Traffic

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This week Alastair sits down with Jo Garretty, Managing Director at Salt3 to talk traffic and waste management.

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urban.com.au

Weekly Podcast: Episode 17 - Jo Garretty from Salt3 discusses traffic and waste management

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GUEST OBSERVATION
Scientists, engineers and economists have used equations to solve real-world problems since the invention of basic addition and subtraction. Not sure how to price your apple harvest? Use a supply-and-demand equation to find the ideal price. It seems there’s an equation for everything in life.
Equations are great for modelling well-defined problems where the rules do not change, and as such have been the backbone of pure science. But in today’s ever-growing, fast-shifting society, many problems are too complex to model with a single equation. So how do we deal with complex phenomena such as traffic, the subject of this Conversation series? To that end, Descartes gave us a hint many years ago: "Divide each difficulty into as many parts as feasible and necessary to resolve it."
Agent-based modelling (ABM) is an approach that encapsulates this philosophy and has been gaining in popularity for the past decade. Instead of treating the problem as one entity, ABM looks at modelling the behaviour of each of the smaller elements (agents) within the system.
It is a bottom-up approach where macro outcomes are derived from the activities of individual micro actors. As the agents interact, you gain a better understanding of the system as a whole.
So how does this work for traffic?
Let’s take the example of a traffic jam. Conventionally, scientists have used sophisticated analytical approaches, such as equations, that treat traffic like a flow of liquid. Interestingly, we can replicate the same traffic phenomena by modelling individual cars with two simple rules:
<ol>
<li>
If there’s a car in front of you, slow down.
</li>
<li>
If there aren’t any cars in front of you, speed up (while obeying the speed limit).
</li>
</ol>
These rules are essentially descriptions of what a rational person would do while driving. One could presume that to cause a traffic jam we would need an additional factor such as police checks or traffic accidents. Traffic jams do not just happen by themselves – or do they?
It turns out that those two rules are enough to naturally cause a traffic jam. A traffic jam can occur purely out of internal interactions between cars, and not because of any external factor. In this phenomenon, a congestion “shockwave” travels in the direction opposite to the direction of the cars.
Researchers from several Japanese universities recreated this phenomenon with real cars back in 2008, as shown in the video below.
<iframe src="https://www.youtube.com/embed/Suugn-p5C1M?wmode=transparent&amp;start=0" frameborder="0" width="440" height="260"></iframe>
A shockwave traffic jam recreated.
When an unexpected phenomenon results from the interactions between individuals (agents), this is called emergent behaviour. It is this unique property of agent-based modelling that gives it an advantage over conventional models.
Another benefit is that it is very intuitive to develop an agent-based model. Instead of having to understand the mechanics of traffic jams, all you need to do is define the rules that govern the behaviour of individual cars. It shouldn’t be too surprising that two high school students from Boston originally built the earlier traffic jam model more than 20 years ago.
A good example that demonstrates ABM’s intuitive nature is the modelling of flocking behaviour in birds. It’s fantastically difficult to model mathematically, but flocking behaviour can be replicated in agent-based modelling using three simple governing rules:
1. Separation: steer to avoid crowding local flockmates
<img src="https://cdn.theconversation.com/static_files/files/155/01-Separation-v2.gif?1528257919" width="100%">
Adapted from original graphicby Craig Reynolds.
2. Alignment: steer towards the average heading of local flockmates
<img src="https://cdn.theconversation.com/static_files/files/157/02-Alignment-v2.gif?1528257919" width="100%">
Adapted from original graphicby Craig Reynolds.
3. Cohesion: steer to move towards the average position of local flockmates.
<img src="https://cdn.theconversation.com/static_files/files/156/03-Cohesion-v2.gif?1528257919" width="100%">
Adapted from original graphicby Craig Reynolds.
The resulting model is remarkably similar to real life. The three rules successfully bring out the emergent phenomenon of flocking behaviour.
<iframe src="https://www.youtube.com/embed/MvmN5o6dZ8s?wmode=transparent&amp;start=0" frameborder="0" width="440" height="260"></iframe>
Daniel Shiffman’s implementation of Craig Reynold’s Boids program to simulate flocking. Each bird steers itself based on rules of avoidance, alignment and cohesion.
<h2>From playful toy to policy tool</h2>
In addition to traffic management, agent-based modelling has practical applications in other fields. For example, to understand how infectious diseases such as MERS spread through a city, the author has developed a large-scale ABM simulation that models each individual living in his home town, Suwon, in South Korea.
