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The Social Impactsof Road Pricing
Summary and Conclusions

The International Transport ForumThe International Transport Forum is an intergovernmental organisation with 59 member countries. It
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Acknowledgements
This summary report of the ITF roundtable meeting on the social impacts of time and space-based roadpricing was prepared by Stephen Perkins and Nicolas Wagner (ITF) and Joanne Leung (New Zealand
Ministry of Transport). It draws on the input papers prepared by Maria Börjesson, Yves Crozet and
Aurélie Mercier, Stef Proost, and Jillian Anable and Phil Goodwin and presentations by Vincent Van Den
Berg, Luis Martinez (ITF), Mark Darrough and Scott Wilson. The presentations can be viewed on the ITF
website at https://www.itf-oecd.org/road-pricing-roundtable, the input papers are published ascompanion reports also available on the ITF website.

THE SOCIAL IMPACTS OF ROAD PRICING | SUMMARY AND CONCLUSIONS | ITF ROUNDTABLE 170

Table of contents
Executive summary ............................................................................................................................. 5
Road pricing and its social impacts: Scope and objectives ................................................................... 8
Theory and practice of charging for road use ...................................................................................... 9
Current practices ............................................................................................................................. 9
Principles for pricing congestion and road user charges ............................................................... 11
Optimising the allocation of urban space and optimising the use of roads .................................. 12
Public transport pricing ................................................................................................................. 14
Understanding and mitigating social impacts .................................................................................... 15
Results of modelling the overall social impacts of road pricing schemes in Europe ..................... 15Road pricing options and scheme designs .................................................................................... 20
Fiscal impacts of road pricing and subsidies for public transport .................................................. 23
Assessing the distributional impacts of road pricing .......................................................................... 25
Establishing an assessment framework ......................................................................................... 25
Analytical dimensions of vulnerability ........................................................................................... 27
Models for distributional analysis ................................................................................................. 28
Conclusions on the design of road pricing to manage congestion...................................................... 32
References ........................................................................................................................................ 34
List of participants ............................................................................................................................. 37

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Executive summary
What we did
This report examines the distributional impacts of road pricing. It focuses on road pricing systems thatare differentiated by time of use and location and on strategies to minimise or mitigate any negative
distributive impacts. Road pricing has been advocated by economists as the efficient way to manage
congestion. It is controversial because, on introduction, some existing road users gain while others lose.
However, experience shows that where road pricing has been introduced, the new norm is rapidly
accepted and pricing is then supported by the overall population affected.
The report presents the results of an ITF Roundtable held in Auckland, New Zealand, in December 2017.
The Roundtable brought together around 18 experts from eight countries. The experts considered input
papers on the relation of road pricing to peak demand pricing on public transport (Proost 2017), how amore optimal allocation of road space can improve social welfare (Crozet and Mercier, 2017),
assessment of two road pricing schemes in Sweden (Börjesson, 2017) and the kind of detailed modelling
required to understand and mitigate or minimise any negative impacts (Anable and Goodwin, 2017).
Discussions also covered experience with the road pricing scheme in Singapore and presentations from
Australasia, Dutch and ITF experts on the value of detailed modelling.

What we foundRoad pricing provides an effective way to manage congestion. The alternative to pricing congestion isqueuing. While levies that increase the cost of using cars, such as congestion charges, parking fees andfuel taxes can be regressive, they have been found to be progressive in some circumstances.
The revenues from fuel taxes will decline as vehicles use less fuel due to higher efficiency andelectrification. Governments will need to make up the funding shortfall, taxing road use through othermeans. Electronic road pricing that is differentiated by time and place would provide an opportunity toalign prices for road use with the marginal cost of using roads.
From an urban planning perspective, road pricing is as much about containing the demand of car drivers
for road space as allocating road space between road users. A well-designed road pricing regime will
reduce incentives for urban sprawl and less land will be needed for expanding road capacity.
Applying peak pricing to public transport fares in order to balance demand and fund additional services
in the peak could bring large improvements for users with or without road pricing. Improvements comefrom reduced crowding and reduced boarding delays in public transport as well as reduced congestion
on the road as some car-users switch to better managed public transport. Peak pricing on public
transport could accompany road pricing as in cities with an already high share of public transport use,
adding public transport capacity or lowering prices when road pricing is introduced is likely to bring lessbenefit than peak pricing.
Most studies find the overall distributional impact of road pricing schemes to be small. However, there
can be households in pockets of urban areas that are seriously adversely affected. Disaggregated spatial
analysis is useful to help design road pricing schemes to reduce the number of vulnerable households
affected and indicate where investments in public transport can most effectively provide an alternative
to car use.

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Fraud is a consideration as many types of exemption and targeted assistance provide opportunities forfraud. If households vulnerable to having mobility curtailed by road pricing are spread sparsely acrosscities, location-specific mitigation measures are unlikely to be an efficient way to address negative social
impacts. Regardless of whether road pricing is introduced, the tax and social security system is always
the most powerful mechanism for protecting vulnerable households.

What we recommend
Make demand management and congestion reduction the primary objective of road pricing
The primary objective of road pricing should be to cut congestion and manage demand for the use ofprivate vehicles. Other forms of taxation that cost less to administer are recommended if the objective is
simply to raise revenue.
Differentiate road pricing by location and time
Price road use with differentiated kilometre charges to manage congestion and demand. This is much
more efficient than using proxies such as fuel tax alone. The size of distributive effects will depend on
travel patterns by income group and the spatial location of jobs and residential zones. The blunter thepricing scheme the more adverse impacts result because users are not priced strictly according to use.
Fine differentiation of pricing by time of use allows travellers to select their preferred departure time tomatch their willingness to pay. This enables most users to adjust travel patterns rather than be priced off
the road, minimising distributional impacts.
Combine road pricing and public transport planning to improve efficiency
Road pricing needs to be planned in conjunction with the operations of additional public transport
services. Peak pricing should be considered for public transport, with or without road pricing, if current
prices are low and the share of public transport use is already high. Service frequency should be
increased in peak periods to the extent that peak pricing covers the additional cost. Where alternatives
to car use are inadequate, major investment in public transport systems and pedestrian and cycling
infrastructure is called for. Some cities with more dispersed land use and trip patterns may find suchalternatives to be viable only on part of the network in the short term but should plan for more transit
oriented development over several decades. The revenue collected from road pricing is a possible sourceof some of the funding for investment in and operation of alternatives to car use.
Examine the combined effects of scheme design and mitigation to understand distributional impacts
The overall distributional impacts of road pricing are highly dependent on the specific design of thescheme, in particular the location of cordons, when cordons are used as part of the scheme design.
Whether these effects could be mitigated or compensated for through other components in the fiscal
system will need careful assessment and would also depend on the detailed design of the pricing
options. Assessing the social and distributional impacts of road pricing requires examination ofvulnerability by location based on a mix of income, cost burden and adaptive capacity. However, as theoverall distributional impact of time-differentiated pricing is likely to be low, it may be sufficient to relyon simpler approaches for assessing distance, time and location-based network-wide road pricing.

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Consider the use of discounts and exemptions carefully
Exemptions and discounts have been used to improve the acceptance of road pricing schemes and target
assistance to user groups but can jeopardise the effectiveness in managing congestion and are open to
fraud. This has been the case with the London Congestion Charge. Exemptions and discounts for any
users other than emergency services and public transport should only be considered after carefulassessment of their impact on congestion.
Develop road pricing as part of an intervention package to achieve better utilisation of urban space
Road pricing will incentivise more efficient use of the roads, which should have a similar effect to building
new roads in increasing productivity and improving access to jobs. Coupled with investment in public
transport, road pricing will drive more transit oriented urban development and contain sprawl. Better
utilisation of road space will ultimately make city living more attractive, reducing commuting time andemissions of air pollution from traffic.
Reconcile economic, practical and political aspects in the design of road pricing schemes
The cordon charges for road use that are in operation, and also the proposed schemes that have been
turned down, suggest that particular care must go into the design of road pricing proposals. Economic
theory provides invaluable input to scheme design but thorough exchange between decision makers andexperts on the design of congestion pricing schemes is essential. Expert assessment is important as anevidence-based scheme that can increase benefits and reduce negative side-effects, but practical issues
are equally important as implementation is ultimately a political process.
Differentiate charges and consider adopting a rules-based pricing approach
The London, Stockholm, Gothenburg and Singapore cases are all different and have, above all, different
histories. A careful analysis must be made of the extent to which different components can be adapted
to potential road pricing systems elsewhere. Nevertheless, differentiating charges by time and location,according to the distribution of congestion, will always reduce distributional impacts.
Singapore has enjoyed general support for road pricing from the population as their rules-based pricing
approach has made the policy apolitical and provided reassurance that tolls are not adjusted to increase
revenues but, instead, are regularly corrected to maintain levels of service. This approach to pricing also
removes the need to conduct sophisticated modelling to set or modify prices.

