A Review of Congestion Area-wide Pricing Strategies

And Their Public Acceptability

Louis Lafata

TR-7123

Spring 2015

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Table of Contents

Introduction..... 1

Stockholm, Sweden..... 6

London, United Kingdom. ..... 8

Singapore, Republic of Singapore... 10

Seattle, Washington........ 13

Acceptability of Congestion Pricing... 14

Congestion Pricing System Effects & Comparisons... 16

Conclusion....... 17

References..... 18

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Introduction

Increasing levels of traffic congestion pose a variety of detrimental effects to the

populations of cities and surrounding suburban areas including longer delay times, increased

pollution and higher fuel consumption. If left unchecked, these harmful effects can severely

lower the productivity of cities and negatively impact quality of life. Many regions around the

world have successfully implemented congestion pricing strategies to mitigate traffic congestion

and reduce traffic demand to reasonable levels. Cities such as Stockholm, London and Singapore

have successfully mitigated growing demand volumes, high travel time rates and increasing

miles of vehicle travel through various congestion pricing techniques. The implementation of

these systems has typically been faced with initial strong opposition and public concern, but

those cities that have managed to put congestion pricing schemes in place have realized

increased public support and various congestion management advantages.

Traffic congestion is increasing dramatically in urban areas around the world,

prominently in larger cities. In the United States, the overall percentage of congested peak period

traffic has risen from 33% in 1982 to 67% in 2001. According to the 2012 Texas Transportation

Institute report, the U.S. experienced 4.2 billion hours of congestion delay in 2007 along with 2.8

billion gallons of additional fuel consumption (Urban Mobility Report, 2012). In the United

States between 1986 and 2011, the percentage of peak vehicle miles traveled that were congested

increased from 26% to 55% (Urban Mobility Report, 2012). Chicago, IL experienced an increase

from 34% to 88% of congested peak VMT and the New York-New Jersey-Connecticut

metropolitan area experienced an increase from 41% to 52% during the same time frame. Figure

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15

25

35

45

55

65

75

1982 1987 1992 1997 2002 2007 2012

Per

cen

tag

e o

f C

on

ges

ted

Pea

k V

MT

Year

Yearly Congested Travel in the U.S.

(% of Peak VMT)

Very Large

Medium

Small

Figure A: Yearly Congested Travel in the U.S. (Urban Mobility Report, 2012)

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A shows the increase in the average percentage of congested peak vehicle-miles traveled for very

large, medium and small-sized metropolitan areas in the United States from 1982 to 2011. The

very large category experienced the largest increase from 37% to 76%. The medium category

showed an increase from 15% to 37% and the small category from 8% to 27%. These changes in

percentages of congested travel show that traffic congestion situations in large urban areas are

deteriorating most rapidly.

Traffic congestion costs nations billions of dollars annually (Urban Mobility Report,

2009). Annual costs of traffic congestion in the United Kingdom are estimated to be 15 billion

(de Palma and Lindsey, 2011). These increased costs are a result of increases in urban

populations, increased demand for roadway travel and increased costs associated with delay and

additional planning time. The Center for Economics and Business Research has estimated the

total cost of traffic congestion for the United Kingdom, France, Germany and the United States

in terms of direct costs and indirect costs. Direct traffic congestion costs encompass the value of

fuel and time wasted in traffic and indirect costs include the increased cost of business

operations. It is estimated that the combined cost of roadway traffic congestion to the economies

of these four nations was $200.7 billion in 2013, and is projected to increase to $293.1 billion by

2030 (Centre for Economics and Business Research, 2014). The projected increase is based on

an expected 19% increase in passenger vehicle miles traveled, a 14% increase in freight vehicle

miles traveled and a 6% increase in delay time as a result of congestion (Centre for Economics

and Business Research, 2014). The increasing amount of delay time being and the associated

costs have significant aggregate economic impacts on cities.

Traffic congestion has been worsening for several reasons. There is an overall increase in

demand for specific facilities that serve centers of activity due to population growth, economic

growth and growing incomes. The capacity of transportation facilities has not kept pace with

demand increases, leading to more frequent and more intense occurrences of traffic congestion

(Hensher and Puckett, 2007). Pricing techniques have been implemented in several cities around

the world such as Stockholm, Singapore and London in an effort to curb the growing negative

effects of traffic congestion. They have in general been successful once implemented, although

garnering initial public and political support has proven difficult.

