End of Award Report submitted to the ESRCESRC Award Reference Number R000223117

Road Pricing andUrban Congestion Costs

Georgina SantosDavid Newbery

Department of Applied EconomicsUniversity of CambridgeCambridgeMarch 2002

Full Report of Research Activities and Results

Background

Our research project ‘Road Pricing and Urban Congestion Costs’ couldhave not been better timed. The Labour Government decided to give

transport priority in the political agenda and throughout 1997-2000produced a series of documents, reports and consultation papers that lead to

the Transport Bill in 1999 passed as an Act in 2000.In 1997 the UK Government launched an invitation to contribute with

ideas on Developing an Integrated Transport Policy (DETR, August 1997).The Government then expressed that their aim was to achieve better and

more integrated public transport systems.The following year, there were two documents that prepared the way

for the Transport Act. The first one, the White Paper (DETR, 1998a), wasa government statement of what they were aiming to achieve: integrated

transport, local transport plans and traffic management policies. In thedocument they clearly announced that they would introduce legislation to

allow local authorities charge road users and to levy a new parking chargeon workplace parking.

Later that year, a second document came out, Breaking the Logjam

(DETR, 1998b). This was a consultation paper on fighting traffic

congestion and pollution through road user and workplace parking charges.The ideas that later were passed in the Transport Act were already there.

The Transport Act (Acts of Parliament, 2000) was finally passed inDecember 2000. With the Transport Act traffic authorities, including the

Mayor in London, were given legal powers to singly or jointly, introducea mandatory charge for using a vehicle in or entering a designated area,

or for using specified roads - for the purpose of reducing congestion ortraffic growth, or meeting other objectives contained in a local transport

plan (for local authorities) or otherwise set out by the Secretary of State(for schemes on motorways and trunk roads).

With four government documents in five years and constantpressure of the media on politicians, civil servants and researchers, the

environment to carry out our research was ideal.

Objectives

The objectives of the project were to:

1) Measure congestion costs in the most congested areas in a range ofdifferent towns.

2) Attempt to identify generalised relationships determining congestioncosts in urban areas using econometric techniques.

3) Undertake a cost-benefit analysis of different types of road schemes.All three objectives were addressed and met. The first objective of

measuring congestion costs was achieved by using the traffic simulationand assignment program SATURN (Van Vliet and Hall, 1997) and

Matlab programs (The Maths Works, 1994) together with batch fileprocedures and the development of a method to compute average social

congestion costs.The second objective produced mixed results. Although speed-flow

relationships were found to have a linear segment over the relevant level oftraffic, the coefficients of the relationship need to be estimated for each

town. Observations on the existing average speed combined with a smallnumber of simulated estimates for lower levels of traffic allow these

coefficients to be determined, and from them the marginal congestion costs,and deadweight losses. The coefficients vary considerably from town to

town, in contrast to the link speed-flow coefficients. We therefore foundthat there are no general area speed-flow relationships that can be applied

algebraically and without simulation across towns.The third objective was met using standard cost-benefit analysis

techniques.

Methods

As concluded in our previous ESRC research project ‘Quantifying the

Costs of Congestion’, urban congestion seems to be caused by theinteraction of flows at junctions rather than by the number of cars going

along a particular link. Junctions are specially modelled in a suite ofprograms for Simulation and Assignment of Traffic in Urban Road

Networks (SATURN) and this is the reason why this software was chosen.Although the software was developed mainly to evaluate road

schemes, the outputs were used to study the social costs of congestion in arange of towns, varying in size, traffic, road outlay, and age. The towns are

Northampton, Kingston upon Hull, Cambridge, Lincoln, Norwich, York,Bedford and Hereford.

Our main methodological contribution was to derive area speed-flowrelationships for a range of towns by considering equiproportional

variations in all origin-destination flows. We then computed the averagemarginal congestion cost in different towns and areas within towns, the

potential average corrective charge, and the deadweight loss as anindication of the maximum likely benefit from road pricing (subject to

more careful tests with the simulated road pricing later).The standard way of computing the inefficiencies of excessive traffic

(Morrison, 1986; Newbery, 1990; Lewis, 1994; Button and Verhoef, 1998)starts by comparing the efficient level (Qe) with the actual level (Qa). The

social costs of congestion or deadweight loss (DWL) of inefficient pricingof scarce road resources can in practice be measured by the area between

the marginal social cost (MSC) and the inverse demand (P(Q)) curvesbetween the efficient and actual levels of traffic. Graphically, it is the

triangle BHC on Figure 1.

