Transportation Elasticities

How Prices and Other Factors Affect Travel Behavior

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TDM Encyclopedia

Victoria Transport Policy Institute

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Updated 23 March 2016


This chapter investigates the influence that prices and other user costs (such as travel time) have on travel behavior. It summarizes research on various types of transportation elasticities and describes how to use this information to predict the travel impacts of specific TDM strategies. For more information on this subject see the report “Transportation Elasticities” at www.vtpi.org/elasticities.pdf.

 

 

Contents

Introduction.. 2

Factors Affecting Price Sensitivity. 4

Type of Price Change. 4

Type of Trip and Traveler 5

Quality And Price Of Alternative Routes, Modes And Destinations. 5

Scale and Scope of Pricing. 5

Time Period. 5

Transportation Elasticity Estimates. 6

Summaries. 6

Individual Elasticities. 7

Vehicle Operating (Out-of-Pocket) Expenses. 7

Parking Price. 8

Fuel Consumption With Respect to Fuel Price. 10

Vehicle Travel With Respect to Fuel Price. 12

Road Pricing and Tolls. 13

Mileage and Emission Charges. 14

Generalized Costs. 15

Travel Time. 15

Vehicle Price and Income. 16

Transit Elasticities. 16

Vanpool Elasticity Studies. 18

Cross Elasticities. 18

Transit Service. 19

Transit Elasticities Summary. 19

Taxi Service Elasticities. 20

Commute Trip Reduction Programs. 20

Mode Shifts. 21

Freight Elasticities. 23

Related Chapters and Resources. 23

References and Resources for More Information.. 23

 

 

Introduction

Life is full of trade-offs. People must constantly decide how to spend the limited amounts of money and time they have available. The choices that people make when faced with these trade-offs reflects their knowledge, preferences and values.

 

This chapter describes techniques used to define and quantify these trade-offs, which can evaluate Travel Demands and help predict how various types of changes to the transportation system are likely to affect travel behavior. This information can be very helpful in evaluating potential TDM strategies, particularly those that involve changes in consumers’ transportation Options and Prices.

 

Prices are the direct- internal-variable-perceived costs involved in consuming a good, that is, the factors that directly affect decisions by individual people and organizations (called firms) concerning what goods and services to consume. The term is sometimes limited to monetary costs, but it can include non-monetary costs such as time, discomfort and risk. For example, the price of an airplane trip includes the financial cost of the ticket, expenses for getting to the airport, plus the time and risk of travel. Factors such as discomfort and risk can be considered to affect Travel Time Costs: a minute spent by travelers in comfort and safe conditions imposes less cost to consumers than the same minute spent in uncomfortable or unsafe conditions.

 

Price changes often affect consumption decisions. For example, you may consider a particular product too expensive at its regular price, but you buy it when it is discounted. Similarly, a price increase may motivate you to use less of a product or shift to another brand.  Such decisions are said to be marginal, that is, the decision is at the margin between different alternatives, and may therefore be affected by a small price change. Although individually these decisions may be quite variable and difficult to predict (you might succumb to a sale one day, but forego the same offer the next day), in aggregate they tend to follow a predictable pattern: when the price of a good declines its consumption increases, and when a good’s price increases its consumption declines. This is called the “law of demand.”

 

Transportation activities tend to follow this pattern. When the monetary, time, discomfort or risk costs of travel decline, the amount of mobility (measured in trips, person-miles or ton-miles) tends to increase. When costs increase, mobility declines. Price changes can have a variety of impacts on travel, affecting the number of trips people take, their destination, route, mode, travel time, type of vehicle (including size, fuel efficiency and fuel type), parking location and duration, and which type of transport services they choose (Institute for Transport Studies, 2004).

 

Economists measure price sensitivity using elasticities, defined as the percentage change in consumption of a good caused by a one-percent change in its price or other characteristics (such as traffic speed or road capacity). For example, an elasticity of -0.5 for vehicle use with respect to vehicle operating expenses means that each 1% increase in these expenses results in a 0.5% reduction in vehicle mileage or trips. Similarly, a transit service elasticity is defined as the percentage change in transit ridership resulting from each 1% change in transit service, such as bus-miles or frequency. A negative sign indicates that the effect operates in the opposite direction from the cause (an increase in price causes a reduction in travel). Elasticities can be calculated based on ratios, rather than absolute price values, such as the ratio between transit fares and automobile operating costs, or vehicle costs as a percentage of average income or wages.