The governing rules are simple:
<ol>
<li>In the daytime, individuals go to work (or school, depending on their age) and interact with their colleagues (or classmates).</li>
<li>At night, individuals return home to spend time with their family.</li>
<li>Individuals can become infected when interacting with a sick person.</li>
</ol>
Through this simple set of interactions, a vast network of secondary contacts is formed, through which a disease can spread. To ensure that the results represent the city accurately, we carefully selected each individual’s age, gender, family members, and home and work locations, to ensure that they matched regional census statistics on housing, labour and education.
The results revealed that prevention efforts should focus on key schools, where most of the disease spread takes place.
<img src="https://images.theconversation.com/files/213063/original/file-20180404-189801-w2wpb.png?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip" alt="">
Close-up of Suwon city showing all residential buildings. Individuals spend their time with their family within each house, and interact with neighbours from nearby. Author provided
Beyond the above examples, ABM has practical applications in marketing, traffic management, and biomedical fields. As computational power increases, this modelling approach is limited only by imagination in its implementation and use.
Yohan Kim, Research Principal, Institute for Sustainable Futures, University of Technology Sydney
Jay Falletta, Research Assistant, Institute for Sustainable Futures, University of Technology Sydney
Scott Kelly, Research Principal, University of Technology Sydney
This article was originally published on <a href="http://theconversation.com" target="_blank">The Conversation</a>. Read the <a href="https://theconversation.com/traffic-is-complex-but-modelling-using-deceptively-simple-rules-can-help-unravel-whats-going-on-92833" target="_blank">original article</a>.
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j.ballantyne1990-6AQqhLzZaK

Traffic is complex, but modelling using deceptively simple rules can help unravel what’s going on

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GUEST OBSERVATION
Traffic signals give priority to motor vehicles over pedestrians. This inequality undermines many of the stated goals of transport, health and environment policy.
State and city governments say they want to encourage walking and biking for many reasons:
- it is space efficient - it has less environmental impact - it is healthier - it is safer for other travellers - it reduces the numbers of cars on the road, so even motorists should be in favour of other people walking.
To help achieve these goals, road management agencies should reprioritise traffic signals to redistribute delays at intersections from pedestrians to cars.
Planners tend to focus on the long-term decisions, like infrastructure and land development. However, it is the shortest of short-term decisions, how many seconds of green light each movement gets at an intersection, that shapes perception of the feasibility of walking or driving to a destination at a given time. This influences the choice of route, destination and mode of travel.
Traffic signal timing involves maths, so has been historically delegated to engineers. But it also involves values and priorities, and so is the proper subject of public policy.
Since the early 20th-century dawn of what Peter Norton calls “Motordom, street space has steadily been regulated and enclosed. This has limited the rights and privileges of pedestrians while promoting those of drivers, in the name of safety and efficiency. But safety and efficiency for whom?
Pedestrians once crossed the street whenever and wherever they wanted. The introduction of signals prioritised the movement of motor vehicles at the expense of pedestrians, which slowed effective walking speed through the city. Pedestrians now spend roughly 20% of their time waiting at intersections. The consequences of making it easier to drive and harder to walk are consistent with the rise of vehicle-dominated cities.
<iframe src="https://www.youtube.com/embed/ZORzsubQA_M?wmode=transparent&amp;start=0" frameborder="0" width="440" height="260"></iframe>
How Barcelona is taking city streets back from cars.
How the phases of traffic signals work
Pedestrians take longer to cross streets than cars because they are slower. As a result, the "don’t walk” signal flashes before the light turns red.
<img src="https://images.theconversation.com/files/221495/original/file-20180604-177095-712qfo.png?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip" alt="">
 CC BY-ND
But at many intersections it is worse than that. Traffic signals are set to give less green time to pedestrians on a phase (from the time the light turns green to when it turns red, or from “walk” to “don’t walk”) than to motor vehicles, to give vehicles a protected left turn without having to yield pedestrians. Adaptive signal control also extends the green light for cars when these are detected, but not for pedestrians, who aren’t.
<img src="https://images.theconversation.com/files/221486/original/file-20180604-177111-oqxh1v.png?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip" alt="">
CC BY-SA
The average pedestrian arriving randomly at the intersection waits longer than a car. Several factors guarantee this.