Congestion pricing techniques are implemented by municipalities whose objective is to

reduce the demand volume for a specific facility. The demand is reduced by imposing a toll that

will incentivize drivers who have more flexibility in their travel patterns to either utilize different

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transportation modes, travel during a different time window, eliminate the trip altogether or

combine multiple trips that would otherwise be made separately. The value of the charge is

typically the cost of delay imposed on other users of the facility, or the marginal social cost.

When charges are imposed on a roadway, drivers will compare the value of the trip to the price

of the congestion charge, making the decision to either pay the toll, forego the trip or shift to a

more cost effective mode of transport (Congress of the United States, 2011). Overall the effect is

a decrease in traffic volume. If prices are variable and are higher during peak congestion periods,

vehicles will tend to concentrate less in space and time and the existing roadway capacity will be

utilized more efficiently. This phenomenon is called peak spreading. Drivers will alter the

times during which they travel in an effort to avoid the most congested times of day, effectively

mitigating congestion and spreading out the peak demand volumes (Wolff and Villain, 2007).

Not only will pricing reduce congestion, it also has the potential to provide the municipality with

a source of revenue. Transportation agencies can reinvest the generated revenue from tolls into

improvement projects such as capacity expansion or transit service expansion (Transportation

Research Board, 2012). Businesses also benefit from lower costs associated with decreased

buffer time that is typically allotted in anticipation of delay caused by traffic congestion (Brown

et al., 2001). When demand is effectively managed, drivers experience savings in travel time,

new travel options and improvements in travel time reliability while businesses and

municipalities benefit economically.

Roadway congestion pricing takes various forms including variably priced lanes, variable

tolls on entire roadways, cordon charges and zone-based charges (Brown et al. 2001). High

occupancy toll (HOT) lanes are a form of variably priced lanes. They are managed lanes made

available free of charge to high occupancy vehicles and charge standard occupancy vehicles for

the use of the lane. Transit vehicles are typically allowed to use the lane for free or for a reduced

charge. Implementing a HOT lane provides drivers with more travel time savings and higher

travel time reliability, as the lanes are managed to be able to maintain free flow conditions even

during the peak hour (Federal Highway Administration, 2000). Importantly, congestion pricing

has a sub-component of value pricing, where only a portion of the roadway implements a pricing

structure. In this instance, drivers have the ability to choose to either use the tolled managed lane

or freely use the general purpose lanes, depending on the perceived value of the trip (Victoria

Transport Policy Institute, 2013).

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Variable tolls are usually implemented on roadways where toll infrastructure already

exists. Toll prices can be varied at fixed increments based on the time of day or can function

dynamically, where the charge is more precisely related to the roadway congestion level. Tolls

are charged only when congestion occurs to increase efficiency and maintain flow (Texas

Transportation Institute, n.d.). Zone-based congestion pricing is when a vehicle is tolled to enter

or exit a specific zone, as well as tolled to travel within the zone. Cordon pricing is implemented

by charging a toll to enter a specific geographic area, typically a central business district. In cities

that are situated on separate landmasses with limited points of entry to the city center such as

Stockholm, cordon charges are more easily implemented. Cities such as London, which are not

completely separated geographically, would likely implement a zone-based approach. Tolls can

be charged in the outbound direction, inbound direction or both, and can have radial elements

that control movement around the cordon area (de Palma and Lindsey, 2011). Benefits of cordon

pricing include decreased roadway congestion within the zone and the increased use of

alternative transportation modes (California Transportation Commission, n.d.).

Stockholm, Sweden

Stockholm implemented a congestion pricing trial in 2006 from January 3rd to July 31

st to

assess the impact of imposed congestion charges on the overall network efficiency. After the

observed success of the program, the pricing scheme became permanent in 2007 (de Palma and

Lindsey, 2011). A cordon area pricing scheme was established around the inner city of

Stockholm, an area of about 11.6 square miles. The dashed line in Figure B delineates the cordon

area, where a total of 18 charging points were created. The bold line represents the single road

that has no toll, a result of its bypassing route and political influence (Eliasson et al., 2009).

The goals of the metropolitan area are to reduce traffic congestion, increase accessibility

and improve environmental quality (Eliasson et al., 2009). Four months prior to the

implementation of congestion pricing, the public transit network was expanded to offer 16 new

bus routes, more park and ride facilities and additional rail departures, expanding overall

transport services by 7%. Toll prices were varied based on time of weekday at fixed increments.