 

Several methods are used to compute elasticities, some more accurate than others. These methods and their application are described in detail, along with examples, in Pratt (2003), Appendix A, “Elasticity Discussion and Formulae” and in TRL, 2004. The most frequently used form of elasticity in transportation analyses is the arc elasticity. An arc elasticity reflects the change in consumption that results from each 1% change in price, calculated in infinitesimally small increments. Measured in this way, a large price change consists of numerous small incremental changes. For example, a –0.5 price elasticity applied to a 10% price increase can be calculated as ten 0.5% reductions in consumption (e.g., trips taken, miles driven, fuel consumed, etc.). The first reduces current consumption by 0.5% to 99.5%, the second reduces this by another 0.5%, which is reduced by another 0.5% in the third step, and so on a total of ten times.

 

Cross-elasticities refer to the percentage change in the consumption of a good resulting from a price change in another, related good. For example, automobile travel is complementary to vehicle parking, and a substitute for transit travel. As a result, an increase in the price of driving tends to reduce demand for parking and increase demand for transit travel. To help analyze cross-elasticities it is useful to estimate mode substitution factors, such as the change in automobile trips resulting from a change in transit trips. These factors vary depending on circumstances. For example, when bus ridership increases due to reduced fares, typically 10-50% of the added trips will substitute for an automobile trip, that is, one automobile trip is reduced for each two to ten additional transit trips. Other trips will shift from nonmotorized modes, ridesharing (which consists of vehicle trips that will be made anyway), or be induced travel (including chauffeured automobile travel, in which a driver makes a special trip to carry a passenger). Conversely, when a disincentive such as parking fees or road tolls causes automobile trips to decline, generally 20-60% shift to transit, depending on conditions. Pratt (1999) provides information on the mode shifts that result from various incentives, such as transit service improvements and parking pricing.

 

The steepness of this curve indicates how sensitive (or “elastic”) a particular good is with respect to price. A high elasticity (i.e., a gradual curve) indicates that a relatively small price change causes a relatively large change in consumption. A low price elasticity (i.e., a steep curve) indicates that price changes have relatively little impact on consumption.

 

Elasticity analysis is normally based on “real” (inflation adjusted) prices, as opposed to “nominal” or “current” prices (unadjusted for inflation). For example, if during a time period there is 10% inflation and nominal prices do not change, real prices will have declined by 10%. If during that time period prices increase by 10%, real prices will have stayed constant. If nominal prices increase 20% during that period, real prices will have increased by approximately 10%.

 

Although elasticities are often reported as single, point estimates, there are actually many factors that can affect the price sensitivity of a particular good. In other words, elasticities are actually functions with several possible variables, including the type of market, type of consumer and time period. For example, although the elasticity of vehicle travel with respect to fuel price may be defined as –0.3 (a single value), the actual value will vary between –0.1 and –0.8 depending on the type of trip (commercial, commute, recreational, etc.), the type of motorist (rich, poor, young, old, etc.), travel conditions (rural, urban, peak, off-peak), and the time period being considered (short-, medium- or long-run). Some of these variables are discussed in more detail in the next section.

 

 

Factors Affecting Price Sensitivity

Various factors described below can affect how much a change in prices impacts travel activity.

 

Type of Price Change

Different types of charges can have different impacts on travel behavior. Fixed vehicle purchase and registration fees can affect the number and type of vehicles purchased. Fuel prices and emission fees affect the type of vehicle used. A road toll may shift some trips to other routes and destinations, while congestion pricing (a time-variable fee, higher during congested periods) may shift travel times, as well as changing mode and the total number of trips that occur. These impacts depend on the specific type of pricing – for example, an increase in residential parking fees is most likely to affect vehicle ownership, and a time-variable parking fee can affect when trips occur.

 

Table 1                        Impacts of Different Types of Pricing

Type of Impacts

Vehicle Fees

Fuel Price

Fixed Toll

Congestion Pricing

Parking Fee

Transit Fares

Vehicle ownership. Consumers change the number of vehicles they own.

X

 

 

 

X

X

Vehicle type. Motorist chooses different vehicle (more fuel efficient, alternative fuel, etc.)

X

X

 

 

 

 

Route Change. Traveler shifts travel route.