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Road pricing and its social impacts:
Scope and objectives
Economists have advocated road pricing as the efficient way to manage congestion since the seminal
work of Pigou (1920) that inter alia examined the external costs of travel demand. He proposed what
have become known as pigouvian taxes to internalise externalities such as pollution and congestion sothat they are taken into account in the decisions taken by individual economic actors. This produces amore optimal allocation of resources.
On busy roads, adding unpriced capacity often provides only temporary relief from congestion.
Additional capacity allows additional trips to be made and the system quickly returns to the equilibrium
determined by the cost of delays and unreliable journey time imposed by congestion (Duranon andTurner, 2011; SACTRA, 1994). Careful use of pricing, differentiated by time and location, can improve
greatly on rationing by queuing, guiding users to avoid peak periods, and to car-pool or shift to public
transport and cycling where they can. Such pricing can reduce travel time variability significantly andcontribute to a more optimal allocation of scarce urban space to roads and between road users.
Road pricing is controversial because, upon introduction, some existing road users gain while others lose.
Drivers with a high value of time gain from reduced journey time, as does much commercial traffic thatcan pass costs on. Drivers with a low value of time and low capacity to pay lose by being obliged tochange departure times, switch to a less convenient mode or pay a charge that exceeds the value of theirtime-savings. It is also controversial because the revenues raised are typically larger than the direct
benefits to drivers; the biggest benefits fall to the community more widely, including people using public
transport.
Experience shows that where road pricing has been introduced, the new norm is rapidly accepted andpricing is then supported by the overall population affected. However, concern over distributional effects
is clearly legitimate whenever a public policy intervention creates gainers and losers. Moreover, anumber of proposed cordon charging systems have been rejected largely because they were perceived
to be unfair by some road users, as for example in Edinburgh. Fairness in relation to the impact of tolls
according to where people happen to live and work (horizontal equity) has been as much an issue as thepotential impact on vulnerable low-income households (vertical equity).
The roundtable meeting convened by the International Transport Forum in Auckland in December 2017
examined recent work on the social impacts of time and space-based road pricing. The context for the
discussions was provided by New Zealand Transport Minister, Phil Twyford, who underlined theimportance of developing a multi-modal transport system where outputs are managed to provide thebest possible outcome for citizens. He also emphasised the role of transit (public transport) oriented
development for shaping the growth of cities and the importance of making housing and land markets
more efficient. Large cities need investment in public transport as the only way to move large flows ofpeople at peak times and provide a reliable alternative to the car. Road pricing is an important demand
management tool and has the potential to steer land use patterns to maximising benefits. He noted thata failure to provide public transport is likely to have a far more regressive impact than charging forcongestion on the roads. The Minister highlighted the need to better understand the interplay between
public transport and road pricing and stressed the importance of understanding the social anddistributional impacts of demand management policies.

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The objectives of the roundtable were to:understand the current state of international practices in conducting social and distributional
impact analysis, specifically applied to road pricing assessment

share information on mitigation strategies to minimise the negative social and distributional
impacts and maximise the effectiveness of such strategies

explore recent developments in social impact assessment and modelling methodologies used to
inform policy design, enhance transparency and identify and mitigate risks.

Work commissioned for the roundtable included a paper on the relation of road pricing to peak demand
pricing on public transport (Proost, 2018) and a paper on the function of road pricing as part of a fullrange of instruments for integrated land use and transport planning to achieve a more optimal allocationof space in an around cities (Crozet and Mercier, 2018). Discussions covered Singapore, where roadpricing has been applied since 1975 as part of a full set of integrated planning instruments,
demonstrating how road pricing can be used to fine tune speeds and schedule delay. Assessment of tworoad pricing schemes in Sweden revealed the broader social impacts of pricing and a high degree of
public support for the systems today temporarily and spatially (Börjesson, 2018). Several presentations
identified the importance of detailed modelling and differentiated pricing in achieving the maximum
benefit from road pricing. Work underway in New Zealand on the assessment framework required tounderstand the distributional impact of road pricing was discussed together with the kind of detailed
modelling required to understand and mitigate or minimise such impacts from theoretical charging
schemes in the UK (Anable and Goodwin, 2018; Bonsall and Kelly, 2005).
This report is organised as follows. The first section looks at the theory and practice of charging for roaduse. Then it examines international experience in assessing and mitigating the distributional impacts ofroad pricing. The third section establishes a framework for the evaluation of distributional impacts andreviews recent developments in related modelling techniques. The report closes with a discussion offindings that are transferable to the development of a potential road charging system for Auckland.

Theory and practice of charging for road use
Current practices
Current taxes and charges related to road use vary greatly across ITF member countries, both in absoluteterms and in relation to expenditure on the roads. In some countries, fuel excise and vehicle duties are
generally insufficient to fund the investments required to meet demand. In others the revenues exceed
expenditure. In the UK, for example, fuel and vehicle excise duty produced revenues roughly three times
expenditure on the roads (Figure 1). The extent to which revenues from these taxes and charges areearmarked to spending on road or transport infrastructure also varies greatly. In the US, the Federal
Highway Trust Fund (which now also funds transit investments) was financed entirely with dedicatedfunds from Federal fuel taxes until 2008, when transfers from General Funds began to be required toensure the Fund remained solvent. Since then the gap has widened with fuel taxes frozen in nominal© OECD/ITF 20189

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terms and fleet average fuel economy improving. In 2016 general funds contributed twice the amount of
the dedicated fund.
The reason why taxes on motoring are used in some countries to contribute to general government
revenues well in excess of road expenditure is that demand is relatively inelastic. Taxing car use is lessdistorting than taxing more elastic consumption of goods and services as it has less impact on the
allocation of resources in the economy.
Figure 1. Public road expenditure and taxation in Great Britain (inflation adjusted at 2015/16 prices)

Source: RAC Foundation (using data obtained from the UK Department for Transport to 2016/17).

The revenues from fuel taxes are declining in most OECD countries as vehicles become more fuelefficient, driven by regulatory and fiscal incentives under climate change policies. As electric vehicles
penetrate the market this trend will intensify and if the ambitious goals for decarbonisation of the sector
set by many countries are met, fuel tax revenues will eventually be extinguished. Governments will needto make up this shortfall and taxing road use by kilometre driven rather than fuel consumed is an option.
Increasing taxes on car purchase and ownership is another alternative.

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Box 1. Road user charging in New ZealandRoad User Charges (RUC) were introduced in New Zealand in 1978 as a means of more efficiently
charging for road use by heavy vehicles and to provide a level playing field for rail and road freightcompetition. It replaced fuel excise duty on diesel and applies to both heavy vehicles over 3.5 tonnes andlight-duty diesel vehicles. A cost allocation model is used to distribute road wear and common costs
between categories of vehicles considering space use, vehicle weight and distance travelled. The modelis regularly run when changes to RUC are considered and the model itself is updated periodically and waslast updated in 2015. RUC is one of the main revenue sources to the National Land Transport Funding(NLTF) in New Zealand and accounts for around 40% of its revenue. Other key funding sources include
fuel excise duty for petrol and gas powered vehicles (around 54%) and motor vehicle registration andlicensing fees (around 6%). The NLTF funds road improvements and maintenance, road safety, public
transport, walking and cycling. Local authorities additionally contribute just under half of the total cost of
improving and maintaining local roads and public transport.
The New Zealand Government has recently introduced a regional fuel tax for Auckland to raise revenue
for transport sector investment, at least some of which will be investment in public transport. The
regional fuel tax applies to sales of petrol and diesel in the Auckland region as of 1 July 2018. The rate is
set at 10 cents per litre, which is less than the variation between retail prices within New Zealand.
A number of short, recently-constructed stretches of national highway are tolled in New Zealand, withflat rate charges to enter the tolled sections designed to recover costs. Tolls can only be applied to newroads and tolls are applied with fully electronic free-flow technology, with payment by internet accountor at selected filling stations. Payment can be made before or after use or by automatic debit.
Compliance is enforced with automatic number-plate recognition cameras.
For the longer term, traffic demand management measures, such as congestion pricing, are being
investigated.

Principles for pricing congestion and road user charges
As Pigou (1920) discussed, the reference level for setting charges to achieve the economic optimum isalignment with marginal social costs. Marginal because we are concerned with the additional costs ofadding one more user to the system, social because as well as private costs we are interested in the costs
to other users of the transport system and to society as a whole, including impacts on safety and theenvironment. The relevant cost for congestion pricing is the cost of the congestion that an additional
vehicle on the road inflicts on the other users of the road (i.e. the external congestion cost, external tothe driver of the additional car). Both short- and long-run costs are important but the short-run costs
provide the basis for charging for the use of transport infrastructure as explained in the following
passage from an earlier ITF report (ECMT, 2003):
There are two fundamental aspects to efficiency: efficient use of the infrastructure that exists and,over the longer term, efficient provision of transport infrastructure in terms of quantity andquality. The use of any road, railway, waterway and port, etc., is optimised when its traffic ischarged the short run marginal costs of using it. When there is ample capacity, charging for theuse of infrastructure is made according to the following main categories of cost: maintenance andadministration; emergency services and other external accident costs; air and noise emissions.
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management charge should be used to balance demand with capacity — in place of rationing bycongestion. This should ensure that capacity is reserved for the highest value uses.
When demand management charges reach levels that generate sufficient revenues to financeexpansion of capacity, this should be a trigger for an assessment of the potential benefits ofinvesting in additional infrastructure. The assessment would have to go beyond financing toconsider the full range of costs and benefits that affect economic welfare, including theopportunity costs (for example of land cleared that could instead be used for housing and offices,etc.) and impacts on landscape, water courses and biodiversity. Projects that pass assessment
would proceed in order to ensure efficient development of transport infrastructure.
When congestion is present and charged for, the capital costs of roads will normally be recovered.
Where there is no congestion, optimal pricing could leave these costs uncovered. Treatingtransport infrastructure as a public good, these costs should be met through general taxation. In
cases where governments seek to recover some or all of these costs directly from users it is mostefficient to do this through fixed charges (such as annual road taxes) in order not to price-off
beneficial use of the capacity available (although this can present different challenges aroundenforcement of such charges and can have mild regressive impacts on access to private motoring).