Charges of 10, 15 or 20 SEK ($1.2, $1.8, $2.3 respectively) were levied between the hours of

6:30 am to 6:30 pm with the two peak pricing periods being at 7:30-8:30 am and 4:30-5:30 pm.

No fees were imposed on evenings, weekends or holidays and the maximum daily payment was

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60 SEK ($7) (Eliasson et al., 2009). Vehicles are identified by a camera system that photographs

the license plate, eliminating any need for the vehicles to slow down to pay the toll (Seattle

DOT, n.d.). Certain vehicles, including emergency vehicles, buses and taxis are exempt from the

tax. The total cost of system implementation was $510 million and the estimated annual profit is

$75 million (Seattle DOT, n.d.).

Figure B: Stockholm Congestion Pricing Zone (Eliasson et al., 2009)

Initial estimates of traffic volume reduction through the cordon ranged from 20% to 25%.

These estimates were quite accurate, as the final reduction in volume settled at 22% (Eliasson et

al., 2009). The highest reduction occurred in the 4:00 pm to 6:00 pm peak period at 23%,

whereas the morning 7:00 am to 9:00 am peak period volume decreased by 18%. Traffic

volumes decreased on arterials and both major and minor streets inside the cordon as well as

outside and close to the cordon. The only significant increases in traffic volume occurred on the

Essinge Bypass (4%) and the Southern Link (10%). The high increase in volume on the Southern

Link is believed to be the result of a natural increase in traffic volume from the suburbs to the

south (Eliasson et al., 2009). Travel time also significantly decreased. On all cordon area arterial

roads except the Essinge Bypass and the Southern link, average travel times decreased by about

33% during the morning peak period and by about 50% during the afternoon peak period. It was

also observed that roughly 50% of the eliminated trips were commuters and that 96% of these

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trips shifted modes from auto to public transit. The other half of eliminated trips were either

cancelled completely or combined with other trips (Eliasson et al., 2009). Public transit ridership

increased by 6% soon after the congestion pricing scheme was implemented, of which 4.5% is

attributed the actual imposed toll charges and 1.5% to other factors such as gas prices and

business conditions (Eliasson et al., 2009).

London, United Kingdom

In February 2003, London became the first major European city to implement a zone-

based congestion pricing system. The city charged vehicles driving or parking on roads in the

Central London cordon area. In 2007, the City of London expanded the zone westward, but the

area was reduced back the original boundary the same year due to strong opposition (Victoria

Transport Policy Institute, 2011). The initial price was 5, which was increased to 8 in July

2005. A driver only pays the toll one time, and is permitted to use all roads within the charging

for the duration of the day. The zone is eight square miles and is bound by the ring road that

surrounds Central London (Figure C). Tolls are not levied on the ring road itself.

Figure C: London Congestion Charging Zone (Transport for London, 2004)

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The congestion charge is a fixed-rate daily toll. It does not vary with peak and off peak

periods. People who reside within the cordon area are entitled to a 90% discount on the

congestion price and vehicles exempt from the charge include emergency vehicles, buses and

registered taxis (Leape, 2006). Tolls are collected through an electronic system, so traffic does

not need to slow down at tollbooths. Video cameras at all zone entry points as well as on mobile

vehicles within the charging zone capture vehicle license plate numbers to enforce payment

compliance. (Leape, 2006).

Initial predictions of the effects on congestion were a 20-25% decrease in car VMT in the

Central London area and a 10-15% decrease in overall VMT in the area. Between 2002 and 2003

when the congestion charge was implemented, passenger car miles traveled deceased by 33%,

truck miles traveled decreased by 7%, bus miles traveled increased by 22% and overall vehicle

miles traveled decreased by 12% (Leape, 2006). It is estimated that of the 33% reduction in car

travel, half of these trips shifted to other modes of public transport, 25% are believed to have

diverted around the charging zone and about 10% have switched to another form of private

transport. Travel network speeds within the charging zone have also improved from an initial

speed of 8.9 miles per hour to 10.4 miles per hour in June of 2003 (Leape, 2006). The average

travel time rate on the main roads approaching the charging zone improved, decreasing 20%

from 2.41 minutes per mile to 1.93 minutes per mile. Some small net increases in traffic

congestion have been reported in areas neighboring the congestion charging zone in the first year

it was implemented, but this trend did not continue as traffic congestion decreased in all areas the

subsequent year (Leape, 2006).