 

 

X

X

X

 

Time Change. Motorist shifts trip to off-peak periods.

 

 

 

X

X

 

Mode Shift. Traveler shifts to another mode.

 

X

X

X

X

X

Destination Change. Motorist shifts trip to alternative destination.

 

X

X

X

X

X

Trip Generation. People take fewer total trips (including consolidating trips).

 

X

X

X

X

 

Land use changes. Changes in location decisions, such as where to live and work.

 

 

X

 

X

X

 

 

Type of Trip and Traveler

Commute trips tend to be less elastic than shopping or recreational trips. Weekday trips may have very different elasticities than weekend trips. Urban peak-period trips tend to be price inelastic because congestion discourages lower-value trips, leaving only higher-value automobile trips. Travelers with higher incomes tend to be less price sensitive than lower-income travelers. Travelers on business tend to be less price sensitive than people traveling for personal activities.

 

Quality And Price Of Alternative Routes, Modes And Destinations.

Price sensitivity tends to increase if alternative routes, modes and destinations are good quality and affordable. For example, highway tolls tend to be more price sensitive if there is a parallel untolled roadway. Driving is less price sensitive in automobile-dependent areas where transportation alternatives are inferior (e.g., walking, cycling and transit are poor substitutes for driving).

 

Scale and Scope of Pricing

In general, narrowly defined transport is more elastic than broadly defined transport, because consumers have more alternatives. For example, demand for peak-period automobile travel on a certain road is usually more elastic than for total personal travel along a corridor, since a higher price for driving at a particular time at a particular road may shift travel to alternative routes, destinations, modes and travel times.

 

Most individual price components of driving (fuel, parking, tolls) are considered inelastic because they each represent a small portion of users total costs. But driving is actually quite elastic with respect to total costs. For example, since fuel is only about 15% of total vehicle costs (purchase, insurance, maintenance and repairs, parking, etc.), a -0.2 elasticity of driving with respect to fuel price represents an elasticity of -1.3 with respect to total financial cost. This implies that if all user costs were converted into variable charges, each 1% increase in this charge would reduce driving by -1.3%.

 

Time Period

Transportation elasticities tend to increase over time as consumers have more opportunities to take prices into effect when making long-term decisions. For example, if consumers anticipate low automobile use prices they are more likely to choose an automobile dependent suburban home, but if they anticipate significant increases in driving costs they might place a greater premium on having alternatives, such as access to transit and shops within convenient walking distance. These long-term decisions affect the options that are available. For example, if consumers are in the habit of shopping in their neighborhood, local stores will be successful. But if they always shop at large supermarkets, the quantity and quality of local stores will decline. For this reason, it may take many years for the full effect of a price change to be felt. Studies cited by Button (1993, p. 41) estimate that short-term elasticities are typically one-third of long-term elasticities. Short run is typically less than two years, medium run is two to 15 years, and long run is 15 years or more, although definitions vary.

 

 

Transportation Elasticity Estimates

This section summarizes the results of many transportation elasticity studies. Lipow (2008) provides an excellent summary of elasticity studies. The BTE Transport Elasticities Database Online (www.bitre.gov.au/tedb) is an excellent resource for international literature on transportation elasticities, containing (in 2001) approximately 200 separate bibliographic references and 400 table entries.

 

Summaries

The table below summarize some transport elasticity studies.

 

Table 2                        Estimated Long Run Transportation Elasticities (Johansson & Schipper, 1997, p. 209)

Estimated Component

 

Fuel Price

 

Income

Taxation

(Other than Fuel)

Population Density

Car Stock

(vehicle ownership)

-0.20 to 0.0

(-0.1)

0.75 to 1.25 (1.0)

-0.08 to -0.04

(-0.06)

-0.7 to -0.2

(-0.4)

Mean Fuel Intensity (fuel efficiency)

-0.45 to -0.35

(-0.4)

-0.6 to 0.0

(0.0)

-0.12 to -0.10

(-0.11)

-0.3 to -0.1

(-0.2)

Mean Driving Distance

(per car per year)

-0.35 to -0.05

(-0.2)

-0.1 to 0.35 (0.2)

0.04 to 0.12

(0.06)

-0.75 to 0.0

(-0.4)

 

Car Fuel Demand

-1.0 to -0.40

(-0.7)

0.05 to 1.6

(1.2)

-0.16 to -0.02

(-0.11)

-1.75 to -0.3

(-1.0)

 

Car Travel Demand

-0.55 to -0.05

(-0.3)

0.65 to 1.25 (1.2)

-0.04 to 0.08

(0.0)

-1.45 to -0.2

(-0.8)

These show the range of elasticities from various studies. Numbers in parenthesis indicate the original authors’ “best guess” values.