Figure 1: Average and marginal costs

The efficient charge in this case would be BD, since that would facedrivers with their true costs. This charge can be proved to be an ‘average

charge’ per km (Newbery, 2000). This charge would be the average of allthe charges that would have to be levied on each trip to reduce all trips

proportionately. These road charges would have to be levied solely as afunction of origin and destination to avoid influencing the choice of route

(other than in response to the changed level of traffic). Such road chargesare doubly inefficient relative to the first best, in that they do not discourage

traffic from the most congested parts of town, nor do they penalise tripswith higher external costs more heavily than those with low external costs

(Newbery, 2000). They do, however, allow one to place meaning on thevarious costs, externalities and corrective charges on Figure 1.

The inverse demand functions assumed were linear, constantelasticity, and elastic exponential. Six different point elasticity values at

the actual level of traffic were assumed: 0.2, 0.3, 0.4, 0.5, 0.6 and 0.7.These seem to cover the whole range of possible point elasticity values

that have been found in previous studies (Goodwin, 1992; Oum et al,

1992; Fowkes et al, 1993; DETR, 1998c; DETR, 2000; Victoria

Transport Policy Institute, 2000; De Jong, G. and H. Gunn, 2001).Reactions such as change in car ownership or relocation are not

considered.

Traffic load on the network (pcu/h)

Ave

rage

and

Mar

gina

l Cos

ts (

p/P

CU

km)

MSC

APC

Demand = MPB = MSB

A

BE

D

C

H

Qe Qa

F

In order to estimate the deadweight loss (area BHC on Figure 1) a

second degree polynomial to approximate the MSC that would allownumerical integration was used because the approximation would be

better than a straight line with the area BHC assumed to be a triangle.

Problems with this methodIt is common to model a town dividing it in several sub-areas, for

instance a central business or historical district where the highestcongestion takes place, surrounded by rings of diminishing congestion.

However, the method described above presents a problem when appliedto the sub-areas within the network. For instance, an interesting sub-area

to study is the whole town up to but excluding the surrounding ruralroads. Most towns are surrounded by motorways and trunk roads and

typically traffic travels faster on these roads than on urban roads and as aconsequence average speeds are higher and costs, lower. Including these

roads may push averages down and excluding them could act as acorrection in order to see the real picture of what costs are in an urban

area. A similar analysis applied to city centres where average speeds arelower than average speeds in the whole town. The problem that arises in

these cases is that the inverse demand curve is artificial and not anaccurate representation of the actual demand, which is for trips, not for

bits of trips, i.e. the part of the trip that takes place inside the area understudy.

Reliable estimates of average private and marginal social cost inthese sub-areas can still be obtained. They do not depend on any demand

assumption for segments of trips. Marginal congestion cost estimates,computed as segment CH on Figure 1, are also reliable as they are not

influenced by the inverse demand assumed. Although benefits are a trip-related concept, costs may be disaggregated. On the other hand, potential

charges computed as segment BD on Figure 1, do depend on the demandassumed and therefore may not be a good approximation of efficient

charges. Table 1 in next section summarises the results.

A different approach to estimate charges

The approach presented in Figure 1 does not model change of route. If acharge is introduced demand goes down but no change of route to avoid

payment is allowed for. We decided to try a different method and found thatwhen change of route is modelled an efficient equilibrium can be achieved

with a lower charge. Cordon toll schemes rather than trip based chargeswere considered for eight English towns and optimal tolls estimated

showing that in all cases, the maximum social surplus can be obtained withlower tolls and higher levels of traffic as long as traffic is assigned more

efficiently, i.e., there is a shift of traffic from congested to uncongestedareas. A cordon toll scheme also seems to be reasonably robust and error-

tolerant in the demand elasticity estimates. Errors in the elasticities assumedand consequent errors in the efficient toll estimates do not lead to great

losses in the social surplus.The model used to simulate this was SATTAX (Milne and Van

Vliet, 1993), a batch file procedure that can be added to SATURN. Themodel has two responses: route choice within the current static time

period and transfer off the road (due to change of departure time, changeof mode, car-pooling, cancellation of the trip, etc). The demand function

to carry out these simulations was elastic exponential and threeelasticities were considered: 0.2, 0.4 and 0.7, again covering the possible

range. Results for the lowest and highest elasticities are reported below.This is the type of scheme for which we performed a cost-benefit

analysis. In this report we present results for the cheapest technology.Another paper produced under this grant compares two technologies, and

concludes that borderline cases are viable with the cheapest one.