Initial estimates of overall public transit usage increases were 3% for individuals

traveling into the charging zone and 4% for those traveling within the zone. Ultimately, bus

ridership in the morning peak period increased by 38%. Rail trips however declined, thought to

be a product of facility closures for improvements and stunted local economic conditions (Leape,

2006). The increase in bus usage was evenly attributed to both improved bus service and the

existence of the congestion charge.

The Victoria Transport Policy Institute does not consider Londons congestion pricing

scheme to be optimal for a number of reasons. The fee is not based on the amount of miles a

driver travels, so there is no incentive to minimize the trip length once the toll is paid. The fee

does not vary based on time of day or location, eliminating the desired peak spreading effect,

especially on the most congested facilities (Victoria Transport Policy Institute, 2011). Although

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the City of London has generated some surplus revenue from congestion pricing, the actual

amounts were far lower than expected. Revenues of 68 million in 2003-2004 and 97 million

in 2004-2005 fell short of forecasted net revenues of 230-280 million. The low amounts were a

result of the larger than anticipated decrease in car traffic which would have typically added to

the generated revenue, the large number of discounted vehicles paying low tolls, significant

levels of nonpayment and large amounts of enforcement spending (Leape, 2006).

Singapore, Republic of Singapore

The city with the longest experience in congestion pricing, Singapore implemented an

area based tolling scheme in June of 1975, updating it to an electronic road pricing system in

1998 (Seng, 2014). Between 1975 and 1998, Singapore applied the Area Licensing Scheme

(ALS), where drivers were tolled to enter the central business district or the restricted zone, an

area of roughly 2.4 square miles (Figure D). There were initially 22 points of entry to the

charging area, all of which were monitored manually by operators (Small and Gomez-Ibanez,

1998). In order to enter the charging area, drivers were required to purchase and display a

mountable car decal.

Figure D: Singapore Restricted Zone Charging Area

(Small and Gomez-Ibanez, 1998)

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The main objective of the city was to reduce traffic volume in the morning peak hours by

25 to 30%. It was anticipated at the time that evening peak travel would be reduced by the same

magnitude. Drivers were tolled a fixed price of S$3 to enter the restricted zone during the

morning peak period between 7:30 am and 9:30 am, Monday through Saturday. Full month

licenses were also available for S$60. Buses, military vehicles, goods vehicles and vehicles

carrying four or more passengers were exempt from the congestion charge. In an additional

effort to encourage carpooling, parking prices within the restricted zone were doubled and a

park-and-ride system was implemented (Yong-Phang and Toh, 2004).

Overall the ALS system achieved a reduction in travel of 43% during the charging

period. Auto travel during the charging period decreased by 76.2% and travel by other vehicles

decreased by 1.5%. The half hour before the charging period, from 7:00 am to 7:30 am, overall

travel increased by 18.1% and the half hour after the charging period, from 9:30 am to 10:00 am,

overall travel increased by 17.7% (Yong-Phang and Toh, 2004). The congestion charge

essentially achieved a peak spreading effect, where some vehicles shifted the time frame during

which they travel. A certain percentage of vehicles also diverted their routes around the charging

zone in an effort to avoid paying the daily license fee. The desired effect of volume reduction

during the evening peak period was not observed, due to a lack of incentive to redistribute the

times during which trips are made (Yong-Phang and Toh, 2004). Various negative effects were

also observed due to the structure of the congestion scheme, the most prominent being a large

underutilization of existing road capacity during the charging period. A large number of vehicles

shifted their trip time outside the charging period simply redistributing the traffic congestion to

these times (Yong-Phang and Toh, 2004). Over the next decade, the City of Singapore made

changes to the lengths of the congestion charging window, the prices charged for various vehicle

types and in 1989, implemented an evening congestion charging period. However, the imbalance

still existed where an undesirable intensity of traffic congestion occurred during times

surrounding the charging time window.