 

 

After a detailed review of international studies, Goodwin, Dargay and Hanly (2003) produced the average elasticity values summarized in Table 3.

 

Table 3            Overall results: Elasticities of Various Measures of Travel Demand (Goodwin, Dargay and Hanly, 2003)

Dependent Variable

Short term

Long term

Fuel consumption (total)

Mean elasticity

Standard deviation

Range

Number of estimates

 

-0.25

0.15

-0.01, -0.57

46

 

-0.64

0.44

0, -1.81

51

Fuel consumption (per vehicle)

Mean elasticity

Standard deviation

Range

Number of estimates

 

-.08

N/A

-.08, -.08

1

 

-1.1

N/A

-1.1, -1.1

1

Vehicle kilometres (total)

Mean elasticity

Standard deviation

Range

Number of estimates

 

-0.10

0.06

-0.17, -0.05

3

 

-0.29

0.29

-0.63, -0.10

3

Vehicle kilometres (per vehicle)

Mean elasticity

Standard deviation

Range

Number of estimates

 

-0.10

0.06

-0.14, -0.06

2

 

-0.30

0.23

-0.55, -0.11

3

Vehicle stock

Mean elasticity

Standard deviation

Range

Number of estimates

 

-0.08

0.06

-0.21, -0.02

8

 

-0.25

0.17

-0.63, -0.10

8

 

 

They conclude that:

·      Fuel consumption elasticities are greater than traffic elasticities, mostly by factors of 1.5 to 2.

·      Long run elasticities are greater than short run, mostly by factors of 2 to 3.

·      Income elasticities are greater than price, mostly by factors of 1.5 to 3.

 

They conclude that if the real (inflation adjusted) price of fuel rises by 10% and stays at that level, the result is a dynamic process of adjustment such that the following occur:

·         Volume of traffic will fall by about 1% within about a year, building up to a reduction of about 3% in the longer run (about 5 years or so).

·         Volume of fuel consumed will fall by about 2.5% within a year, building up to a reduction of over 6% in the longer run.

·         Efficiency of the use of fuel rises by about 1.5% within a year, and around 4% in the longer run.

·         Total number of vehicles owned falls by less than 1% in the short run, and by 2.5% in the longer run.

 

 

Individual Elasticities

Various types of transport elasticities are discussed below.

 

Vehicle Operating (Out-of-Pocket) Expenses

This refers to the travel effects of vehicle operating expenses (i.e., variable monetary costs), including fuel, parking fees and road tolls. Button estimates the elasticity of driving with respect to out-of-pocket costs for various trips, shown in Table 4. Oum, Waters, and Yong (1992) estimate the elasticity of vehicle travel with respect to price is -0.23 in the short run and -0.28 in the long run. Oum, Van Ooststroom and Yoon (1996) found the elasticity of automobile travel in the Netherlands to range between -0.02 and -0.28. De Borger, et al, (1997) find elasticities for urban peak travel in Belgium to be -0.384 for automobile and -0.35 for public transit, with higher values for off-peak travel.

 

Table 4                        Elasticity Estimates for Various Trip Types (Button, 1993)

 

Trip Type

Elasticity of Road Travel with Respect to Out of Pocket Expenses

Urban shopping

-2.7 to -3.2

Urban commuting

-0.3 to - 2.9

Inter-urban business

-0.7 to -2.9

Inter-urban leisure

-0.6 to -2.1

 

 

Parking Price

Motorists appear to be particularly sensitive to parking prices because it is such a direct charge (Parking Evaluation). Compared with other out-of-pocket expenses, parking fees are found to have a greater effect on vehicle trips, typically by a factor of 1.5 to 2.0 (USEPA, 1998). For example, a $1.00 per trip parking charge is likely to cause the same reduction in vehicle travel as a fuel price increase that averages $1.50 to $2.00 per trip.