Optimising the allocation of urban space and optimising the use ofroads
The community is concerned about the use of urban space whereas individuals are concerned more
about travel times. When individuals make travel decisions based only on average cost to them, there
will be excess demand as the external costs to other users are ignored, with pressure to consume more
space for roads than is optimal for the community.
In cities, the space for extra lanes is expensive. Congestion is often network-wide and at points of accessto highways, rather than on specific links. Pricing only part of the road network is likely to be insufficient
to manage congestion, although in Stockholm the effects of the central cordon are felt on the arterial
roads across the urban area. Singapore has applied area-wide road pricing since 1975, with a system thatbegan as a simple cordon and has become progressively more sophisticated, with tolling extended
gradually along highways further out from the central part of the city. Tolls are varied by time of day andby the location of the charging gantry. Levels are reviewed every three months based on data on traffic
speeds and adjusted where necessary to maintain speeds around 20-30 km/h on city roads and45-65 km/h on expressways. The targets are based on traffic flow modelling of optimal speeds (Chin,
2010). Where prices have been increased the response is a 3 to 5% reduction in traffic. In one case, two
successive price increases were required for the response on a particularly busy corridor. The system has
proved effective, maintaining traffic flow as planned.
From an urban planner’s perspective, Crozet and Mercier (2018) stress that optimisation of the use ofurban space rather than increasing speed should be the key function of road pricing. Vehicles occupy
more space at higher speeds, as the distance between vehicles for safe operation increases with speed.
The design speeds in Singapore are in line with their findings, which suggest space consumption isminimised at speeds between 20 and 40 km/h. Traffic composed entirely by automated and connectedvehicles would in principle allow higher speeds, but on city networks with frequent junctions andpedestrian crossings the impact would be negligible. Road pricing commoditises the use of scarce urban
road space, with users paying for what they consume.

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Better utilisation of existing vehicle capacity (e.g. use of shared mobility services) is another way to
improve use of road space. While shared mobility may increase over time, road pricing can be a catalyst
to speed up such behavioural change. Wide adoption of centralised door-to-door shared mobility
services might result in significant reductions in congestion and emissions (ITF, 2017a). However,growing use of shared mobility also raises issues, for example, around the use of curb space for picking
up and dropping off passengers and the management of idle vehicles that add to traffic between rides(ITF, 2018). In the absence of congestion pricing, any reduction in traffic achieved through sharedmobility is in any case likely to be cancelled out by traffic drawn to the improved traffic flow conditions.
In the absence of road pricing, congestion effectively “prices” potential users off the road. Releasing
capacity through shared rides is likely to release pent up demand for car use in the same way as
expanding roads, with congestion returning quite quickly to previous levels.
Road pricing increases economic output by sorting traffic. It allocates road space according to the value
attached to the trip by each road user. It should be effective in achieving better matching of skills andopportunities in the labour market through this sorting mechanism, driving productivity and generating
the kind of agglomeration benefits delivered by infrastructure investment. Peak charging in particularcauses changes in commuting patterns and labour market matching. For example, Anderstig et al. (2016)
found Stockholm congestion charges to have delivered positive labour market effects that are equivalent
to 75% of charge revenue (of EUR 80 million per year). Expert labour is likely to gain most from thistraffic sorting effect.
It was suggested in the roundtable discussions that the impact of road pricing on commuter traffic isperhaps revealed in the neighbouring French and Swiss towns of Annecy and Lausanne. Commuters on
the French motorway system pay a toll with the result that a large number of commuters using the
motorways ride-share. In Switzerland motorway access is subject to an annual fee rather than a toll.
Most commuting trips on the motorways around Lausanne are made by solo drivers. Research on the fullrange of factors influencing behaviour, and covering tolled commuter roads elsewhere in the world,
would be required to establish the extent to which tolling accounts for the difference in car occupancy
between the two cities.
In Singapore road pricing is used to stabilise traffic speeds and journey time reliability in an integrated
transport and land-use planning system, managed with a complete range of instruments (Chin, 2010). In
common with other cities, on-street parking prices and dwell-time limits are key components in traffic
demand management in Singapore’s overall strategy. These play an important part in determining
individuals’ car use decisions. Parking space provision is regulated together with pricing for parking
because insufficient parking space can exacerbate traffic. Minimum parking provision limits apply to newbuildings but because the supply of parking places also incentivises car use, the rules currently allow
developers to provide 20% less car parking than the minimum. Ownership of cars is managed throughauctioned permits and purchase taxes that can be as high as, if not higher than, the value of the car (suchauction systems are also used in some major Chinese cities). Public transport systems are well-developedin Singapore with the highest share of passenger traffic carried by bus. Housing and commercial
developments are authorised in conjunction with extensions to bus services. Road pricing fine-tunes
traffic demand at the apex of the suite of demand management tools.
Road pricing based on road use has been used successfully on highways to manage congestion in several
places (e.g. High Occupancy Toll [HOT] lanes in California and Chinese Taipei’s distance-based payas-you-go electronic toll collection system for using the national motorway network). Notably, on the
I 15 highway in San Diego, a continuously varying toll provides access to reserved lanes that are priced toensure flow on these lanes is always fluid (Supernak et al., 2002). Drivers have the alternative of using© OECD/ITF 201813

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the adjacent, congested lanes. The solution is seen as fair because the toll pays for the extra lanes and,regardless of income (and the average value of time that implies), any driver can benefit on occasions
that they have an imperative reason to take the uncongested lane and pay.
In Chinese Taipei, electronic toll collection was first introduced in 2004. It was applied as a fixed-charge
tolling system for entry to the freeway network. This raised concerns over fairness as the system resulted
in short commuting trips being charged the same rate as longer journeys on the network. Toll stations
outside urban areas applied higher rates to counter this effect but that in turn was discriminatory
towards people living near these entry points. In 2014, distance-based charging was introduced to
improve efficiency and to overcome equity concerns, covering all classes of vehicle using 932 kilometresof tolled roads. To ensure public acceptance, a daily discount is currently applied, with the first
20 kilometres free of charge. Discounts are also available for long distance trips and during holiday
periods. These concessions clearly undermine the value of the system for managing congestion.
Nevertheless the Freeway Bureau estimates that electronic toll collection has reduced the travel time
between Taipei and Kaohsiung (approximately 370 kilometres) by between 20 and 30 minutes. The
Bureau plans to differentiate charge rates by peak and off-peak periods once road users are familiar with
distance charging. It also plans a novel approach to peak holiday traffic. Demand is so intense before andafter the major annual festival that the Bureau believes it is beyond any acceptable peak charge level tomanage. For this period standard rather than peak charges will therefore apply, conceding that service
standards cannot be maintained even with high prices.

Public transport pricing
Although travelling on public transport may take longer than by private car, space consumption per
traveller is much lower at reasonable load factors. Congestion generated essentially by car traffic delays
buses, affecting many more bus passengers than car drivers. In the absence of congestion pricing,second best approaches to managing urban traffic include subsidised public transport fares. Whenprivate transport is optimally priced, however, public transport pricing should be based on the same
principles as pricing for road use; and should take into account of the marginal external cost of anadditional user. Crowding on public transport increases boarding time, slowing travel speed. Crowding
also results in discomfort that further reduces the welfare of users (ITF, 2014). Using simulation resultsfor Stockholm and Paris, Proost (2018) shows that during peak and off-peak periods it is strongly welfare
enhancing to differentiate public transport prices and vary frequency of service. Peak pricing helps tocover the costs of supplying capacity. When peak prices are too low, there is an excess demand forcapacity and with a low revenue base it is difficult to improve or expand public transport services.
Public transport prices may also be set low for social inclusion purposes although a more effective
approach is to aid vulnerable users directly. Bogota is one of the few cities to do this. Its SISBÉN system
for identifying people eligible for support from social security programmes is also used to provide
subsidised travel cards for public transport (ITF, 2017b).
Setting public transport prices too low increases operating deficits and leads to excessive demand,causing crowding and discouraging modal switch away from car use. For example, bus transport is notpeak-charged in London and school children travel for free and 40% of all users enjoy concessionary
fares. School children travelling for free account for 14% of all bus passengers (London Assembly, 2013).
They use buses for short trips in place of walking. This unintended behavioural change – forces
commuters off public transport. Many cities that have trialled or implemented free public transport
services have found that even though it results in some modal shift from cars to public transport, a

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larger, undesired shift from walking and cycling has also been induced (e.g. Macharis et al., 2006; Van
Goeverden et al., 2006; Cats et al., 2017).
Proost (2018) concludes that when public transport prices are set low and the share of public transport
trips is already high, as is the case for most major cities, it is neither efficient nor effective to direct
revenue from road pricing to further reduce public transport prices. Modelling results for Stockholm andParis, he finds only very small welfare gains are achieved from this approach in contrast to the large gains
from optimising prices and frequencies in relation to peak and off-peak demand on the public transportsystem.
For cities with insufficient public transport provision, improving services will increase socio-economic
welfare, with or without road pricing. This effect is particularly important in developing economies,
where transport consumes a major share of income for a large part of the population and wheremotorcycles may offer a cheaper alternative to public transport with their attendant crash risks, noise
and air pollution costs. Any change in fares policy should be assessed for unintended consequences.

Understanding and mitigating social impacts
Results of modelling the overall social impacts of road pricing
schemes in Europe
Eliasson (2016) examined the distributional effects of congestion pricing for an earlier ITF roundtable
meeting (ITF, 2017b). He investigated the fairness of the congestion charges in Stockholm and
Gothenburg and theoretical charging systems in Helsinki and Lyon, from consumer and citizen
perspectives. His consumer analysis takes a standard economic welfare assessment approach, withwelfare effects falling in four parts: the tolls paid; the cost of adapting travel patterns to the charges; thevalue of travel time gains; and the benefit of recycled revenues. Eliasson first examined the equity effectof the toll payments alone; noting that use of the toll revenues is crucial, even if the amount of tolls paid
is a good proxy for the rest of the overall welfare effect. The results are instructive.
The first point is the very different incidence of the tolls across the population in the four schemes(Figure 2). In all four cases the design results in higher income groups paying higher amounts of toll
(Figure 3, left panel) although in two cases the high income group pays less than some middle income
groups. In Helsinki this is because those with the highest incomes tend to live and work in the city centre
and have shorter trips. In Gothenburg it is because company cars are exempt from the charge under
Swedish tax law. Even if the poor pay less than the rich they do pay a higher share of their income in tolls(Figure 3, right panel). Eliasson assesses their impact using the Suits Index (Suits, 1977) and compares
their effects with some other transport sector charges and other taxes (Table 1). The tolls in Stockholmand Helsinki are slightly regressive and those in Gothenburg and Lyon moderately regressive but no more
regressive than the other taxes assessed.