In 1994, Singapore transitioned from the previous part time ALS to a whole-day ALS

with a shoulder pricing system. This effort was in response to low traffic demand volumes

occurring during what would normally be considered the actual peak periods and higher demand

during times surrounding the existing congestion charging period. The goal was to mitigate this

imbalance in traffic demand and entice more drivers to make their trip before or after, but not

during the peak period. Under this new ALS, drivers were tolled between the hours of 7:30 am

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and 6:30 pm Monday through Friday and from 10:15 am to 3:00 pm on Saturday, instead of

being charged only during the peak periods. The standard price for cars entering the congestion

charging area was S$2 and part day licenses were offered for purchase. During the shoulder

periods, which were defined as the periods surrounding the am and pm peak periods, a lower toll

was charged, essentially attracting a percentage of the drivers who would typically attempt to

avoid all congestion charges. Total morning traffic increased from 49,000 vehicles to 60,000

vehicles, midday traffic decreased from 169,000 vehicles to 143,000 vehicles and evening traffic

increased from 28,000 to 34,000 vehicles (Yong-Phang and Toh, 2004). These results suggest

that a percentage of those vehicles attempting to avoid the congestion charge shifted their travel

time to the shoulder period or the peak period. This provided more of an opportunity for drivers

to take advantage of existing capacity. Even with these successes, the manual system was

complicated to manage and enforce given the large number of license and vehicle types and the

ability of individual licenses to be physically shared between multiple vehicles. This was the

motivation for Singapore to adopt the Electronic Road Pricing (ERP) scheme (Yong-Phang and

Toh, 2004).

With the implementation of the ERP system, drivers were provided with in-vehicle unit

transponders that operate using radio frequency, optical detection, imaging and smart-card

technologies (Yong-Phang and Toh, 2004). Drivers are charged a toll based on the vehicle type

and time of entry. Toll prices are predetermined and are reviewed and updated on a quarterly

basis by the Land Transportation Authority in an effort to maintain the desired roadway speeds

of 28 mph to 40.4 mph on expressways and 12.5 mph to 18.6 mph on arterial roads (de Palma

and Lindsey, 2011). All vehicles except for emergency vehicles are required to pay the toll. After

switching over to the ERP system from the ALS in 1998, traffic volume into the central business

district decreased by approximately 10 to 15%. This is a result of a decrease in the number of

multiple trips that were previously made using the same area license during a single day.

Considering overall statistics to the year 2000, the morning peak period showed a 13% decrease

in volume traveling to the central business district, the evening peak period showed a 8%

decrease in volume, and the 7.5 hour in-between off peak period showed a 9% decrease in

volume. Since 1998, the ERP system has been significantly expanded and upgraded. In 1998

when the ERP scheme was launched, 33 gantry points were in use, which has since increased to

66 gantries in 2010 (2005 was the first year ERP was used to manage congestion in the evening

13

rush period. The ERP system generates profits of about $40 million annually (Danish

Architecture Center, 2014).

Seattle, Washington

The City of Seattle is one of the next cities to follow suit with a congestion pricing

program. In 2009, the city conducted an initial study modeling the effects of possible types of

congestion pricing techniques. Seattles objectives are to reduce greenhouse gas emissions,

generate revenue for transportation infrastructure improvements, improve roadway efficiency

and maximize throughput capacity. With the main effort to reduce greenhouse gas emissions,

Seattles goals are to encourage the shift of travel mode from car to transit. This is planned to be

implemented by setting toll prices higher than the transit fare or at the marginal social cost of

roadway driving, charging higher tolls for less fuel-efficient vehicles and providing toll discounts

for high occupancy vehicles, decreasing the likelihood of transit riders switching to automobile

travel and discouraging the use of less fuel-efficient vehicles (Booz et al., 2009).

Implementing toll facilities on highly traveled roadways can tend to cause traffic

diversion onto other non-tolled roadways or onto local roads. The Washington State

Transportation Commission expressed this effect to be of major concern when choosing the

facilities that will be converted into toll roads. Tolls would not be charged at locations too far

from the destinations of many travelers, which would encourage exiting the roadway prior to

paying the toll and using local roadways for the remaining portion of the trip. Tolls would also

be charged on roadways that run parallel to the tolled routes initially selected in order to prevent

diversion (Booz et al., 2009).

Seattle has projected that implementing dynamic tolling on the full roadway system

(defined as all freeways and arterial roadways within the urban growth area) will increase the

daily average freeway speed by 24%, increase the average arterial speed by 7.3%, decrease the

VMT per capita by 11% and generate annual revenues of $6.1 billion. The city has projected that

implementing tolling on the freeway system (defined as all limited access roadways within the

urban growth area) will increase the daily average freeway speed by 26.5%, decrease the average

arterial speed by 3.2%, decrease the VMT per capita by 6.2% and generate $1.9 billion (Booz et

al., 2009).