 

Several studies (Vaca and Kuzmyak 2005; Vaca and Kuzmyak, 2005) provide detailed reviews of parking price elasticities. Kuzmyak, Weinberger and Levinson (2003) describe how parking supply affects parking and travel demand, but this may actually reflect price impacts (reduced parking supply increases prices). These studies indicate that the elasticity of vehicle trips with regard to parking prices is typically in the –0.1 to –0.3 range, with significant variation depending on demographic, geographic, travel choice and trip characteristics. Pratt (1999, p. 13-40) finds significantly higher elasticities (-0.9 to -1.2) of parking price with regard to commercial parking gross revenues, since motorists can respond to higher prices by reducing their parking duration or changing to cheaper locations and times, as well as reducing total vehicle trips. Similarly, in a study of downtown parking meter price increases, Clinch and Kelly (2003) find that the elasticity of parking frequency is smaller (–0.11) than the elasticity of vehicle duration (-0.20), indicating that some motorists respond to higher fees by reducing how long they stay.

 

TRACE (1999) provides detailed estimates of the elasticity of various types of travel (car-trips, car-kilometers, transit travel, walking/cycling, commuting, business trips, etc.) with respect to parking price under various conditions (e.g., level of vehicle ownership and transit use, type of trip, etc.). The table below summarizes long-term elasticities for relatively automobile-oriented urban regions.

 

Table 5            Parking Price Elasticities (TRACE, 1999, Tables 32 & 33)

Term/Purpose

Car Driver

Car Passenger

Public Transport

Slow Modes

Trips

 

 

 

 

Commuting

-0.08

+0.02

+0.02

+0.02

Business

-0.02

+0.01

+0.01

+0.01

Education

-0.10

+0.00

+0.00

+0.00

Other

-0.30

+0.04

+0.04

+0.05

Total

-0.16

+0.03

+0.02

+0.03

Kilometres

 

 

 

 

Commuting

-0.04

+0.01

+0.01

+0.02

Business

-0.03

+0.01

+0.00

+0.01

Education

-0.02

+0.00

+0.00

+0.00

Other

-0.15

+0.03

+0.02

+0.05

Total

-0.07

+0.02

+0.01

+0.03

Slow Modes = Walking and Cycling                  WRT = With Respect To

 

 

Hess (2001) assesses the effect of free parking on commuter mode choice and parking demand in Portland’s (Oregon) CBD. He finds that with free parking, 62% of commuters will drive alone, 16% will commute in carpools and 22% will ride transit; with a $6.00 daily parking charge 46% will drive alone, 4% will ride in carpools and 50% will ride transit. The $6.00 parking charge results in 21 fewer cars driven for every 100 commuters, a daily reduction of 147 VMT per 100 commuters and an annual reduction of 39,000 VMT per 100 commuters. The Trip Reduction Tables predict travel reductions resulting from parking fees and other commuter financial incentives (Comsis Corporation, 1993).

 

Hensher and King (2001) model the price elasticity of CBD parking, and predict how an increase in parking prices in one location will shift cars to park at other locations and drivers to public transit (Table 6). Harvey (1994) finds that airport parking prices range from -0.1 for less than a day to -2.0 for greater than 8 days.

 

Table 6            Parking Elasticities (Hensher and King, 2001, Table 6)

 

Preferred CBD

Less Preferred CBD

CBD Fringe

Car Trip, Preferred CBD

-0.541

0.205

0.035

Car Trip, Less Preferred CBD

0.837

-0.015

0.043

Car Trip, CBD Fringe

0.965

0.286

-0.476

Park & Ride

0.363

0.136

0.029

Ride Public Transit

0.291

0.104

0.023

Forego CBD Trip

0.469

0.150

0.029

This table shows elasticities and cross-elasticities for changes in parking prices at various Central Business District (CBD) locations. For example, a 10% increase in prices at preferred CBD parking locations will cause a 5.41% reduction in demand there, a 3.63% increase in Park & Ride trips, a 2.91% increase in Public Transit trips and a 4.69% reduction in total CBD trips.

 

 

The use of parking price elasticities can be confusing since most parking is currently free, so it is meaningless to measure a percentage increase from zero price. The table below summarizes the changes that occurred in commute mode at worksites that shifted from free to priced parking. Other case studies find similar impacts. Shifting from free to priced parking typically reduces drive alone commuting by 10-30%, particularly if implemented with improvements in transit service and rideshare programs and other TDM strategies.