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Figure 2. Share of population who pay various amounts in tolls

Source: Eliasson (2016).

Figure 3. Average toll payments by income class
Total payments

Toll share of income
240Percentage

Total payment(euros/month)

50

30
20
10

Helsinki

1.5

Lyon

1
Gothenburg

0.5

Stockholm

001,000 2,000 3,000 4,000 5,000

1,000 2,000 3,000 4,000 5,000

Income class (euros/month)

Income class (euros/month)

Source: Eliasson (2016).

Table 1. Regressivity/progressivity of congestion charges and other taxes
Tax or charge
Stockholm congestion tax
Helsinki congestion charge model
Gothenburg congestion tax
Lyon congestion charge model
Swedish fuel tax
Swedish differentiated vehicle tax
United States vehicle miles travelled tax
United States differentiated vehicle tax
United States sales tax
Texas gasoline tax
Texas sales tax

Suits Index

Study

-0.09
-0.09
-0.13
-0.16
+0.03
-0.09
+0.14
-0.30
-0.11
-0.25
-0.18

Eliasson, 2016
Eliasson, 2016
Eliasson, 2016
Eliasson, 2016
Eliasson, 2016b
Eliasson, 2016b
West, 2004
West, 2004
Metcalf, 1996
CPPP, 2007
CPPP, 2007

Note: The Suits Index is bounded by -1 and +1. A flat rate tax has an index of 0, a progressive tax has a positivescore and a regressive tax a negative score.
Source: Eliasson, 2016.

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Eliasson (2016) also examined attitudes to congestion charges by income group, broadening theassessment to cover a fuller range of variables relating to self-interest. The number of cars in thehousehold, the value of time savings and the number of car trips taken are considered as well as the costof toll payments. Households were surveyed in the four cities and asked how they would vote in areferendum on congestion pricing. Very little difference in attitudes was found between income groups.
Moreover, in modelling the costs and benefits of each group, Eliasson found very little difference inattitudes to paying one more euro in tolls, one more car trip etc. The effect in relation to any of thevariables tested seemed to be the same regardless of income, probably because for the moderate size ofthe sums considered; differences in the marginal utility of money are not large enough to affectattitudes. Figure 4 shows Eliasson’s results for how much better or worse off each income group
perceives itself to be on average compared to the average citizen in that city after introduction ofcongestion charging. Patterns are remarkably similar, with the lowest income groups benefiting most.
Figure 4. Average “compound self-interest” by income group, relative to the mean in each city

Source: Eliasson, 2016.

With this perspective, congestion charging appears “progressive”. Lower income groups are hurt less, as
perceived by the individuals themselves, on the basis of how self-interest variables influence voting
response. Eliasson then goes on to examine fairness from a “citizen perspective”, on the basis that
depending on an individual’s views of procedural fairness and views on equity and environmental issues,congestion charging can be viewed as more or less fair in an abstract sense regardless of the direct
consumer effect. Winners from congestion pricing reform are people who approve of the underlying
rationale of congestion pricing. Eliasson produced a model to identify how consumer variables and social
attitudes affect views on congestion pricing. He found that from the “citizen utility” point of view
(fairness in relation to social attitudes) it is the middle class that benefit most from congestion pricing.
Eliasson’s findings are consistent with those of Van den Berg and Verhoef (2011), who examine
heterogeneity in values attached to scheduling preferences and travel time using a bottleneck model ofcongestion. A standard result from the economic literature is that, when rescheduling is neglected, users
with a high value of time gain and those with low value of time lose. Although the value of time is a weak
proxy for income, this is often taken as implying that the poor lose and the rich gain. Van den Berg andVerhoef found that this is no longer true if users significantly reschedule their trips and if the main formof congestion is queuing behind bottlenecks. In fact in this case, a majority of travellers are better off
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is that the very fine differentiation of tolls over the day enables spreading of demand so as to dissipate
most of the queuing. The time lost due to queuing is transformed into payments for road use. Moreover,
it is not users with the lowest value of time that incur the greatest losses, or enjoy the smallest gains.
This suggests that it is important to include schedule delay costs as a standard component in theassessment of distributional impacts of congestion pricing. Van den Berg’s conclusion is that wherecongestion mainly takes the form of bottleneck delays, for example mainly at access points to arterial
roads, peak and shoulder pricing can be used to spread peak demand sufficiently to cut congestion whilst
avoiding pricing most lower income users off the road, as almost all car users can reschedule rather thancancel trips.
Eliasson concludes that it is hard to find much support for the view that congestion pricing is unfair, if its
purpose is to correct prices and allocate scarce resources more efficiently. Both in terms of absolute
payments and self-interest, lower income groups fare better than average. From a citizen perspective,
differences are small, but lower income groups fare at least as well as high income groups. This changes,
however, if the purpose of the charging system is in fact to generate revenues. In that case, the (slight)
regressivity of pricing systems is a serious problem: it is difficult to defend making poor groups
contribute more than proportionately to public revenues.
Cain and Jones (2008) narrow the assessment of the distributional impacts of congestion charging to theissue of most concern: does it cause hardship to low income car users and their dependents? They
examined the proposed Edinburgh congestion charging scheme, rejected in a referendum in 2004, using
national Family Expenditure Survey data and the Scottish Household Survey. The authors definedhardship in terms of affordability criteria developed in the utilities sector and set three conditions forroad pricing to result in hardship:
1. The charge payment (or a series of charge payments) would breach affordability criteria.
2. No reasonable alternatives are available to enable the charge to be avoided.
3. A charged trip (or a series of charged trips) is required to access a basic need.
The Edinburgh scheme involved a weekday charge of GBP 2 for inbound crossings of either a city centre
cordon or an outer cordon just inside a bypass ring road. The payment provided for unlimited cordon
crossings that day. The central cordon charge applied 7 am to 7 pm while the outer cordon charge would
have applied only in the morning peak, 7 am to 10 am. No charges were applied at weekends. The charge
was part of an integrated scheme to develop public transport, using the charge to encourage modal shift,with revenues to be recycled into public transport.
Data from Cain and Jones shows that, for Scotland as a whole, only 25% of households in the lowest
quintile income group spend any money on motoring (Figure 5). Figure 6 shows the proportion of
disposable income used for motoring averaged for all households and for households with access to a car
only (labelled as “Spenders Only”). This shows that car-dependent households on low incomes spent amuch higher proportion of their disposable income on motoring. However, this data also illustrates
another important point that focusing on impacts on car users only can ignore the largest (75% in thisexample) part of hardship and equity issues related to transport policy.
The low-income households surveyed did not regard motoring as a luxury but rather a necessity to reachbasic needs and services and responded that they were no more likely to change their car use because ofcongestion charging than people in higher income groups. The charge would have increased hardship forthe poorest drivers especially when they were already spending far above the affordability thresholddefined by the study. For those in the poorest fifth of the population driving to work across the cordon,
motoring costs would increase by up to 20%. This reinforces the need to look into disaggregated data to

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identify at-risk user groups and target mitigation measures. In some cases, varying charges by time of day
may be sufficient to alleviate the road pricing impacts on some lower income earners who work night
shifts or at isolated out of town developments and therefore have limited alternatives to car use.
Figure 5. Proportion of Scottish households in each income quintile incurring some motoring costs (%)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0-20%
20-40%
40-60%
60-80%
80-100%(£4,690 pa) (£9,120 pa) (£14,615 pa) (£21,820 pa) (£38,400 pa)
Source: Cain and Jones (2008).

Figure 6. Proportion of disposable income spent on motoring by income quintile in Scotland
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%

Total Sample
Spenders Only

0-20%
20-40%
40-60%
60-80%
80-100%(£4,690 pa) (£9,120 pa) (£14,615 pa) (£21,820 pa) (£38,400 pa)
Source: Cain and Jones (2008).

A number of studies on the social impacts of congestion charging in Stockholm and Gothenburg havebeen published (Börjesson and Kristoffersson, 2014; West and Börjesson, 2017). They find that
distributional impacts are higher in Gothenburg because of the distribution of incomes and a high levelof car dependency by all income groups. In Stockholm, incomes are much higher than average in thecentral core of the city, which is the area subject to the cordon charge. In Gothenburg there is no such
relationship between where the tolls are charged and the distribution of households and trips by income
group. Overall welfare benefit is much lower in Gothenburg. The charge in Gothenburg is designed toraise revenue rather than manage congestion, which is limited in scale.