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Acceptability of Congestion Pricing

Congestion pricing system proposals are often met with a mix of support and opposition,

from different groups. Transportation economists, traffic engineers and planners support pricing

efforts, but public acceptance is often mixed and usually low initially, often preventing

implementation. There are several components to the acceptability of congestion pricing

schemes, including the self-interest of the individual driver, the perceived fairness of the pricing

system, external political factors and individual opinions about congestion pricing (Hamilton,

2012). With the implementation of a congestion pricing scheme, drivers are typically concerned

about the out of pocket expenses, travel time savings and the benefits from the use of revenue

generated for the municipality, all of which show to be highly significant factors affecting a

schemes acceptability (Hamilton, 2012). With the implementation of a congestion scheme,

many drivers realize benefits such as reduced travel time that are more significant than

anticipated and discover that the charges imposed do not impact them as much as expected

(Hamilton, 2012). Regarding fairness, there are equity concerns for drivers who are negatively

impacted by the tolls as well as concern for those drivers who do not have any available

transportation alternatives. Attitudes toward congestion pricing schemes can be based on

political viewpoints concerning environmental quality as well as overall trust in government.

People who are more concerned about environmental issues are more likely to support

congestion pricing. Those individuals who do not trust government agencies to manage

congestion pricing systems or to correctly allocate the revenue generated are more likely to

oppose pricing, even if they agree with the principle from an economic or environmental

standpoint (Hamilton, 2012). When the reason for implementing the congestion pricing scheme

is well defined to the public, acceptability has shown to increase. Also, it has been demonstrated

that public acceptability rises with the length of time a congestion pricing scheme has been

implemented (Bhatt et al, 2008). There is a psychological aspect to any resistance to the charging

scheme, which tends to dissipate after the system is in place (Hamilton, 2012).

In Stockholm, the initial proposition for the congestion charging trial was met with strong

opposition. Overall support before the trial was 40%. Support declined to 36% when the start

date of the trial approached, but once the congestion trial started, acceptance jumped to 52%.

When the permanent congestion charging scheme was implemented in 2007 acceptance

increased to 66%, further increasing to 70% by 2011 (Hamilton, 2012). The increase in

15

acceptance was primarily due to the effectiveness in mitigating traffic congestion along with a

plan to make the benefits widely known to the public. Another important effect was the

communication of the expected environmental benefits.

Londons congestion charging scheme was also met with strong opposition from the start.

Many politicians, labor organizations and other interest groups did not support the idea. Once the

scheme was implemented, the public realized its benefits and attitudes changed. Travel became

30% more reliable (Leape, 2006). Many businesses have expressed satisfaction with the

congestion charge, as they have found that travel and delivery times have been substantially

shortened and employees spend less time delayed in traffic (Litman, 2005).

New York City went through the process of proposing a congestion charging scheme in

2007. Introduced by Mayor Michael Bloomberg, it was the first area-wide system to be proposed

for a North American city. It faced conditional opposition from residents of the five boroughs

and was ultimately blocked by the state legislature. The general concept of the plan was to toll

cars a fixed daily fee of $8 to enter the pricing zone in Manhattan between the hours of 6:00 AM

and 6:00 PM. The complete congestion pricing plan was developed by the Traffic Congestion

Mitigation Commission in January 2008, which was largely based off the PlaNYC initiative that

was presented in April 2007.

Only 38% of New York City voters supported the congestion pricing plan for driving

below 60th street in Manhattan, but the resistance was much less given the stipulation that all

generated revenue would be used for transit improvements with 59% of voters supporting. 43%

of voters believed it was likely that funds from congestion pricing would actually be used for

transit improvements (Quinnipiac University, 2008). The opposition to the congestion pricing

scheme was based primarily on driver self-interests. Much of the of resistance came from elected

officials of the farthest areas surrounding Manhattan, specifically southern Brooklyn and eastern

Queens, where the populations are more auto-dependent and mass transit options are sparse.

These individuals believed they were being unfairly charged. More opposition came from those

who believed that mass transit would not be a viable alternative to driving (Schaller, 2010).

Some individuals believed that the congestion and overcrowding on mass transit lines would

only become worse. Other opponents did not trust that the generated funds would reach the

Metropolitan Transportation Authority and/or that the agency would not use the funds for the

intended purpose of transit improvement (Schaller, 2010).

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Congestion Pricing System Effects & Comparisons

The cities that have successfully implemented congestion pricing schemes and overcome

opposition have experienced considerable results in mitigating traffic congestion issues.

