow detailed information on traffic conditions to be constantly collected and processed.

Even more crucial, however, has been the development of improved mathematical models and algorithms to process and harness the new data.

London Permaculture

One novel idea gaining traction is real-time collaborative transport, which is essentially carpooling for the 21st century.

Using smartphone technologies, an app can efficiently match passengers with vehicles using optimisation algorithms and smart user interfaces. Platforms to accommodate real-time ride-sharing are currently being developed at the University of Melbourne by Stephan Winter and collaborators.

The vehicles used in such schemes could be private cars, but they could also be buses that are routed adaptively, to make sure there’s a bus when and where you need it. A group led by Monash University’s Mark Wallace is currently developing scheduling algorithms capable of controlling such adaptive bus schemes.

An even broader vision would be to schedule all transport. If drivers notified the transport system each time they started a journey, it could then schedule road use to balance out traffic across the road system and minimise congestion. Simulations suggest even with a small percentage of drivers using the system, users could halve their travel times.

So the next time you find yourself in bumper-to-bumper traffic, instead of leaning on your horn, try pondering some mathematics. Not only will it save your sanity, it could also save you a lot of time.
Mark Wallace will be presenting an overview of many of above topics at a free public lecture on Tuesday June 18 2013: Cheap solutions to the transport problem from 5:30pm-7pm in Theatre S3, Building 25, of Monash University’s Clayton campus.

The lecture forms part of an international program dedicated to the Mathematics of Planet Earth.

Tim Garoni, Senior Lecturer / ARC Future Fellow, School of Mathematical Sciences, Monash University

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