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There are significant differences in the spatial impacts of alternative pricing options. This has implications
for the location, coverage and scope of the charging systems as well as the choice of possible mitigation
measures. Eliasson and Mattsson (2006) looked at the net effects of congestion charging in Stockholmwith different measures to channel revenue back to travellers. They assessed the net effect on users of
total charges paid, the value of changes in travel patterns and the value of travel time savings (alsoreferred as value of time). They found a consistent geographical distribution of net effect between thethree revenue recycling options examined: a public transport refund scheme, a lump sum refund scheme
and a tax cut refund scheme. They found that travel costs more towards the city centre and that therural community benefits most from the recycling measures. Crozet et al. (2012) looked at the impacts ofaccessibility to jobs and found similar results using a homogeneous value of time, but the pattern was
reversed when heterogeneous values of time are used. This underlines the need to consider the
distribution of the value of time amongst users in order to understand the distributional impacts ofcharging.
Using administrative records (such as vehicle inspection data and income data) to develop a
microsimulation approach, Mattioli et al. (2017) found diverse spatial patterns for the vulnerability
dimensions studied (cost burden, income and adaptive capacity). They found that inner city areas tend to
be less vulnerable but there are pockets of very high vulnerability between major urban areas. Thisfinding supports the need to use disaggregated information rather than the traditional approach of
focusing on average users.
Bonsall and Kelly (2005) modelled a range of potential congestion charging systems in Leeds. They foundthe populations of users vulnerable to road pricing under the different schemes to be scattered quite
sparsely across the built-up area. This suggests location-specific mitigation measures would have been
unlikely to be effective.
Perhaps surprisingly, once income is controlled for, distributional effects across different socioeconomic
characteristics appear to be relatively small (Eliasson, 2016). He found exceptions in only a few cases; forexample, households with young children would pay more than households without children in Lyon and
Gothenburg and older people would pay less in Lyon.

Road pricing options and scheme designs
Road pricing can be applied in several different ways, summarised in Table 2. The choice of pricing option
and scheme design can have a significant effect on how the impacts of road pricing are distributed
between population groups and between locations. Modelling is important for setting the boundaries ofarea charges and cordons, for minimising rat-running and traffic diversion and for successfully targeting
congestion and avoiding any drift towards simply revenue collection.
The most efficient charging system will charge according to scarcity, so it will charge by distance driven(road space consumed) rather than just when a cordon is crossed, and it will be higher at morecongested times and locations. Prices might vary continuously, or vary according to fixed schedules bytime of day and day of the week. And charges and charging schedules might be revised periodically.
The modelling that underpins Stockholm’s cordon charge, which incorporates peak and off-peak prices,suggested that most of the benefit in terms of congestion management could be achieved with arelatively simple system. Introducing charges just in the centre of Stockholm was beneficial over thewhole of the region as congestion tended to back-up along corridors behind the bridge crossings where
the cordon is applied.

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Generally, however, the blunter the scheme the more losers there will be and the charging scheme
needs to be sufficiently refined to target the congestion problem where it occurs. Singapore has themost sophisticated system in operation today as it is differentiated by time and location. As part of a fullset of integrated land use and transport management tools, it is used to fine-tune traffic speeds. At theother extreme, Gothenburg applies peak and off-peak charges despite the absence of congestion outside
the peak; it generates a net welfare loss (West and Börjesson, 2017). In London the once a day charge
regime has proven ineffective in managing delivery vehicles for e-commerce, one of the fastest growing
sectors of traffic. A fixed daily fee is also unsuited to managing taxis and app-based ride services, anotherfast-growing sector. Singapore’s system is differentiated to the point that it has three daily pricing peaks(and charges are specific to each charging point). In addition to the morning and evening rush hours, a
lot of offices schedule meetings at 2:30 pm generating a third peak in work day traffic. Simplicity mightargue for just two peak periods but the rule-based pricing strategy for determining prices (see Box 2) has
generated the triple peak charge.
Table 2. Options for differentiating road pricing systems
Type*

Main characteristics

Options
Scheme coverage

Cordon charging

Charge for each crossing of acordon delimiting the charging
zone in a city

Location and sizeof cordon

Area charging

Daily charge for driving into orwithin a defined area but noadditional charge for crossing
cordon more than once
Charges for passing points along acorridor in a city

Size and locationof area/s

Variable tolls

Peak charges for already tolled
highways and bridges

Tolled lanes

Tolled lane on un-tolled roadsegment, often
discounted/exempt for highoccupancy vehicles
Uses transponders to enablecharging of any use of the entire
road network or a specified partof the network

Local, Regional,
National(dependent on
toll network)
Number andlocation of
charging points

Corridor charging

Electronic time,
distance andplace-based
charging

Number andlocation of
charging points

Local, Regional,
National,
specificallycongested routes

Fixed or variable charges
Time of day
Day of the week
Number of trips
Vehicle type
Direction of travel
Time of day
Day of the week
Vehicle typeDistance travelledTime of day
Day of the week
Number of trips
Vehicle type
Direction of travel
Time of day
Day of the week
Vehicle typeDistance travelledTime of day
Day of the week
Vehicle type
Vehicle occupancy
Time of day
Day of the week
Vehicle typeDistance travelled

Other
Exemptions
Concessions
Frequency ofrevising prices* It is possible to combine features of more than one of the categories identified in this column and most roadpricing systems do this in practice. For example, area charges usually employ a cordon.

Differentiated charges are fairer from the point of view of charging users according to what they
consume. With a flat rate cordon charge, short trips are penalised in relation to longer trips andvulnerable users who reside just outside the cordon but need to commute to the centre tend to pay
more often. Charging peak and shoulder rates over the full period of the peaks, with reasonably finedifferentiation, brings particular advantages. It enables road users to respond by advancing or delaying© OECD/ITF 201821

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journey time. In principle no user need be priced-off the road when this option is available (Van den
Berg, 2017), at least where traffic is not atrophied by hyper-congestion.

Box 2. Rule-based pricing in Singapore
Singapore’s electronic road pricing system uses prices determined by optimisation of traffic flow. Prices
are set to ensure traffic speeds are maintained at agreed levels: 20-30 km/h on arterial city roads,
45-65 km/h on expressways. Electronic Road Pricing rates are determined by a quarterly review of traffic
speeds of priced roads and during the June and December school holidays. The pricing formula wasdeveloped using a traffic flow model developed by the Land Transport Authority. When speeds fall below
the target levels prices are increased. When speeds rise above the target range, prices are reduced.
The benefit of this rule-based methodology is transparency. This aids understanding for both the publicand decision makers and underpins public support for the system. It also permits prices to be set at thelevel needed to contain congestion and modified when needed, without having to revert to a politicaldecision each time changes are required.

Regarding the congestion problem as a bottleneck, use can be priced to spread demand away from pinch
points in time and space and allow all trips to be made. Some of the lowest income road users have theleast flexibility in their schedules, so the principle may break down in relation to users of most concern interms of social impact, but there is a much larger propensity to adjust schedules and trips than generally
expected. This was demonstrated by monitoring of the responses to the closure of the Forth road bridge
linking Edinburgh to the north of Scotland for maintenance (Marsden et al., 2016). This found retiming oftrips was much higher than predicted.
Bottleneck congestion is probably the dominant feature in Auckland and other cities with a network of
expressways running through the city. Access on and off the expressways is where a lot of congestion
occurs. In such circumstance, applying a uniform charge misses the opportunity to spread the peak. A
differentiated charge would achieve the same overall traffic reduction at an average price that is much
lower than a flat rate charge (Van den Berg, 2017). Distributional effects are complex, but efficiency andequity tend to coincide more with variable pricing, with the availability of alternative modes of transport
being less critical.
The effect of a congestion charge in sorting traffic, providing more space for those able and willing topay, raises social equity issues. Bonsall and Kelly (2005) examine the impact of congestion charge design
on social exclusion and at-risk groups. They examined the likely impacts of six hypothetical congestion
charging schemes in Leeds, UK, modelling a synthetic population with highly disaggregate data on
household composition and income by location from the Small Area Statistics of the UK National
Household Census. They found that the distribution and severity of impacts on at-risk groups depend
crucially on the precise definition of the charge area, the basis of the charges and the exemptions
provided. Their modelling confirmed that charges proportional to distance driven within the charged
area have less serious consequences for those at-risk than flat area fees or cordon charges. They also
found that if a charge is based on crossing a cordon, the cordon can be adjusted to affect fewer at-risk
people.
This type of detailed modelling can be used to go some way to design the system to mitigate impacts on
at-risk communities and design any potential exemptions, although the study concludes it could be
difficult to target help effectively. Road user charging will result in some lower income drivers reducing

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car use and others making economies elsewhere. Those most at risk are on low incomes with no realistic
alternative to make essential journeys. The trip may be too long or their health too poor to undertake onfoot or by bike, public transport service may not exist or be inaccessible to them. Factors for vulnerability
include disability, age, gender, membership of a social minority and specific responsibilities for thetransport of other people. More broadly, lower income jobs tend to have the least flexibility in terms ofworking hours, and people on low wages often have several places of work with need to travel between
them, ruling out adjusting travel times as a response to road pricing.
The position of the cordon or boundary of the charging area and the way the charge is levied – cordon
crossing or time and distance-based charge – combine to determine the spatial distribution of the drivers
affected by the charging scheme and how much they pay. The number and location of at-risk drivers
affected can vary greatly with scheme design as the data from Leeds demonstrates. Under one of the
proposed cordon schemes examined, many affected at-risk drivers were located just outside the cordonboundaries. In this case park and ride systems might provide a feasible alternative for a significant
proportion of regular trips. Under other schemes, however, the spatial distribution of origin anddestinations of low income drivers affected was spread quite sparsely across the built-up area. Thissuggests that additional buses, or park and ride services to points just outside a cordon, would not offer
a feasible alternative to use of the car.
Whilst expert design is important, ultimately political judgement will determine the type of congestion
charging scheme adopted. It is therefore important that political decision makers and experts confer
early on, so that politicians understand the design as it unfolds and in particular to avoid prematurecommitments to rule out or rule in features that might significantly hamper effective congestion
management.