Results

Congestion costsThree sets of simulations were run: one including the motorways and trunk

roads that surround each town in those cases for which there are such roads,one covering the whole town up to but excluding the motorways and trunk

roads that surround them, and one for the central (most congested) area or

city centre.1

As expected, average and marginal costs expressed in pence per

PCUkm (passenger car units multiplied by kilometres demanded) arehighest in central areas and lowest when motorways and trunk roads are

included in the calculations. Table 1 shows number of trips and AKTtogether with the averages of the average private and marginal social costs

for the different areas.Deadweight loss values were also estimated. As explained above the

procedure to estimate deadweight loss is correct when applied to wholetowns but it is not correct or accurate enough when applied to sub-areas. It

is interesting however that all numbers seem robust to both differentdemands and elasticities, with low standard deviations in all cases. The

average of the estimates are presented in Table 2 below.Deadweight loss in pence per PCUkm was computed as the area of

triangle BHC divided by the average of the efficient and actual averagekilometres travelled (AKT) on the network during the time period

simulated, one hour. Annual deadweight loss in £ million was obtained asthe area of triangle BHC multiplied by 250 working days.2 MCC in pence

per km are also given for all three areas. Marginal congestion costs arehigher in city centres, where the vehicle kilometres travelled are only a

small percentage of the total kilometres travelled on the network. Shares ofvehicle kilometres travelled in central areas with respect to totals are given

for each town.

1 This exercise involves a considerable amount of work and time, mainly because ofthe data preparation and manipulation. A new network file and trip matrix is producedfor each area using the original data sets as a base. In order to obtain the final results,the network is checked so that there are no inconsistencies such as missing turns, etc,the simulation and assignment procedure is run, and then the average time andaverage distance matrixes are dumped into a spreadsheet. This is done thirty times foreach area so that thirty different levels of traffic (uniformly scaled up and down) areconsidered. The rest of the calculations are done with Matlab (The MathWorks,1994).2 There are 250 working days per year in the UK, excluding weekends and bankholidays.

Table 1: Number of trips, average kilometres travelled, average private costs and marginal social costs in £ million at 1998 prices

Town Number of trips (PCUs per hour) AKT (PCUkm per PCU) APC (pence per PCUkm) at theoriginal level of traffic

MSC (pence per PCUkm) at theoriginal level of traffic

Area under study Area under study Area under study Area under study

Includes Excludes Central Includes Excludes Central Includes Excludes Central Includes Excludes Centralmotorways motorways area motorways motorways area Motorways motorways area motorways motorways area

Northampton 51,581 46,693 15,049 5.3 3.1 1.1 99 132 130 414 555 690 Kingston upon Hull 47,080 na 15,834 7.7 na 2.0 78 na 110 244 na 596 Cambridge 42,471 34,938 16,581 6.4 3.4 1.9 49 77 97 120 222 334 Lincoln 27,747 27,240 13,737 5.2 3.9 1.8 57 62 102 123 133 327 Norwich 40,431 25,946 14,831 9.4 2.5 0.7 50 77 99 64 146 302 York 37,910 33,847 11,690 6.6 3.0 1.3 45 62 111 97 125 365 Bedford 27,316 24,644 14,717 19.7 3.3 1.9 36 60 68 46 123 178 Hereford na 15,383 8,069 na 4.3 1.4 na 57 99 na 114 436

Source: Own calculations, see text. The data used for all simulations was provided by the different local authorities and consultancies working for them.Note: There is a problem with the results for Northampton, which has lower APC in the central area than in the whole town excluding motorways. The reason for this liesin sampling and estimation errors, which are considerable in the exercise carried out.