 

Table 7            Changes in Workplace Travel Due to Parking Pricing

 

Canadian Study

Los Angeles Study

 

Before

After

Change

Before

After

Change

Drive Alone

35%

28%

-20%

55%

30%

-27%

Carpool

11%

10%

+9%

13%

45%

+246%

Transit

42%

49%

+17%

29%

22%

-24%

Other

12%

13%

-8%

3%

3%

0%

(Feeney, 1989, cited in Pratt, 1999)

 

 

Travel behavior can also be influenced by how parking prices are structured. Significant discounts for long-term parkers (e.g., lower-priced monthly leases) encourage use by commuters, while parking prices and management strategies that discount short-term parking (e.g., “First-Hour-Free” rates) favor shoppers and business trips. Rate increases of $1-2 per day directed at commuters are found to reduce long-term parking demand by 20-50%, although much of this may consist of shifts to other parking locations rather than alternative modes (Pratt, 1999).

 

 

Fuel Consumption With Respect to Fuel Price

Increased fuel prices cause a combination of reduced driving and increased fuel efficiency. Short-term fuel savings consist of reduced driving, and a shift toward using more fuel-efficient vehicles, particularly in multi-vehicle households where drivers have short-term choices (Institute for Transport Studies, 2004; Sterner, 2006).

 

Long-term fuel savings consist primarily of purchases of more fuel-efficient vehicles. Agras and Chapman (1999) using 1982-1995 U.S. data find that the short-run price elasticities of VMT and MPG with respect to fuel price are –0.15 and 0.12 respectively, summing to an overall short-run gasoline price elasticity of –0.25. Their long-run fuel price elasticities are –0.32 for VMT and 0.60 for MPG, which sum to an overall long-run gasoline price elasticity of –0.92. This means that a 10% fuel price increase is likely to reduce driving by 1.5% and improve fuel economy by 1.2% in the short-run, and over the long run mileage will decline by 3.2% and fuel efficiency will increase by 6%, leading to a 9.2% overall reduction in fuel consumption.

 

Glaister and Graham (2000) perform a comprehensive review of international studies of the effects of fuel price and income on vehicle travel and fuel consumption. They find short run elasticities range from –0.2 to –0.5, and long run elasticities range from –0.24 in the U.S. (with variations from –0.24 to –0.8) up to –1.35 in the OECD overall (with variations from –0.75 to –1.35). They identify a number of factors that tend to affect fuel price elasticity values, including the functional form, time span, geographic factors, and what other factors are included (such as vehicle ownership), and find that long-term gasoline demand appears to be getting more elastic. They conclude that short-run elasticities are –0.2 to –0.3, and long-run elasticities are –0.6 to –0.8. Summarizing international research, Goodwin (1992) estimates the price elasticity of gasoline to be -0.27 in the short run and -0.7 in the long run. He predicts that a 10% increase in vehicle fuel prices will have the following effects:

 

·         In the short run it reduces vehicle travel by about 1.5%, and reduces fuel consumption by 2.7%, due in part to more fuel efficient driving (such as shifting more driving to a household’s most fuel efficient car).

 

·         In the long run it reduces vehicle travel by 3-5%, split between a reduction in car ownership and per-vehicle use. It reduces petroleum consumption by 7% or more, due in part to the purchase of more fuel-efficient vehicles.

 

 

The Congressional Budget Office found that a 20% gasoline price increase reduces traffic volumes on highways with parallel rail transit service by 0.7% on weekdays and 0.2% on weekends, with comparable increases in transit ridership, but find no traffic reductions on highways that lack parallel rail service (CBO 2008). They also found that a $0.50 per gallon fuel price increase reduces median uncongested highway traffic speeds by about three-quarters of a mile-per-hour.

 

Dargay (1992) reports higher values averaging -0.67 when price increases and decreases are calculated separately. Sterner et al (1992) found that fuel elasticities are relatively high in North America, greater than -1.0. Dahl and Sterner (1991) estimate the elasticity of fuel consumption to be -0.18 in the short run and -1.0 in the long run. DeCicco and Gordon (1993) conclude that the medium-run elasticity of vehicle fuel in the U.S. is -0.3 to -0.5. Eltony (1993) finds that elasticity of fuel consumption in Canada is approximately -0.3 in the short term and rises to approximately 1.0 after a decade, as vehicle fleet efficiency increases.