Fiscal impacts of road pricing and subsidies for public transport
Taxes that increase the cost of using cars, such as fuel taxes and congestion charges, may be regressive
to the extent that they affect non-discretionary travel, e.g. travel to work where there is no viable
alternative to the car. As with any consumption tax, they will account for a larger share of income or
expenditure in low-income households than high-income households. However, most studies have found
the regressive impact to be small (CBO, 1986; Poterba, 1991; Eliasson, 2016). McInnes (2017), in an
examination of the distributive impact of environmental taxes and charges in G20 countries for theOECD, concludes that taxes designed to internalise external costs are among the least regressive formsof taxation. The study finds that taxes on transport fuels and congestion charges are progressive in mostcountries, except in specific situations, notably the working poor in high-income countries with high car
ownership but limited public transport options. In Gothenburg, for example, public transport accounts
for a relatively low share of commuting and low-income individuals typically travel by car. Middle andhigher income earners are likely to pay lower fuel taxes over time as they switch to newer and more fuelefficient vehicles. In Gothenburg, where there is little congestion and the scheme mainly serves to raise
revenue for infrastructure investment, the charge applies all day even though there is no congestion
outside peaks. The system is regressive and creates a net welfare loss (Eliasson, 2016; West and
Börjesson, 2017). In mitigation, any alternative revenue raising tax would have been equally regressive
and the use of revenues for a large project with a low benefit cost ratio may, nevertheless, be alegitimate political decision.
How revenues from road pricing are used has a major impact on the overall distributive impact of roadpricing. Revenues could be used to offset more distorting taxes, i.e. taxes that provide a disincentive for© OECD/ITF 201823

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productive economic activity, or to offset regressive taxes elsewhere in the economy. More importantly,
there are powerful fiscal mechanisms in widespread use to address social equity. The general taxationand social security system can be employed to mitigate any regressive effects of congestion pricing. This
was a key finding of earlier work on road pricing reported to Transport Ministers (ECMT, 2003).
Small (1992) outlined three broad categories of revenue-allocation scheme to compensate those whoare most adversely affected: monetary reimbursement to travellers as a group, for example reduction inroad taxes; substitution for general taxes used to pay for transport services, for example a reduction in
local property taxes; new transport services, for example improvements in public transport (for areas
with under-provision) or highway improvements. How charging revenue is used can have a significant
impact on how effects of pricing are distributed between user groups. Net impacts, accounting for theuse of revenues, are the relevant outcome to model in assessing equity effect (Eliasson and Mattsson,
2006; Anable and Goodwin, 2018; D’Artagnan et al., 2018). Small (1992) argues that charging revenue isusually large enough to allow those who are most adversely affected to be more than fully compensated,
providing a surplus to promote general social goals.
Within the transport sector, road pricing revenue can be used to provide alternatives to car use fornon-discretionary travel – public transport and cycling infrastructure. In practice, existing public
transport users in London, Stockholm and Singapore have benefitted from urban congestion pricing
through additional bus and metro services and through improved speed and reliability of bus services.
For cities where public transport services are currently inadequate, a package of measures that expand
mass transit services and introduce peak pricing on these services at the same time as pricing road use
will maximise overall benefits.
Taxing mobility is probably less distorting than taxing business to fund public transport. The revenues
from London’s relatively small area Congestion Charge are used for investment in transport in London,including buses, road maintenance and the metro system. But surcharges on business rates are being
used to fund major public transport investments in London and Paris; Crossrail and the Metro de GrandeParis respectively. It would be more efficient to fund these investments through congestion charges
across the cities’ roads.
Paris has chosen to manage the scarce available space on its roads by restricting access through closing
expressways. In a more fiscally constrained environment, for example if central funding were notavailable to contribute to the costs of public transport, the city might have preferred congestion
charging. The availability of central government funding strongly affected the development of congestion
charging plans in English cities. Advanced plans for congestion pricing in the East and West Midlands andin Bristol and Cambridge were dropped, and Manchester’s congestion charging scheme was rejected in a2008 referendum, in the context of a relaxation of conditions for making central funds available fortransit investments. Freedom for local authorities to raise funds from congestion charges was granted by
the Transport Act 2000, under the condition that any revenues were recycled in local transport projects.
The Ministry of Transport then introduced a Transport Investment Fund in 2005 that provided for a totalof GBP 9.5 billion of central funds for public transport, cycling and pedestrian projects for local
authorities who installed congestion charging or workplace parking schemes (Butcher 2010; 2018). In the
run up to the 2010 election it was announced that funds would be provided without the requirement tointroduce congestion charging. The charging plans were politically contentious and removing the link tofunding probably tipped the balance. Some of the cities subsequently started to work again on charging
systems but the charging powers have only been used outside London for a small scheme to restrict
access to the historic centre of Durham and to emissions-charging schemes in Brighton and Nottingham.

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One thing congestion charging achieved in Stockholm was acceptance for a major expansion of public
transport and how it should be expanded, something that had never been achieved before. The use ofrevenues for metro investment was beneficial even if benefit-cost ratios (BCRs) for those projects were
low in comparison to road projects; it should be remembered that BCRs should be used to rank similar
projects and are of limited use in comparing projects with very different characteristics.
Experience in Stockholm and elsewhere suggests that lasting improvements in public transport capacity
have to be supported by both users and a broad range of taxpayers. In practice, all households will needto contribute to the subsidies necessary for investment and operation of public transport, and roadpricing is likely to contribute only a limited part. At the same time the subsidies needed for public
transport can be reduced and the incentives for efficiency provided by peak pricing preserved bytargeting support to the users that need it most. Targeted subsidies have been used successfully in
Bogota to protect the mobility of the poor through credits to electronic travel cards (ITF, 2017b).
The objective of congestion charging is to manage traffic rather than raise revenues, not least because
there are cheaper ways to do that. Although the cost of the technology for pricing has fallen since thelast review of charging options by the ITF (2010), fuel taxes are still cheaper to administer. From atechnical point of view, congestion charging can certainly be part of a network-wide electronic roadpricing system with revenue raising objectives, to pay for roads or to contribute to public finances more
broadly. However, the peak prices necessary to manage congestion must not be tempered by any
mechanism designed to set a limit on the overall revenues raised by generalised road pricing. If they are
subject to a cap below the level required to manage demand they will be ineffective and discredited.

Assessing the distributional impacts of road pricing
Establishing an assessment framework
Although the overall distributional impacts of road pricing may be small, pricing schemes can clearly havea large impact on some vulnerable households and individuals. Mitigating these effects or compensating
vulnerable groups requires review of design options, quantifying impacts on different household types.
One first needs to clarify the equity concepts at stake and explore the available options in a systematicway. This is the role of an assessment framework. New Zealand’s Ministry of Transport is currently
developing such a framework and will test it on case studies using existing transport modelling tools
supplemented with additional census and household travel survey data. From an economic perspective,
there is no single definition of equity. The literature on road pricing has historically focused on
distributional impacts among income groups, as the question of whether road pricing is regressive, i.e.
whether lower income groups pay more has been at the core of the debate. Yet equity is a broader
concept and many other dimensions can be considered. For example, is the scheme offering consistenttreatment for individuals in the same income group (horizontal equity)? What is the spatial distributionof winners and losers (territorial equity)? Some authors also argue for some form of temporal equity
depending on how benefits and losses are distributed to the present or the future. In short there areseveral household segmentations to be considered.

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Bills and Walker (2017) discussed an equity analysis framework (Figure 7) that makes use of
disaggregated micro data (such as population, land use and travel behavioural data obtained from
activity-based travel demand models) to overcome biases from aggregation. Their framework includes
two crucial components - identifying an appropriate segmentation of the population and identifying theequity indicators to evaluate. The common equity indicators used in practice include accessibility, travel
time, travel distance, mode share, distance travelled in congested conditions and traffic displacement.
Segmentation of households may involve the use of one or more variables of segmentation (such as
income and characteristics of the individuals or households) and a unit of segmentation (such asindividual or household or geographic zone). Bills and Walker (2017) recommended selecting thevariables of segmentation based on the chosen equity dimension (e.g. horizontal versus vertical equity).
For the unit of segmentation, they recommended using smaller units to reduce aggregation bias.
An assessment framework is important for screening options at an early stage of policy development andweeding out unpromising options, to allow analytical resources to focus on the main alternatives.
Figure 8 summarises the dimensions an assessment framework needs to consider. Three of the mostimportant aspects are: road pricing options and scheme designs, mitigation measures and use ofrevenue from congestion charging (both discussed in the previous section) and analytical dimensions to
identifying vulnerability.
Figure 7. Distributional impacts assessment framework

Source: Based on Bills and Walker (2017).

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Figure 8. Aspects to consider in an assessment framework

Source: Based on MRCagney (2017).

Analytical dimensions of vulnerability
Mattioli et al. (2017) define three dimensions of vulnerability, which can also be used to categorise the
distributional impacts of road pricing (Table 3). The three vulnerability dimensions are exposure,sensitivity and adaptive capacity. Mattioli et al. define exposure as the cost burden of travel. This
includes the level of vehicle ownership per household and household location. Sensitivity refers to ability
to pay covering factors such as income, employment status and job type. Adaptive capacity refers toaccessibility to alternative means of transport. Factors that influence the adaptive capacity include
household type, demographic characteristics, physical constraints (e.g. physical disabilities) andavailability of alternate modes of transport or routes for travel. Employment status and job type cansometimes play a role. There are also interactions between these factors.
Four basic types of at-risk groups can be identified form the nine factors summarised in Table 3:
1. low-income earners who have inflexible employment arrangements that constrain their freedom
to choose departure and arrival times
2. households with young children who might need to meet both the children’s educationcommitments and the adult’s employment commitments at specific locations and times
3. people with physical constraints such as young children or elderly dependents and people withdisabilities that constrain their ability to switch modes
4. households located in areas with no or limited access to public transport suitable to substitutefor charged trips, which constrains their ability to switch modes.
As a result, there are five analytical dimensions to identifying which at-risk groups would be mostadversely affected. The first four dimensions align with the four basic types of at-risk group, namely
income, household type, user type and location. Time of day is the fifth component because trip
generation rates vary with time.