Table 2: MCC, average DWL in pence per PCUkm, ratio of DWL to MCC, and average annual DWL in £ million at 1998 prices

MCC (pence per PCUkm) Average DWL(pence per PCUkm)

Average DWL over MCC(%)

Average Annual DWL(£ million)

Area under study Area under study Area under study Area under study

Town

Includesmotorways

Excludesmotorways

Centralarea

Includesmotorways

Excludesmotorways

Centralarea

Includesmotorways

Excludesmotorways

Centralarea

Includesmotorways

Excludesmotorways

Centralarea

VKT incentralarea

as % oftotal VKT

Northampton 315 423 561 29 40 47 9 9 8 14.5 10.5 1.5 6.1 Kingston upon Hull 166 na 485 14 na 41 8 na 8 8.9 na 2.6 8.7 Cambridge 71 146 237 6 13 19 8 9 8 2.5 2.5 1.1 11.6 Lincoln 67 71 225 5 5 19 8 7 9 1.2 0.9 0.8 2.7 Norwich 14 69 203 1 4 14 4 6 7 0.3 0.4 0.3 17.1 York 44 63 254 3 4 19 7 7 7 1.2 0.7 0.5 6.1 Bedford 11 63 111 0 4 7 4 7 6 0.3 0.6 0.4 5.2 Hereford na 57 337 na 4 25 na 7 8 na 0.4 0.6 17.1

Source: Own calculations, see text. Note: DWLs are averages over the three demand functions and elasticities assumed. Standard deviations were remarkably low.

There is an important variation in MCC (ten to one) across towns

suggesting that each town needs to be considered separately at the time ofstudying congestion costs and potential congestion charges. There does

not seem to be any relationship between the number of trips and MCCthat would help understand the differences. The variations in MCC are

likely to be linked to the outlay of the town, the road density, junctions,width of roads, patterns of travel, etc. The study of these characteristics

requires a considerable amount of data and exceeds the scope of thestudy.

Generalised relationships

It was conjectured that a general speed-flow relationship could be foundthat would simplify marginal congestion costs calculations. In order to

find such general relationship, SATURN results for different towns wereused. Links in SATURN are assigned a fixed speed and intersections play

a major role in determining delays. Delays at each type of junction arecalculated using models suggested by the Transport Research Laboratory.

The processing is complex and cannot be reduced to a simple set ofsimultaneous equations and much less to a single equation.

SATURN runs on data contained in an origin-destination (O-D)matrix and a specification of the road network (characteristics of links

and junctions). The network is divided in zones. In order to go from oneorigin zone O to one destination zone D, drivers can choose from several

possible routes. The model simulates and assigns traffic until it finds theequilibrium, characterised by the situation in which no road user can

lower his (perceived) trip cost.We determined the curves representing average speed (veh-km/veh-

h) vs demanded volume (veh/h) in the central areas of ten English towns.On Figure 2 the curves are shown in the speed band relevant to

congestion (10 to 20 km/h).

KeyHerefordYorkLincolnNorwichKingston upon HullNorthamptonCambridgeBedford

Figure 2: Speed against demanded veh/h in central areas for different towns

The curves, in the congestion band, were approximated with a linear

function and the values of the coefficients α and β obtained for each of

them. These values were then used to estimate MCC as specified inNewbery (1990). These MCC turned out to be remarkably similar to the

ones computed directly from SATURN outputs.Our findings for the second objective can be summarised as follows:

(a) The general functional form of area-wide speed-flow relationshipswithin the range of speeds usually observed in city centres is

approximately linear around equilibrium, (b) The parameters α and βdiffer across towns and although there is a possibility that towns with

similar layouts may have similar parameters, the sample required todetermine that would need to be five or six times bigger than the one used

in this study, (c) Once α and β have been estimated for any particular

town, the MCC using the estimated area-wide speed-flow function andthe MCC using the SATURN outputs are remarkably similar.

0 0.5 1 1.5 2 2.5

x 104

0

5

10

15

20

25

Demanded veh/h in central area of town

Ave

rage

spe

ed (

veh-

km/v

eh-h

)

Table 3: Comparison of area speed flow relationship results with SATURN results

α β Actual flow(PCU per hour)

Speed (km/h) MCC(pence perPCUkm)

a b c d

Northampton 97.9 0.00567 15,049 12.6 11.8 758 561Kingston upon Hull 85.5 0.00446 15,834 14.9 14.1 448 485Hereford 82.3 0.00815 8,069 16.5 15.9 338 337York 56.5 0.00362 11,690 14.2 14 295 254Lincoln 59.7 0.00319 13,737 15.9 15.4 244 225Cambridge 52 0.00215 16,581 16.4 16.3 187 237Norwich 44.8 0.00199 14,831 15.3 15.9 177 203Bedford 71.8 0.00325 14,717 24 24.6 117 111

Source: see text. Note: Estimates in columns (a) and (c) were computed with the area wide speed-flowfunctions estimated from SATURN outputs. Estimates in columns (b) and (d) were computed directlyfrom SATURN results.