Stockholm, London and Singapore are three unique examples where traffic congestion was

significantly mitigated using differing area-based techniques. Stockholm was able to decrease

evening peak period travel by 23%, while Singapore was able to decrease volume traveling into

the central business district by 13%. London was able to decrease average travel time rates by

20% while Stockholm was able to decrease travel time rates by 30%.

Several conclusions can be made regarding traffic patterns that result from congestion

pricing implementation. In the case of Stockholm, it was observed that the evening peak period

was mitigated more than the morning peak period, meaning that more discretionary trips are

made during the afternoon/evening period and/or that departure times from work are more

flexible than arrival times. Part of the success of a congestion pricing system like Stockholms is

providing a full-day charging period. With Singapores initial morning peak period pricing

scheme, the peak spreading effect was not realized because more drivers simply moved the time

frame during which they traveled, in effect shifting congestion instead of mitigating it by

spreading it out.

As stated by the Victoria Transport Policy Institute, congestion pricing systems are most

efficient when the price of the toll varies with time of day or level of congestion (Victoria

Transport Policy Institute, 2011). Stockholm and Singapores later success is also largely

attributed to the hourly toll variations that help to manage associated demand volumes. Fixed

price congestion schemes such as Londons are not optimal because they do not coerce any peak

spreading effects, although they do reduce traffic demand volumes to a certain degree.

Conclusion

Congestion pricing techniques have proven to be effective in mitigating the growing

demand volumes in urban areas. Traffic congestion is increasing worldwide and has significant

economic impacts associated with its uncontrolled growth. There are various congestion pricing

strategies that can be implemented for varying circumstances and area-wide tolling is only one

measure that has proven to mitigate congestion. Pricing schemes alter driver behaviors to help

reduce and spread demand during the most critical times of day, reduce costs associated with

17

delay, benefit business operations and provide a source of revenue to municipalities. Although

public acceptability is often initially low, there has been much success once the schemes have

been implemented and drivers realize the systems benefits. Congestion pricing measures along

with other congestion management techniques can provide effective demand management and

ensure a sound foundation for the future of urban travel.

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References

2012, December. "Urban Mobility Report." (2012): 6. Texas Transportation Institute. Web. 20

Mar. 2015.

Bhatt, Kiran, Thomas Higgins, and John T. Berg. Lessons Learned from International

Experience in Congestion Pricing. Washington, DC: U.S. Federal Highway

Administration, 2008. Federal Highway Administration, Aug. 2008. Web. 10 Apr.

2015.

Booz, Allen, Hamilton, and Seattle DOT. Seattle Variable Tolling Study. Rep. N.p.: n.p., 2009.

Web. 20 Mar. 2015.

Brown, Ken, Anthony Damiano, Brian Jenkins, and Michelle Tisdale. "Congestion Pricing, A

Primer: Overview." The Journal of Structured Finance 6.4 (2001): 21-28.

Ops.fwha.dot.gov. Federal Highway Administration. Web. 1 Mar. 2015.

California Transportation Commission. "Cordon Pricing." (n.d.): n. pag. CA.gov. California

Transportation Commission. Web. 15 Mar. 2015.

.

Centre for Economics and Business Research, 4. "The Future Economic and Environmental

Costs of Gridlock in 2030." The Future Economic and Environmental Costs of

Gridlock in 2030 (2014): n. pag. CEBR, July 2014. Web. 10 Apr. 2015.

"Chapter 1 Hot Lane Concept and Rationale." Chapter 1: HOT Lane Concept and Rationale.

Federal Highway Administration, 2000. Web. 5 Apr. 2015.

Congress of the United States Congressional Budget Office. "Using Pricing to Reduce Traffic

Congestion." (2009): 5. CBO.gov. 2009. Web. 23 Mar. 2015.

19

Danish Architecture Center. "Singapore: The World's First Digital Congestion Charging

System." DAC.dk. N.p., 21 Jan. 2014. Web. 26 Apr. 2015.

De Palma, Andre, and Robin Lindsey. "Traffic Congestion Pricing Methodologies and

Technologies." Transportation Research Part C 19.6 (2011): 1377+. Dec. 2011. Web.

25 Mar. 2015.

Eliasson, Jonas, Lars Hultkrantz, Lena Nerhagen, and Lena S. Rosqvist. "The Stockholm

Congestion Charging Trial 2006: Overview of Effects ." Transportation Research

Part A 43.3 (2009): 240-50. The Stockholm Congestion Charging Trial 2006:

Overview of Effects. Mar. 2009. Web. 26 Apr. 2015.