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Table 3. Vulnerability dimensions and at-risk groups
Vulnerability dimensions
Factors

Exposure(cost burden of
travel)Vehicle ownership●Household location●

Income(ability to pay)

Adaptive capacity

At-risk groups(accessibility withalternatives)
Areas with no or limitedaccess to public transport

Income●Employment status●o

Job type●oHousehold type●

Demographic
characteristics●

Physical ability (e.g.
people with disabilities)●

Availability of alternate
modes or routes●

Low-income earners withinflexible employment
arrangement
Households with young
children

Young children, elderly andpeople with disabilities

Areas with no or limitedaccess to public transport tosubstitute charged trips

Source: Based on discussion in Mattioli et al. (2017).

Models for distributional analysis
Even simple pricing schemes can have unexpected impacts on behaviour, traffic flows and transport
costs. Therefore most road pricing scheme have been designed with the help of large-scale modellingstudies.
The standard approach to assessing how road pricing impacts transport demand relies on classicalfour-stage modelling: trip generation, trip distribution, mode choice and route assignment. Although thisapproach is an efficient way to forecast the impact on medium-term transport demand, it will beinsufficient when it comes to understanding distributional impacts.
The first reason is that it does not allow for disaggregated analysis, which is essential for estimating
distributional impacts. The second is the wide range of potential responses to pricing that should beconsidered. When road pricing is implemented, users might react in many ways as summarised in
Figure 9. Depending on the scheme design and objectives, some reactions might more important tomodel than others, possibly making the use of conventional transport modelling irrelevant.
This section is divided in two parts. The first subsection discusses the data required as inputs tomodelling, and more specifically argues that synthetic population data are required. The second focuseson modelling requirements.

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Using micro data for disaggregated analyses

Transport practitioners have traditionally relied on mobility surveys to help design transport policies. But
when it comes to identifying which households would be most vulnerable to road pricing, they are
insufficient. As presented in the analytical framework section, the vulnerability of a household depends
on at least five, cumulative dimensions. A low-income household will be much more impacted if it has noaccess to public transport and if one of the family members has physical disabilities. Even with arelatively high sample size, mobility surveys only allow estimation of one or two dimensions for a given
location, with a rather crude spatial definition. While mobility surveys provide sufficient information to
help estimate the travel behaviour within a neighbourhood or the proportion of low-income households
in that neighbourhood, what it is really needed is the combination of the two – how low-income
households travel – at a finer geographical scale – typically the census block.
Figure 9. Behavioural responses addressed by conventional models

Source: Adapted from MRCagney (2017).

The methodological answer is to synthesise a population. A synthetic population provides a microscopic
representation of the actual population. It is built by combining aggregated and disaggregated data frommultiple sources (e.g. different surveys, car ownership and use information or income data). Eachindividual is assigned to a precise residential location and the attributes of the synthetic population
contain detailed information such as age, gender, work or study, car ownership as well as the complete
travel pattern. The synthetic population is a simplified representation of the actual population, with thestatistical characteristics of the true population. Synthetic populations have been used in the assessmentof transport policies for some time and with the advent of large scale microsimulation, synthetic
population generation has produced a vast scientific literature (see Farooq, Bierlaire, Hurtubia, andFlötteröd, 2013). A recent trend is the use of mobile phone data along with traditional survey data.
Microsimulation has become a key tool for distributional assessment (see Box 3 for an example).
Building a synthetic population can provide initial insights into the distributional impacts of a charge even
without explicitly modelling the impacts. This would typically be done by neglecting behavioural
responses (mainly modal shift, rerouting and rescheduling) as in Bonsall and Kelly (2005), a practice thatis sometimes referred to as static microsimulation.

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Box 3. Synthesising a population for transport policy study
The International Transport Forum has recently assessed potential centralised door-to-door sharedmobility services in Auckland (ITF, 2017a) using a microsimulation model. The synthetic population was
generated from respondent-level data from Auckland’s Household Travel Survey and uses an algorithm
in three steps. First, it assigns a virtual household to each residential location by duplicating thecharacteristics of a sample respondent. It then generates for each virtual individual a trip chain that is
plausible in the light of transport options that the individual has access to. Finally it introduces relatively
small variations in trip destinations and schedules. The output is a full synthetic population withinformation on households (residential location, family profile), on individuals (age, education levels) andcomplete mobility behaviour (complete trip chains, including the origin-destination of each trip, timing,
mode and purpose). In addition each individual is embedded with a utility function that summarises its
transport preferences.
This last point is what makes ITF’s approach to population generation well suited for policy assessment,
especially where trip decisions are likely to be significantly affected by the policy. When road pricing is
implemented, car users might shift to other modes, stop driving or reschedule their trips. Correctly
valuing the impacts of these behavioural reactions is essential, but not necessarily straightforward. Froman economic perspective the correct way of proceeding is to compute the surplus variation for eachuser. When trip decisions are modelled using rule-based approaches, which is the case of the majority of
micro-simulators, this can be done only through numerical approximations. On the contrary, when trip
decisions are modelled consistently with utility theory, as it is the case of ITF model, the consumer
surplus can be derived directly, using the so-called “logsum” formula.
Figure 10. Estimation of the consumer surplus variation

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Beyond the four-step model

There are three essential modelling requirements to support effective assessment of the social impacts
of a transport policy. First, the forecasting tool should be able to capture how different individuals react
to pricing. At a minimum, the heterogeneity in users’ value of time should be explicitly modelled as it iscentral to the question of whether the pricing scheme is regressive. As pricing usually reduces travel
times and increases monetary costs, the gains are larger for individuals with higher values of time. The
net impact (of paying the charges) not only differs by trip purpose and traffic mix, it also differs bysocio-economic group. In addition to heterogeneity in value of time, the difference in the preferences
across socio-economic groups regarding modal choice and trip scheduling should also be taken inaccount.
A second requirement is the ability to model trip scheduling behaviour (considering heterogeneous
values of schedule delay). This requirement is important when assessing time-differentiated pricing
schemes. Such schemes usually induce peak spreading which in turn can result in non-trivial distributive
impacts. Modelling this correctly requires dynamic traffic assignment (DTA) models which are not yet
widely used by practitioners. However, DTA has already been successfully applied in one congestion
charge study (De Palma and Lindsey, 2006) and results show that the costs and benefits of a time-varying
tolling scheme are severely underestimated when rescheduling effects are ignored.
The third requirement is the ability to quantify trip reduction. An urban toll will reduce road traffic whenusers consolidate their trips (referred to as trip chaining), switch to a closer destination or simply cancel
their trips. Understanding the underlying mechanisms and the structure of this mobility reduction isessential. One way to achieve this is to move toward an activity-based approach. Activity-based models
derive travel demand from people's daily activity patterns. They predict which activities are conducted
when, where, for how long, for and with whom, and the travel choices they make to complete theactivities. Unfortunately, activity-based models have rarely been applied in practice for planning
decisions, except in a couple of large US metropolitan areas.
There are two broad strategies to fulfil the three requirements:


Calibrate a dedicated multi-agent microsimulation model. This allows modellers to specify any
form of complex decision mechanism regarding mode-change, trip rescheduling, trip-chaining
and destination substitution. The drawback is that microsimulations are often difficult tocalibrate, they also lack theoretical properties (as typically behaviours are inferred from expert
judgement rather than derived using observed preferences) and there might be data
convergence issues.

Extend an existing four-step model to incorporate more realistic – but more complex –behavioural features. This is an appealing solution as it has the benefit of using the existing tools
and offers a smooth transition for modellers that can progressively develop their capabilities.
However, the body of knowledge required for dynamic traffic assignment and activity-basedmodelling is still an ongoing field of research. Although the theoretical concepts are now settled,
they are usually poorly reflected in commercial software.

In summary, there is no one size fits all solution regarding the optimal modelling approach. The skills of
the modelling team, the level of development of an existing model and the precise objectives of thepricing scheme are local factors that should guide the decision on a case-by-case basis.

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Conclusions on the design of road pricing tomanage congestion
Perceived distributive impacts are a significant factor in public attitudes to road pricing. Cordon charges
are often unpopular because they are seen as discriminating against commuters living outside the city
centre and against shops located just inside the cordon. This was the case, for example, in the 2005
referendum that rejected a proposed cordon charge in Edinburgh. In Manchester, concern in thebusiness community about the possible impact on the supply of labour and in turn competitiveness wasa factor in dropping plans for a congestion charge. Consultation on distributional impacts andassessment of scheme design is therefore essential.
Discussions at the roundtable concluded that the overall distributive impacts of road pricing are small
but that some vulnerable populations can be adversely affected, particularly by cordon charges. Cordons
can be designed to minimise the numbers of vulnerable households adversely affected but
differentiating charges to incentivise peak spreading is effective in minimising distributional impacts withall types of charging scheme.
Area-wide electronic vehicle-kilometre charges, differentiated in time and by location, are the mostefficient form of congestion charge and have the lowest distributional impacts. Differentiation acrosspeak times provides opportunities modifying departure times rather than being priced off the system
and allows for lower average charges than would otherwise apply. Geography and nature of local travel
patterns may mean a simple cordon can achieve most of the benefits of a more sophisticated charging
system but in Auckland’s case, where much of the congestion builds up at points of access to themotorway network differentiated distance based charges are likely to be the most efficient option.
Experience from Stockholm and London suggests that the public generally accepts road pricing once thesystem is introduced. Public opinion is certainly more favourable after implementation than before roadpricing is introduced. This is partly because it is very difficult for people to judge the impact of something
they have never experienced. The lesson from Sweden is that surveying public opinion before scheme
introduction is not a reliable guide to acceptability. Many of the negative distributional impacts expectedin Stockholm did not eventuate. For example, one-third of the discretionary trips disappeared without
being noticed by survey respondents. This indicates the potential for trip consolidation and cancellationof non-essential trips whilst meeting basic travel needs.
A less direct factor in the acceptance of congestion pricing in Sweden has been linking grants from
central government for large road investment schemes to the introduction of road pricing. The
Stockholm scheme was part of a package for improving transport in the region that included investments
in a ring road. This was a significant factor as under Swedish law, raising taxes (including those forinvestment in transport infrastructure) is a national (not a regional) prerogative. The Stockholm
congestion charge is therefore legally classified as a national tax; local charges for using roads are notpermitted to avoid the risk of local authorities charging for transit of their communities. As noted, tax
competition between neighbouring cities was feared in Manchester, where the decisive factor inabandoning plans for road pricing was delinking it from funding for investment in the city’s transportsystem. In Gothenburg, where congestion is rather limited in scale and distribution, the main interest for
the local population was the accompanying package of measures including investment in a road tunnel,
the funding for which is only partly compensated for by charging revenues. Congestion charging in