<img src="https://images.theconversation.com/files/219524/original/file-20180518-26258-7p3zmj.jpg?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=237&amp;fit=clip" alt="">
 Phases of traffic signals typically mean pedestrians wait longer than cars. Author provided
1. Cycle length
The cycle length (time from the start of the green light to the start of the next green) tends to be longer at busier intersections and busier times of day. A longer cycle length reduces the number of phases per hour, and thus reduces the lost time associated with each phase, when the intersection is not being effectively used by any approach. Lost time can never be reclaimed, so one understands why engineers might want longer cycle lengths if their main objective is to move cars.
However, long cycle lengths disadvantage pedestrians, who stand out in the open exposed to the elements and the exhaust emissions of cars, motorcycles, trucks and buses. Even more significantly, people consistently misperceive travel delay, so the wait feels even longer than it actually is.
2. Actuation/‘beg buttons’
While some signals are “fixed time”, modern signals are “actuated”. This means they respond to the presence of vehicles by adjusting the phasing and perhaps the cycle time.
Either a camera detects approaching vehicles or, more commonly, a sensor in the road, often a magnetic loop. This automatically allows the signal to stay green longer if it detects a vehicle approaching, or turn red sooner when there are no vehicles.
In contrast, for pedestrians, they must push a button to get a walk signal. If they arrive a second too late, they have to wait the entire cycle to get a walk signal. If there are many pedestrians, they don’t get a longer walk signal.
Pushing the “beg button” (so called as the pedestrian must request the signal) twice does not make it come faster or stay green longer. Ten, or a hundred, pedestrians do not make the “walk” light come faster either. The beg button is often positioned out of the way, requiring the pedestrian to walk further than would otherwise be required. A few seconds here, a few seconds there, add up.
There is a reason that traffic engineers don’t allocate automatic pedestrian phases. Suppose the car only warrants a six-second phase but a pedestrian requires 18 seconds to cross the street at a walking speed of 1 metre per second. Giving an automatic pedestrian phase even if there are no pedestrians will delay cars. And there is no sin worse than delaying a car.
3. Co-ordination
<img src="https://images.theconversation.com/files/219525/original/file-20180518-26277-1pxne8m.png?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=237&amp;fit=clip" alt="">
A 1929 traffic engineer’s signal schedule and traffic flow diagram across Market Street, San Francisco (click to enlarge). Green wave set to 10.5mph (about 17km/h). City of San Francisco
First introduced in 1922 in New York City, traffic signal coordination aims to ensure vehicles arrive at the traffic signal when it is green, so they don’t have to stop. By correctly timing signals in sequence, platoons of vehicles move together through a “green wave”.
Let’s say the wave is set for a speed of 40km/h. As long as a car accelerates from the first signal to 40km/h and maintains that, it should hit the following lights on their green phase.
This is relatively easy to maintain on a single road, but is more difficult on a network, especially a complex, asymmetric network. It also works against the idea of actuation, as interruptions to the pattern (extending or contracting phases) change the window in which cars can hit a green light at a given speed.
Of course, just because cars can make a green wave at a speed of 40km/h doesn’t mean pedestrians will make a green wave unless they travel at exactly a divisor of 40km/h (e.g. exactly 5km/h) between intersections. This means that pedestrians will more likely wait at red lights at intersections timed for cars.
Policies to improve the life of pedestrians
One of the world’s most widely deployed traffic signal control systems, the Sydney Coordinated Adaptive Traffic System (SCATS), was developed in Australia. Just as Australia led the way in traffic control to move cars more smoothly, it should lead in pedestrian-oriented traffic control. Those concerned about pedestrians should insist on a number of steps:
<ul>
<li>pedestrians, like vehicles, should be counted automatically at controlled intersections</li>
<li>there should be a pedestrian phase for every movement</li>
<li>traffic signal algorithms must give equal or greater weight to pedestrian time than to vehicle time</li>
<li>pedestrians should get the maximum feasible green time on a phase, rather than the minimum, so pedestrians arriving when the light is green can take advantage of it, and slower pedestrians are not intimidated by cars</li>
<li>pedestrians should get a “leading interval” so they can step into the street on a “walk” signal before cars start to move on a green light, increasing their visibility to drivers</li>
<li>pedestrian phases should be automatic, even if no actuator is pushed – the actuator should make the pedestrian phase come sooner and last longer</li>
<li>many more intersections should have an all-pedestrian phase (known as a “Barnes Dance”) so pedestrians can cross intersections diagonally without having to wait twice.</li>
</ul>
<iframe src="https://www.youtube.com/embed/i0TFGXnZzFs?wmode=transparent&amp;start=0" frameborder="0" width="440" height="260"></iframe>
Pedestrians cross diagonally at some the world’s busiest intersections, including Oxford Circus in the heart of London since 2009.
