TI 2000-101/3
Tinbergen Institute Discussion Paper
Traffic Congestion and Traffic Congestion and Congestion Pricing Congestion Pricing
C. Robin Lindy
Erik T. Verhoef
Tinbergen Institute
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T RAFFIC C ONGESTION AND C ONGESTION P RICING*
C. Robin Lindy1 and Erik T. Verhoef2**
1Department of Economics University of Alberta
Edmonton
Alberta
Canada
Phone: +1-780-4927642
Fax: +1-780-4923300
E-mail: crli@econ.ualberta.ca
2Department of Spatial Economics
Free University Amsterdam
De Boelelaan 1105
1081 HV Amsterdam
The Netherlands
Phone: +31-20-4446094
Fax: +31-20-4446004青蛙怎么养
E-mail: everhoef@econ.vu.nl This version: 14/11/00
Key words: congestion, road pricing, networks
JEL codes: R41, R48, D62
Abstract
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For veral decades growth of traffic volumes has outstripped investments in road infrastructure. Th
e result has been a relentless increa in traffic congestion. This paper reviews the economic principles behind congestion pricing in static and dynamic ttings, which derive from the benefits of charging travellers for the externalities they create. Special attention is paid to various complications that make simple textbook congestion pricing models of limited relevance, and dictate that congestion pricing schemes be studied from the perspective of the theory of the cond best. The complications include pricing in networks, heterogeneity of urs, stochastic congestion, interactions of the transport ctor with the rest of the economy, and tolling on private roads. Also the implications of congestion pricing for optimal road capacity are considered, and finally some explanations for the longstanding social and political resistance to road pricing are offered.
*The authors would like to thank Richard Arnott, André de Palma, Claude Penchina, Stef Proost and especially Ken Small for corrections and insightful comments on an earlier version of this paper. Any remaining errors, however, are the authors’ responsibility alone.
**Erik Verhoef is affiliated as a rearch fellow to the Tinbergen Institute. Erik Verhoef’s rearch has been supported by a fellowship of the Royal Netherlands Academy of Arts and Sciences.
1.Introduction
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For veral decades growth of traffic volumes has outstripped investments in road infrastructure. The result has been a relentless increa in traffic congestion. Congestion impos various costs on travellers: reduced speeds and incread travel times, a decrea in travel time reliability, greater fuel consumption and vehicle wear, inconvenience from rescheduling trips or using alternative travel modes, and (in the longer run) the costs of relocating residences and jobs. The costs of incread travel times and fuel consumption alone are estimated to amount to hundreds of dollars per capita per year in the US (Schrank and Lomax, 1999) and comparable values have been reported for Europe.
Traffic congestion is a conquence of the nature of supply and demand: capacity is time-consuming and costly to build and is fixed for long time periods, demand fluctuates over time, and transport rvices cannot be stored to smooth imbalances between capacity and demand. Various policies to curb traffic congestion have been adopted or propod over the years. The traditional respon is to expand capacity by building new roads or upgrading existing ones. A cond method is to reduce demand by discouraging peak-period travel, limiting access to congested areas by using permit systems and parking restrictions, imposing bans on commercial vehicles during certain hours, and so on. A third approach is to improve the efficiency of the road system, so that the same demand can
be accommodated at a lower cost. Re-timing of traffic lights, metering access to highway entrance ramps, high-occupancy vehicle lanes and Advanced Traveller Information Systems are examples of such measures.
This paper is concerned with congestion pricing as a tool for alleviating traffic congestion. The insight for congestion pricing comes from the obrvation that people tend to make socially efficient choices when they are faced with all the social benefits and costs of their actions. As just noted various demand management tools to accomplish this can be ud. But congestion pricing is widely viewed by economists as the most efficient means becau it employs the price mechanism, with all its advantages of clarity, universality, and efficiency. Pigou (1920) and Knight (1924) were the first to advocate it. But it was the late William Vickrey, who steadfastly promoted congestion pricing for some forty years, who was arguably the most influential in making the ca on both theoretical and practical grounds. In one of his early advocacy pieces, Vickrey (1963) identified the potential for road pricing to influence travellers’ choice of route and travel mode, and its implications for land u. He also discusd alternative methods of automated toll collection. Another of his early proposals was to t parking fees in real time as a function of the occupancy rate. An overview of Vickrey’s contributions to pricing of urban private and public transport is found in Arnott et al. (1994, pp. 271-5).
As Vickrey’s work makes clear, true congestion pricing entails tting tolls that match the verity of congestion, which requires that tolls vary according to time, location, type of vehicle and current circumstances (e.g. accidents or bad weather). Congestion pricing is
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Traffic Congestion and Congestion Pricing
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common in other ctors of the economy — from telephone rates and air fares to hotels and public utilities. But despite the efforts of Vickrey and other economists, congestion pricing is still rarely ud on roads. Tolls are not charged on most roads, and fuel taxes do not vary with traffic volumes. And costs of registration, licensing and insurance do not even depend on distance travelled. Nevertheless, the number of applications and experiments in road pricing is slowly growing, spurred on by the combined impetus of worning traffic conditions and advances in automatic vehicle identification technology. Descriptions of various road pricing schemes, including Singapore’s pioneering toll system, Scandinavian toll-rings, and Californian pay-lanes are found in Gómez-Ibáñez and Small (1994) and Small and Gómez-Ibáñez (1998).
This paper is organized along similar lines to the review of congestion modelling in Lindy and Verh
oef (2000). Section 2 starts by outlining the basic economic principles of congestion pricing in a simple static (‘time-independent’) tting with one road. Section 3 adds a time element by considering travellers’ time-of-u decisions and time-varying tolls. Various complications are addresd in Section 4, including pricing in networks, heterogeneity of urs, stochastic congestion, interactions of the transport ctor with the rest of the economy, and tolling on private roads. Section 5 considers the implications of congestion pricing for optimal road capacity. Explanations for the longstanding social and political resistance to road pricing are offered in Section 6, and conclusions are drawn in Section 7. Due to space constraints some topics related to congestion pricing are not covered in this review. There is no explicit treatment of freight transportation. Nothing is said about the implications of congestion pricing for urban structure or the location of new developments. And only passing mention in Section 4 is given to the potential effects of congestion pricing on traffic noi, pollution, and traffic accidents.
2.Congestion pricing in time-independent models
饺子的英语The basic principles of congestion pricing can be illustrated in the following simple tting. Consider one origin and one destination connected by a single road. Individuals make trips alone in identical vehicles. Traffic flows, speeds and densities are uniform along the road and independent of time. Eq
uilibrium in this tting is described in Figure 1, which is due to Walters (1961). The horizontal axis depicts traffic flow or volume: the rate at which trips are initiated and completed. The vertical axis depicts the price or ‘generalized cost’ of a trip —which includes vehicle operating costs, the time costs of travel, and any toll. At low volumes vehicles can travel at the free-flow speed, and the trip cost curve, C(q), is constant at the free-flow cost C ff. At higher volumes congestion develops, speed falls, and C(q) slopes upwards. (Figure 1 ignores the possibility of ‘hypercongestion’ that would cau C(q) to bend backwards on itlf; e Lindy and Verhoef, 2000.)
If flow is interpreted to be the quantity of trips “demanded” per unit of time, then a demand curve p(q) can be added to Figure 1 to obtain a supply-demand diagram. The demand