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Stockholm also revolutionised attitudes to investment in public transport, opening the way to a major
expansion of services previously unthinkable.
In Singapore, road pricing enjoys general support from the population. The key factor here is thatcharges are calculated to maintain target speeds, an approach that is understood and accepted. This has
both made the policy apolitical and provided reassurance that tolls are not adjusted to increase
revenues.
Emergency vehicles and cars used by people with disabled parking permits are universally exempt fromcongestion charging schemes. Buses are also generally exempt, except in Singapore (D’Artagnan et al.,2018). Beyond these limited categories, exemptions are not an effective strategy for mitigating social
impacts. Leakage is a major problem, i.e. exemptions being used for non-targeted trips such as vehicles
exempt because they carry a disabled users’ badge being driven for other purposes. Bonsall and Kelly(2005) expected over 90% leakage in the congestion charging systems they examined. London granted asignificant proportion of user discounts and exemptions. Residents from within the charging area areoffered 90% discount. Taxis and minicab drivers are exempt but rapid growth in this sector with theadvent of app-based ride services has driven a sharp increase in vehicle traffic in London. Today,
discounted or exempted trips constitute around 50% of traffic crossing the charging zone (D’Artagnan etal., 2018). Exemptions for residents inside charging areas can be highly counterproductive. Exempting
residents or providing large discounts defeats the object of managing demand for road space and ofcourse foregoes a large part of potential revenue. The consequence is that charges must be much higher
to have the same impact on overall levels of traffic.
To conclude, the following points drawn from the roundtable discussion are particularly relevant for
Auckland in its deliberations on road pricing.








Time- and space-based road pricing allocates road space according to the value attached to thetrip. The resulting network performance improvements can deliver significant economic benefits
with limited distributive effects. As the impact on vulnerable households tends to be scatteredgeographically, negative distributional impacts are better mitigated with targeted support for
those affected than generalised discounts and exemptions.
To understand the distributional impacts of road pricing and target mitigation measures, it isnecessary to understand patterns of non-discretionary trips – who makes them, between what
origins and destinations, and for what purposes. Using administrative and other micro data, it isoften possible to identify vulnerable households based on microsimulation without sophisticatedmodelling. Microsimulation is also useful for setting cordons or boundaries of the charging areas
to minimise rat-running and unintended distributive effects.
Unlike many other major cities, Auckland has a low public transport share for commuting trips.
The timing of investment in public transport will therefore be important to ensuring new services
are available to substitute for car use on introduction of road pricing. For cities with already highshares of public transport use and low prices, introducing peak pricing on public transport at thesame time as introducing road pricing brings the greatest welfare benefits. For Auckland, further
investment in public transport services even without peak pricing can be expected to bring
benefits due to current relative under-provision.
Road pricing should be viewed as a crucial part of a package to achieve better utilisation of spacein the city. Coupled with investment in public transport it will support transit-oriented urban
development and may contain sprawl. Better utilisation of road space will ultimately make city
living more attractive, reducing commuting time and air pollutant emissions, and should drive
productivity by improving access to jobs across the region.

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List of participants
International experts
Ms Jillian ANABLE

University of Leeds, UK

Mr Arie BLEIJENBERG

Netherlands Organisation for Applied Scientific Research, Netherlands

Ms Maria BÖRJESSON

Swedish National Road and Transport Research Institute, Stockholm, Sweden

Mr Kian Keong CHIN

Land Transport Authority, Singapore

Mr Yves CROZETUniversity of Lyon, France

Mr Mark DARROUGH

Australian Department of Infrastructure & Regional Development, Australia

Mr Mans LÖNNROTH

Former State Secretary for Environment, Sweden

Ms Aurélie MERCIER

University of Lyon, France

Mr Stef PROOST

University of Leuven, Belgium

Mr Olivier QUOY

CEO, Atalandes, France

Mr Matthew SCHRODER

Australian Competition and Consumer Commission, Australia

Mr Vincent van den BERG

Free university of Amsterdam,Netherlands

Mr Ian WALLIS

Ian Wallis Associates Ltd, Wellington, New Zealand

Mr Scott WILSON

D’artagnan Pacific,United Kingdom and Australasia

New Zealand Ministry of Transport
Ms Kirstie HEWLETT

Deputy Chief Executive, Regulatory and Data Branch

Mr Tim HERBETManager, Domain Strategy, Economics and Evaluation

Mr Dan JENKINSManager, Analytics and Modelling

Ms Joanne LEUNG

Principal Economist, Domain Strategy, Economics and Evaluation

Mr Sandy FONG

Principal Strategy Adviser, Domain Strategy, Economics and Evaluation

Mr Ralph SAMUELSON

Principal Scientist, Analytics and Modelling

Ms Emily FULLHAM

Senior Adviser, Demand Management

Mr Anaru SILAO

Senior Adviser, Demand Management

New Zealand Treasury
Mr Dieter KATZ

Principal adviser, National Infrastructure Unit

Mr Jon BULTER

Senior adviser, National Infrastructure Unit

© OECD/ITF 201837

THE SOCIAL IMPACTS OF ROAD PRICING | SUMMARY AND CONCLUSIONS | ITF ROUNDTABLE 170

New Zealand Transport Agency
Ms Leigh MITCHELL

Director, Customer Experience and Behaviour

Mr Kevin WRIGHT

Practice Manager, Investment Assurance

Ms Jannette FARLEY

Principal advisor, Regulatory and Transport Policy

Ms Sarah CRONWRIGHT

Principal specialist, Statutory Planning & Community

Ms Kirsten TAYLOR

Communications

Auckland Transport, New Zealand
Mr Chris MORGAN

Group Manager, Analytics and Research

Ms Christine PERRINSGeneral Manager, Strategic Transport Planning

Mr John DAVIESManager, Auckland Forecasting Centre

Mr Jojo VALERO

Team leader Transport Modelling,Auckland Forecasting Centre

Mr Nik VORSTERTransport Economist

Auckland Council, New Zealand
Mr David HAWKEYManager, Transport and Infrastructure Strategy

Mr David NORMAN

Chief Economist

Mr Alastair CRIBBENS

Principal Transport Advisor, Transport & Infrastructure Strategy

Mr Jesse ALLPRESS

Senior social researcher, Research and Evaluation Unit

Additional attendees
Mr Saeid ADLI

Auckland, New Zealand

Mr Michael BUSHBYWSP Australia Pty Ltd, Australia

Ms Helen CHAPMAN

Greater Wellington Regional Council, Wellington, New Zealand

Mr Suh-wen CHIOU

National Dong Hwa University, Taiwan

Mr Stuart DONOVANMR Cagney, Auckland, New Zealand

Mr Simon DOUGLASNZ Automobile Association, Wellington, New Zealand

Ms Josephine DRAPER

Abley Transportation Consultants, New Zealand

Mr Pascal FELIXWSP Australia Pty Ltd, Australia

Mr Bruce JAMES

Griffith University, Australia

Mr S.M. Sohel MAHMUD The University Of Queensland, Australia
Mr Brendan O’KEEFFE

Brisbane City Council, Australia

Ms Janet STEPHENSON

University of Otago, New Zealand

Mr Doug WILSON

University of Auckland, New Zealand

Mr Jian XING

Nippon Expressway Research Institute Co Ltd., Japan38© OECD/ITF 2018

THE SOCIAL IMPACTS OF ROAD PRICING | SUMMARY AND CONCLUSIONS | ITF ROUNDTABLE 170

International Transport Forum, OECD
Mr Stephen PERKINSHead of Research and Policy Analysis

Mr Jari KAUPPILA

Head of Statistics and Modelling

Mr Luis MARTINEZ

Policy Analyst

Mr Nicolas WAGNER

Policy Analyst© OECD/ITF 2018

39

The Social Impacts of Road Pricing

International Transport Forum
2 rue André Pascal
F-75775 Paris Cedex 16+33 (0)1 45 24 97 10
contact@itf-oecd.org
www.itf-oecd.org

2018-10/ Photo credit: Nattapoom V

This report assesses how road pricing impacts are distributed amongst
citizens. It specifically examines how the reallocation of road space
can improve the wellbeing of the community at large, looks at therelationship between road tolling and public transport pricing, andexplores how simulation models can help develop measures to minimise
negative impacts of road pricing. It also reviews current road pricing
schemes in Sweden and Singapore. The report summarises the findingsof an ITF Roundtable held in Auckland, New Zealand, in December 2017that brought together 18 experts from eight countries.