Numerous other steps could be taken to improve the life of the pedestrian and thus increase their number. Certainly, we can demand more patience from drivers as well.
The advent of autonomous vehicles over the next few decades is unlikely, by itself, to eliminate the need for traffic control. But autonomous vehicles should help increase throughput at intersections, losing less time than human drivers, and leading to cars behaving far more safely.
David Levinson, Professor of Transport, University of Sydney
This article was originally published on <a href="http://theconversation.com" target="_blank">The Conversation</a>. Read the <a href="https://theconversation.com/how-traffic-signals-favour-cars-and-discourage-walking-92675" target="_blank">original article</a>.
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How traffic signals favour cars and discourage walking

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GUEST OBSERVER
The equation doesn’t look pretty. Traffic congestion costs us <a href="https://bitre.gov.au/publications/2015/files/is_074.pdf" target="_blank">billions of dollars</a> each year – so we are told – and population growth is not letting up. When road rage meets large economic costs, it’s little wonder our politicians are desperate to do something.
The trouble is, too often that “something” is a great big new freeway. Building more roads <a href="https://theconversation.com/do-more-roads-really-mean-less-congestion-for-commuters-39508" target="_blank">isn’t the best answer</a>, because the roads we have are mostly up to the job – if only we could make better use of them by spreading traffic out beyond the morning and evening peaks.
Instead of focusing on freeways, governments should change the way we pay for urban roads and public transport. To work out how best to do this, the Grattan Institute <a href="https://grattan.edu.au/wp-content/uploads/2017/10/892-Road-congestion.pdf" target="_blank">has looked at</a> several million Google Maps estimates of travel times in Australia’s largest cities. This analysis reveals both the extent of the problem in Sydney and Melbourne and its city-specific characteristics
For many, the problem is minor
The truth is, for a lot of people, road congestion doesn’t matter much. This is because most people work in a suburb close to where they live.
Chart 1 shows congestion delays for Sydney’s 146 most-common commuting trips.
Chart 1: For many Sydney commuters, congestion is very modest
Additional minutes compared to free flow <a href="https://cdn.theconversation.com/files/188305/area14mp/file-20171002-12155-1detu38.png"><img src="https://cdn.theconversation.com/files/188305/width754/file-20171002-12155-1detu38.png" alt=""></a> The horizontal black line in the coloured bar is the median of all journey-to-work routes, weighted by the number of people who used a car to travel to work on those routes in the 2011 Census reference week. Trip times were estimated by assuming all travel between suburbs was between representative addresses for each suburb. Routes with fewer than 400 such commuters are not included. Grattan analysis of Google Maps, and ABS (2011)
The average delay is small: an average commute at the busiest time of day takes around three minutes longer than the same trip in the middle of the night.
While some commutes are delayed for much longer, it is unusual for trips to take more than ten minutes extra in peak periods.
<h2>Congestion is a problem in the CBD and inner suburbs</h2>
Unsurprisingly, the story is different, and worse, in and around central Sydney and Melbourne. Add in all the trucks and vans, students and tradies, shoppers and people going to appointments, and typical delays for travel on CBD-bound journeys are substantially greater (Chart 2).
Chart 2: On commutes into Sydney’s CBD, the average morning-peak delay is 11 minutes <a href="https://cdn.theconversation.com/files/188306/area14mp/file-20171002-12163-u7fwz0.png"><img src="https://cdn.theconversation.com/files/188306/width754/file-20171002-12163-u7fwz0.png" alt=""></a> The horizontal black line in the coloured bar is the median of all journey-to- work routes, weighted by the number of people who used a car to travel to work on those routes in the 2011 Census reference week. Trip times were estimated by assuming all travel between suburbs was between representative addresses for each suburb. Routes with fewer than 400 such commuters are not included. Grattan analysis of Google Maps, and ABS (2011)
The trends are similar in Melbourne. Travel on CBD-bound journeys is much more delayed than to non-CBD locations.
Chart 3 also shows that delays are noticeably larger in the suburbs that immediately surround Melbourne’s CBD.