Goodwin, Phil. "The Economic Costs of Road Traffic Congestion." 268.6942 (1956): 578.

University College London, May 2004. Web. 3 Apr. 2015.

Hamilton, Carl J. "Decisive Factors for the Acceptability of Congestion Pricing." (2012): n. pag.

Center for Transport Studies Stockholm, 2012. Web. 1 Apr. 2015.

Hensher, David A., and Sean M. Puckett. "Congestion and Variable User Charging as an

Effective Travel Demand Management Instrument." Transportation Research Part A

41.7 (2007): 616. Congestion and Variable User Charging as an Effective Travel

Demand Management Instrument. Aug. 2007. Web. 1 Apr. 2015.

Jonathan Leape. "The London Congestion Charge." Journal of Economic PerspectivesVolume

20, Number 4 Fall 2006 Pages 157176 The London Congestion Charge 20.4

(n.d.): 157-76. Fall 2006. Web. 26 Apr. 2015.

Litman, Todd. "London Congestion Pricing: Implications for Other Cities." (2005): n. pag. Mar.

2005. Web.

20

Olszewski, Piotr, and Litian Xie. "Modelling the Effects of Road Pricing on Traffic in

Singapore." Transportation Research Part A 39.7-9 (2005): 755-72. Modelling the

Effects of Road Pricing on Traffic in Singapore. Fall 2005. Web. 3 Apr. 2015.

Quinnipiac University. "Voters Back Congestion Pricing, If Funds Go To Transit." Quinnipiac

University, 13 Mar. 2008. Web. 27 Apr. 2015.

Schaller, Bruce. "New York Citys Congestion Pricing Experience and Implications for Road

Pricing Acceptance in the United States." Transport Policy 17.4 (2010): 266-73. New

York City's Congestion Pricing Experience and Implications for Road Pricing

Acceptance in the United States. Aug. 2010. Web. 20 Apr. 2015.

Schrank, David, and Tim Lomax. "2009 Urban Mobility Report." (2009): n. pag.

Mobility.tamu/edu. Texas Transportation Institute, July 2009. Web. 5 Apr. 2015.

Seattle DOT. "7 Best Practices, Congestion Pricing." Seattle.gov. Seattle Department of

Transportation, n.d. Web. 1 Apr. 2015.

Seng, Lim T. "Area Licensing Scheme." Singapore Infopedia. N.p., 15 Aug. 2014. Web. 20 Mar.

2015.

Small, Kenneti B , boe Kptf B p n f{- cb o ez. Road Pricing for Congestion Management: The

Transition from Theory to Policy. Berkeley: U of California Transportation Center,

1998. Web. 20 Mar. 2015.

Texas Transportation Institute. "Variable Pricing." (n.d.): n. pag. Www.mobility.tamu.edu. Texas

Transportation Institute. Web. 5 Apr. 2015.

Transport for London, Contents. "Impacts Monitering." (2004): n. pag. TRFL.gov.uk. Apr. 2004.

Web. 20 Apr. 2015.

21

Transportation Research Board. "Assessing Highway Tolling and Pricing Options and Impacts."

NCHRP Report 722 1 (2012): 9. Www.TRB.org. Transportation Research Board, 2012.

Web. 1 Apr. 2015.

USDOT. "Traffic Congestion and Reliability: Linking Solutions to Problems." Traffic

Congestion and Reliability: Linking Solutions to Problems. Federal Highway

Administration, 5 Dec. 2013. Web. 26 Mar. 2015.

Victoria Transport Policy Institute. "London Congestion Pricing: Implications for Other Cities."

Urban Research & Practice 5.2 (2011): 1-13. Victoria Transport Policy Institute. 24

Nov. 2011. Web. 20 Apr. 2015.

Victoria Transport Policy Institute, S. "Transportation Cos and Benefit Analysis II." (2013): 5.5-

14. VTPI.org. 28 Aug. 2013. Web. 5 Apr. 2015.

Wolff, Carolyn, and Pierre Vilain. "EVALUATING CONGESTION PRICING IMPACTS

UNDER PEAK SPREADING." (2007): 5. Www.trforum.org. Transportation Research

Forum, 2007. Web. 20 Apr. 2015.

Yong-Phang, Sock, and Rex S. Toh. "Road Congestion Pricing In Singapore: 1975 to 2003."

Transportation Journal (2004): 16-25. 1 Apr. 2004. Web. 26 Apr. 2015.