Cost-benefit analysisWe considered toll cordons to manage congestion in eight English towns.

In a cordon-pricing scheme a trip maker is charged a fixed amount toenter and/or leave the charged area at all or only some times of the day.

The physical location of the roadside sensors determines the boundary ofthe charged area and defines the cordon. We based the decision of where

to put the cordon on two main considerations: what seems to be the mostcongested area in the town in question, and what cordon would not allow

too many alternative routes.Cordon tolling would be worthwhile only if the net present value

(NPV) of benefits less costs were positive. Revenues are transfers, notbenefits, and should not be part of the cost benefit analysis, though they

are clearly of central interest to the charging authority and are themechanism by which the costs are covered. The benefits included in the

calculations are mainly reductions in travel times. There are additionalbenefits linked to the reduction in emissions, but not included in these

estimates of NPV or detailed in this report. A paper reporting theenvironmental results is listed in the Outputs Section below.

One obvious limitation of our approach is that SATURN is amedium-run model that holds car ownership and the O-D pattern trips

constant. We have assumed that the changes in social surplus computedfor the first year would hold during the whole life of the project. This is

of course unrealistic. In the long run higher elasticities should be used as

people and businesses might relocate and in addition, there may be

changes in the local authorities’ land use plans in response to changingtransport demands. The model does not allow for such longer-term

responses. To assess the full impact of any road-pricing scheme a morecomplex transport and land-use model would be needed, augmented with

some view about likely local authority responses. It might be possible toforecast traffic growth, but it is likely that traffic management

arrangements would be adapted to deal with such growth and the existingmodel would then no longer represent the network correctly. Our defence

of the simplifying assumption of constant traffic is that the long-runimpacts of relocation caused by road pricing are likely to reduce traffic,

while economic development is likely to increase traffic, making a no-change assumption not unreasonable. If anything, it is likely to

underestimate the benefits of road pricing.

Table 5: Net Present Value of a cordon toll in different towns (£1998 million)

Town Total cost Benefit Net Present Value Benefit/Cost

Cambridge Inner cordon 16.1 17.6 1.5 1.1Two cordons 22.3 63.6 41.4 2.9Outer cordon 15.9 89.6 73.7 5.6

Northampton 20.6 90.0 69.4 4.4Kingston upon Hull 21.3 123.5 102.2 5.8Hereford 9.7 19.5 9.8 2.0Lincoln 14.5 17.0 2.4 1.2Bedford 15.6 19.9 4.3 1.3Norwich 19.0 23.9 4.9 1.3York 13.2 27.4 14.2 2.1

Source: Own calculations. Note: discount rate: 6%, benefits assumed to be constant throughout the 30years. Elasticiy assumed: 0.2.

The 1998 Treasury test discount rate of 6% was used.3 A comparisonof costs and benefits indicates that road pricing would be very beneficial

in Kingston upon Hull and Northampton, and to a lesser extent inHereford and York, on our conservative estimates. These schemes would

become more beneficial if costs proved to be lower or if the elasticity ofthe demand proved to be higher than 0.2.

3 The Treasury was, in early 2001, reconsidering the test discount rate and may reduceit somewhat. If so, the benefit cost ratio would be increased.

Two cordons in Cambridge, one inner and one outer, each charging

£1.50 per crossing, yield a benefit-cost ratio almost three times as highand make the scheme attractive. A single outer cordon charging £5

increases the benefit-cost ratio to more than five times that of a singleinner cordon. This shows that where a single inner cordon scheme might

not be worthwhile, an outer cordon scheme could be, and a doublecordon, but with each charge optimally set, might do even better (though

if the tolls are not carefully set, much of the benefit may be lost).Location seems to be important as well, as an external cordon can change

the desirability of a scheme.The results presented in this report correspond to the cheapest

technology currently available in the UK. Comparisons with a moresophisticated and expensive technology are detailed in Santos, Rojey and

Newbery (2001).

Activities

We have maintained and strengthened our relations with the Departmentof Transport, Local Government and the Regions, and with the Institute

for Transport Studies at University of Leeds. The work attractedconsiderable interest in the DTLR, who co-sponsored related work on

cordon tolls and their distributional impact. We presented our results toDTLR staff and invited academics in March 2001. David Newbery also

delivered a lecture on the ‘Reform of Road Taxes and Charges’ in thePalace of Westminster to the Second AA Westminster Seminar on 17

December. 2001.