Chart 3: Travel in suburbs surrounding the Melbourne CBD is highly delayed
Increase in travel time relative to free flow travel time <a href="https://cdn.theconversation.com/files/188307/area14mp/file-20171002-12132-ps1pb5.png"><img src="https://cdn.theconversation.com/files/188307/width754/file-20171002-12132-ps1pb5.png" alt=""></a> Average delay is calculated as the ratio of trip duration at each point throughout the day to the minimum trip duration observed for that route over the sample period. Based on travel time of representative route samples collected via Google Maps. Weekends and public holidays excluded. Grattan analysis of Google Maps
<h2>Some commutes are frustratingly unpredictable</h2>
Most travellers don’t just care about how long a trip usually takes. How long it could take also matters.
Chart 4 shows that Melbourne’s Eastern Freeway/Hoddle Street corridor has not only some of city’s worst delays, but also some of the least-predictable travel times. Motorists from suburbs to the north-east have to juggle these less-reliable travel times more than those travelling similar distances from other directions.
Chart 4: Travel on routes to Melbourne’s CBD that rely on Eastern Freeway and Hoddle Street are noticeably delayed and unreliable
Increase in travel time as a proportion of free-flow travel time, weekday morning peak, commutes into Melbourne CBD <a href="https://cdn.theconversation.com/files/188308/area14mp/file-20171002-12115-g3b13o.png"><img src="https://cdn.theconversation.com/files/188308/width754/file-20171002-12115-g3b13o.png" alt=""></a> For travel departing between 7am and 9 am. Excludes weekends and public holidays. The boxes cover the 25th to 75th percentiles. The vertical line in each box lies at the median for each city. The ‘whiskers’ on each side of the boxes extend no further than plus or minus 1.5w where ‘w’ is the box width. Observations beyond the lines are plotted as dots. Grattan analysis of Google Maps
<h2>New roads are not the whole answer</h2>
Congestion tends to be worst in the most built-up parts of Sydney and Melbourne, where it would be most costly to construct new roads. This means that even crippling levels of congestion might not justify the construction of astronomically expensive infrastructure.
In any case, new roads often take years to build and can fill up with new traffic of their own.
New roads are important, however, in new suburbs.
The rule for our policymakers should be: build a road whenever the community will gain more from the new road than it will cost, and whenever the new road is a better option for the community than extracting more from the roads we’ve already got. But do not think of new roads as congestion-busting.
<h2>So what should be done?</h2>
Changing the way we use our existing infrastructure through pricing needs to be at the top of the agenda. This mean charging motorists for the congestion they cause.
Sydney and Melbourne need to consider introducing a congestion charge. That doesn’t mean more toll roads – it means charging people who drive at peak times on congested roads a small fee.
Because some people wouldn’t think it worth paying the charge at the busiest times of day, those who did pay would get a quicker and more reliable trip. People who can travel outside of the peaks would not have to pay, because there would be no congestion charge when the roads are not congested.
The increased cost to drivers could be offset by cuts to car registration fees. And any extra money raised by the congestion charge could be spent improving train, tram, bus and ferry services.
International examples show that introducing a congestion charge need not amount to political suicide. An initially sceptical public came quickly to accept, value, the reform when it was introduced in <a href="https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.20.4.157">London</a> and <a href="https://www.transportportal.se/swopec/cts2014-7.pdf">Stockholm</a>.
<img src="https://counter.theconversation.com/content/84774/count.gif?distributor=republish-lightbox-basic" alt="The Conversation" width="1" height="1">The congestion equation for Sydney and Melbourne is only going to get more ugly as both cities continue to grow. We need more sophisticated policymaking to ease drivers’ road rage and frustrations.
<a href="https://theconversation.com/profiles/marion-terrill-169480">Marion Terrill</a>, Transport Program Director, <a href="https://theconversation.com/institutions/grattan-institute-1168">Grattan Institute</a> and <a href="https://theconversation.com/profiles/hugh-batrouney-318476">Hugh Batrouney</a>, Senior Associate, <a href="https://theconversation.com/institutions/grattan-institute-1168">Grattan Institute</a>
This article was originally published on <a href="https://theconversation.com" target="_blank">The Conversation</a>. Read the <a href="https://theconversation.com/stuck-in-traffic-we-need-a-smarter-approach-to-congestion-than-building-more-roads-84774" target="_blank">original article</a>.
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jonathanchancellor-7SQnKwU6yx

Stuck in traffic: we need a smarter approach to congestion than building more roads

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<img src="https://counter.theconversation.edu.au/content/65027/count.gif" alt="The Conversation" width="1" height="1">
GUEST OBSERVER
Road user charging is probably the best idea we have to reduce congestion and to enable better decisions on road investment. Average travel speeds in our cities are decreasing, and congestion is only likely to worsen as our population continues to grow.
Urban Infrastructure Minister Paul Fletcher recently gave an important speech, albeit largely unnoticed, in which he made the case for a universal road user charging scheme. Charging people to drive has previously been the dream of transport and economic policy wonks – serving politicians tend to see the idea as political poison.
Fletcher trod gently, cautioning his Sydney Institute audience that “there is a lot of work to do” and that any move in this direction would be “a ten to 15-year journey”. It is still remarkable that a federal minister even took these first steps. 
Fletcher warned of the potential impact of electric vehicles on fuel excise revenue, but automated vehicles represent an even bigger change.
The future of road use is made unclear by the looming arrival of these vehicles. Despite predictions that these could be the answer to traffic congestion, complications include the interaction of autonomous and traditional vehicles and the complexities of human behaviour.
Autonomous vehicles could even lead to greater congestion. The ease of travel in these vehicles might encourage travellers to take more trips as they reduce the time cost of being stuck in traffic by being able to read emails and stay connected while the car drives itself. Empty vehicles travelling to pick up goods and passengers could further clog roads.
Thus it is prudent to target road congestion now, especially when current strategies aren’t helping much. Building more road capacity or even improving public transport can’t solve congestion.
The best strategy is management of demand via a pricing mechanism that reflects the cost of the congestion caused by one more vehicle on the road. With prices that vary by location, time of day and distance travelled, such a scheme would encourage people to take non-essential trips at a different time, or not at all.
The charge could be efficient, as the trips that are discouraged are those for which the congestion caused outweighs the benefit derived. And it would be fair: drivers adding to the delay faced by others pay more, while those who drive in non-congested areas or at non-peak times pay less.
The ability to observe road users’ willingness to pay for road space will also give a better signal to planners of where additional road capacity will be of value to the community. 
<h2>Don’t treat it as a revenue raiser</h2>
So Fletcher deserves plaudits for raising the issue. But he got one important thing wrong: he said that the fuel excise tax funds road spending.
Pointing out that fuel excise receipts would fall with the advent of more fuel-efficient vehicles, and electric cars in particular, he argued for a road user charging scheme on the ground that it would raise revenue for road spending.
Linking fuel excise to road funding is a furphy and gets us onto the wrong track at the very start of the road-pricing journey. Fuel excise is merely one source of general government revenue and is not in any way hypothecated, meaning pledged by law to be spent on a specific purpose – in this case roads.
It is no more relevant to say that falling excise revenues will put road funding under pressure than it is to say this will put pressure on health spending or the age pension.
Furthermore, about 75 percent of road funding comes from state and local government revenue, while fuel excise is a federal tax. It is true that falling fuel excise receipts would add to the federal government’s deficit problems. But there is no reason why a loss of fuel excise revenue must be replaced by another charge on motorists, or why motorists alone should fund additional road spending.
<h2>Take care to avoid an inefficient, distorting tax 
</h2>
The government should take a holistic approach to repair its pressured budget. It should restrict the most wasteful spending, wherever it is, and introduce or increase the most efficient, fair and simple taxes. It is not helpful to limit our thinking to motorist-based taxes to solve that part of the budget problem caused by falling fuel excise receipts.
The other problem with introducing road user charging as a revenue raiser rather than a congestion reducer is that a scheme designed on those terms is likely to produce poor results.
If we approach the task asking how we can maximise revenue, we’ll end up with charges on the wrong roads, at the wrong times, priced to maximise financial return rather than optimise congestion. For example, we might charge heavily on major roads, just to increase revenue, when some targeted charges on minor roads might do more to reduce traffic. In short, we’ll have one more inefficient, distorting tax.
So kudos to the minister for opening the debate. Let’s talk about road user charging, but let’s talk about what it should really achieve.
If we start by asking the right questions, road user charging could be the best congestion management policy we’ve seen in Australia. It could improve the driving experience without the need for big spending on more road capacity, and make sure we get the most economic and social value from our roads.
Marion Terrill is transport program director, Grattan Institute and author for The Conversation. She can be contacted <a href="https://theconversation.com/profiles/marion-terrill-169480" target="_blank">here</a>.
Owain Emslie is associate, Grattan Institute and author for The Conversation. He can be contacted <a href="https://theconversation.com/profiles/owain-emslie-250445" target="_blank">here.</a>
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