We have also presented our work at several conferences and seminars.

Conferences

2002 Spring Conference and Exhibit of the Institute of TransportationEngineers, Palm Harbor, Florida, March 24-27, 2002.

2002 Annual Meeting of the Royal Economic Society, University of

Warwick, March 25-27, 2002.

Transportation Research Board 81st Annual Meeting, Washington DC,January 13-17, 2002.

4ème Journée Transport, Univeristé de Cergy-Pontoise, Paris, December

13th 2001.

9th World Conference on Transport Research, Seoul, Korea, July 22-27,2001.

Workshop on Environmental Economics and the Economics of Congestion,

CESifo, Venice Summer Institute, Venice, July 19-22, 2001.

Transportation Research Board 80th Annual Meeting, Washington DC,January 7-11, 2001.

European Transport Conference, Homerton College, Cambridge,

September 11-13, 2000.

15th Annual Congress of the European Economic Association, FreeUniversity of Bozen, Bolzano, Italy, August 30 - September 2, 2000.

70th Annual Meeting of the Institute of Transportation Engineers,

Nashville, Tennessee, August 6-9, 2000.

15th International Symposium on Theory and Practice in TransportEconomics of the European Conference of Ministers of Transport, Aristotle

University of Thessaloniki, Greece, June 7-9, 2000.

Transportation Research Board 79th Annual Meeting, Washington DC,January 9-13, 2000.

Seminars

Department of Economics, University of Hull, October 25th, 2001.

Department of Applied Economics Work in Progress Series, April 30,

2001.

Environmental Seminar, University College London, October 12, 2000.

Outputs

Refereed Publications

Newbery, D. and G. Santos (2002), ‘Estimating Urban Road CongestionCharges’, CEPR Discussion Paper Series, N° 3176, CEPR, London.

Santos, G. (2002), ‘Double cordon tolls in urban areas to increase social

welfare’, paper published in the CD ROM of the TransportationResearch Board 81st Annual Meeting, Washington DC, Jan 13-17.

Santos, G., D. Newbery and L. Rojey (2001), ‘Static Vs. Demand Sensitive

Models and the Estimation of Efficient Cordon Tolls: An Exercise forEight English Towns’, Transportation Research Board Record, N°

1747, pp. 44-50.

Santos, G. and D. Newbery (2001), ‘Urban congestion charging: theory,practice and environmental consequences’, CESifo Working Paper

Series, N° 568, Munich.

Santos, G. (2000), ‘Road Pricing on the Basis of Congestion CostsConsistent Results from Two Historic UK Towns’, Transportation

Research Board Record, N° 1732, December. The paper won theTransportation Research Board’s Fred Burggraf Award, which

recognises excellence in transportation research by young researchers.

Santos, G., Rojey, L. and D. Newbery (2000), ‘The Environmental Benefits

from Road Pricing’, DAE Working Paper 0020, Department ofApplied Economics, University of Cambridge, October.

Santos, G. (2000), ‘On the Economic, Technological and Political Aspects

of Road Pricing as a Tool for Traffic Demand Management’,Proceedings of the European Transport Conference, Homerton

College, Cambridge, September 11-13, 2000.

Other papers

Santos, G. (2002), ‘Park & Ride as a traffic calming policy: the case ofCambridge, England’, paper presented at the 2002 Spring Conference

and Exhibit of the Institute of Transportation Engineers, Palm Harbor,Florida, March 24-27.

Santos, G., L. Rojey and D. Newbery (2001), ‘Congestion Management

with Tags or Smart Cards’, paper presented at the 2001 SpringConference and Exhibit of the Institute of Transportation Engineers,

Monterey, California, March 25-28.

Santos, G. (2000), ‘Congestion Management with Road Pricing: Would itbe Efficient?’, paper presented at the 70th Annual Meeting of the

Institute of Transportation Engineers, Nashville, Tennessee, August 6-9.

Santos, G. (2000), ‘Possible Road Pricing and Use of Revenues in

England’, presented at the PhD Conference, May 11, Faculty ofEconomics and Politics. Also presented at the 15th International

Symposium on Theory and Practice in Transport Economics of theEuropean Conference of Ministers of Transport, Aristotle University

of Thessaloniki, Greece, June 7-9, and at the 15th Annual Congress ofthe European Economic Association, Free University of Bozen,

Bolzano, Italy, August 30 - September 2.

Impacts

Both local authorities and the Department of Transport, Local Government

and the Regions have expressed interest in the results of the research. Also,the paper to be presented at the Annual Meeting of the Royal Economic

Society has already generated attention from the media.

Future Research Priorities

There are lines of research arising from this project, which might be

profitably pursued. These are:

• Distributional impacts of cordon tolls. There is evidence that the final

impact could be regressive, progressive or neutral. The idea would be

to look at a wider range of towns.

• Effects of cordon tolls with heterogeneous trip makers (different user

classes with different elasticities).

• Environmental effects of different road charging schemes.

References

Acts of Parliament (2000), Transport Act 2000 c. 38, HMSO, London.

http://www.hmso.gov.uk/acts/acts2000/20000038.htmButton, K. and E. Verhoef (1998), Road Pricing, Traffic Congestion and

the Environment, Edward Elgar, Lincolnshire.De Jong, G. and H. Gunn (2001), ‘Recent Evidence on Car Cost and Time

Elasticities of Travel Demand in Europe’, Journal of Transport

Economics and Policy, Vol. 35, Part 2, pp. 137-160.

Department of the Environment, Transport and the Regions (1997),Developing an Integrated Transport Policy.

http://www.detr.gov.uk/itd/consult/invite.htm.Department of the Environment, Transport and the Regions (1998a), A New

Deal for Transport: Better for Everyone, The Government’s WhitePaper on the Future of Transport, The Stationery Office, London, July.

Department of the Environment, Transport and the Regions (1998b),

Breaking the Logjam, December.http://www.detr.gov.uk/itwp/logjam/foreword.htm

Department of the Environment, Transport and the Regions (1998c), An

Approach to Estimating the Welfare Costs of Congestion through the

NRTF 1997 Framework, DETR, London, April.Department of the Environment, Transport and the Regions (2000),

Modelling using the National Road Traffic Forecasting Framework

for Tackling Congestion and Pollution and Transport 2010: The 10

Year Plan, Technical Report, DETR, London, December.Fowkes, A. S., Sherwood, N. and C. A. Nash (1993), ‘Segmentation of

the Travel Market in London: Estimates of Elasticities and Values ofTravel Time’, Working Paper 345, Institute for Transport Studies,

University of Leeds, Leeds, May.Goodwin, P. B. (1992), ‘A Review of New Demand Elasticities with

Reference to Short and Long Run Effects to Price Changes’, Journal

of Transport Economics and Policy, Vol. 26, N° 2, pp. 155-169.

Lewis, N. C. (1994), Road Pricing: Theory and Practice, Thomas TelfordServices Ltd., London.

Milne, D. and D. Van Vliet (1993), ‘Implementing Road User Charging inSATURN’, ITS Working Paper 410, Institute for Transport Studies,

University of Leeds, December.Morrison, S. A. (1986), ‘A Survey of Road Pricing’, Transportation

Research, Vol. 20A, N° 2, 87-97.Newbery, D. M. (1990), ‘Pricing and Congestion: Economic Principles

Relevant to Pricing Roads’, Oxford Review of Economic Policy, Vol.6, N° 2, 22-38.

Newbery, D. M. (2000), ‘Measuring Congestion Costs Geometrically’,Mimeo, Department of Applied Economics, Cambridge, December.

Newbery, D. and G. Santos (1999), ‘Road Taxes, Road User Charges andEarmarking’, Fiscal Studies, Vol. 20, No. 2, pp. 103-132.

Oum, T. H., Waters II, W. G. and J-S. Yong (1992), ‘Concepts of PriceElasticities of Transport Demand and Recent Empirical Estimates’,

Journal of Transport Economics and Policy, Vol. 26, N° 2, pp. 139-154.

Santos, G., D. Newbery and L. Rojey (2001), ‘Static Vs. Demand Sensitive

Models and the Estimation of Efficient Cordon Tolls: An Exercise forEight English Towns’, Transportation Research Board Record, N°

1747, pp. 44-50.The MathWorks (1994), Matlab for Windows, Version 4.2c.1, Natick,

Mass.Van Vliet, D. and M. Hall. SATURN 9.3 User Manual. The Institute for

Transport Studies, University of Leeds, Leeds, 1997.Victoria Transport Policy Institute (2000), ‘Transportation Elasticites’, in

TDM Encyclopedia.http://www.vtpi.org/tdm/tdm11.htm