 

Charles Komanoff’s Gasoline Price-Elasticity Spreadsheet (www.komanoff.net/oil_9_11/Gasoline_Price_Elasticity.xls) estimates the price-elasticity of demand for gasoline in the U.S. based on monthly fuel consumption and price data. The results as of early 2009 indicate a short-run elasticity of -0.12 and a long-run elasticity of -0.30.

 

Hagler Bailly (1999) conclude that the fuel price elasticity for gasoline is –0.15 in the short run and –0.6 in the long run, with separate estimates for air, freight and transit transport. Table 8 summarizes the price elasticities of various types of transportation fuel. Using 1980-2000 U.S. data, Zupan (2001) finds little relationship between fuel price and VMT in the short-term, but a relationship is found if price changes are evaluated with a 6-month lag, indicating that approximately 25% of VMT changes can be accounted for by fuel price.

 

Table 8            Estimated Fuel Price Elasticities (Hagler Bailly, 1999)

 

Short Run Elasticity

Long Run Elasticity

 

 

Low

Base

High

Low

Base

High

Road Gasoline

-0.10

-0.15

-0.20

-0.40

-0.60

-0.80

Road Diesel - Truck

-0.05

-0.10

-0.15

-0.20

-0.40

-0.60

Road Diesel - Bus

-0.05

-0.10

-0.15

-0.20

-0.30

-0.45

Road Propane

-0.10

-0.15

-0.20

-0.40

-0.60

-0.80

Road CNG

-0.10

-0.15

-0.20

-0.40

-0.60

-0.80

Rail Diesel

-0.05

-0.10

-0.15

-0.15

-0.40

-0.80

Aviation Turbo

-0.05

-0.10

-0.15

-0.20

-0.30

-0.45

Aviation Gasoline

-0.10

-0.15

-0.20

-0.20

-0.30

-0.45

Marine Diesel

-0.02

-0.05

-0.10

-0.20

-0.30

-0.45

This table summarizes changes in consumption that are likely to result from changes in price for various types of vehicle fuel.

 

 

Vehicle Travel With Respect to Fuel Price

As fuel costs per vehicle-mile decline (taking into account inflation-adjusted fuel prices and vehicle fuel economy), average annual vehicle mileage tends to increase, as illustrated in Figure 1.

 

Figure 1          Fuel Costs Versus Annual Vehicle Mileage (BTS 2001)

This figure illustrates the relationship between real (inflation-adjusted) per-mile fuel costs and average annual vehicle-mileage, based on 1960-2000 US data. As per-mile costs decline, mileage tends to increase. To obtain an Excel spreadsheet with the source data of this graph, click here: FuelTrends

 

 

As mentioned above, Agras and Chapman (1999) find that the short-run price elasticity of VMT with respect to fuel price is –0.15, and the long-run fuel price elasticity is –0.32. Similarly, Glaister and Graham (2000) conclude that the elasticity of vehicle travel with respect to fuel price is –0.15 in the short run and –0.3 over the long-run. This means that a 10% fuel price increase is likely to reduce driving by 1.5% in the short-run, and 3% over the long-run. Schimek (1997) finds the elasticity of vehicle travel with respect to fuel price in the U.S. to be -0.26. These results are consistent with international research (Johansson and Schipper, 1997).

 

One study finds that a $0.40 to $2.00 increase in fuel prices would reduce Puget Sound region vehicle trips by 1.2-6.7%, and vehicle mileage by 1.4-7.2% (PSRC 1994). INFRAS (2000) cites estimates of the long-term elasticity of vehicle use with respect to fuel price to typically average about –0.3. Some U.S. studies of fuel price and consumption patterns during the 1990s, when real fuel prices declined and real incomes increased, found low price responses (Hughes, Knittel and Sperling 2006; Small and Van Dender 2007), but more recent research indicates more normal elasticities (Williams Derry 2008; Gillingham 2010). INRIX (2008) evaluated the effects of fuel price increases on U.S. vehicle travel and traffic congestion, using the "Smart Dust Network" of GPS-enabled vehicles which report roadway travel conditions. The results indicate that increased gas prices in the first half of 2008 significantly reduced VMT and highway traffic congestion.

 

 

Road Pricing and Tolls

Road Pricing means that motorists pay a toll for using a particular roadway or driving in a particular area. There is growing interest in Congestion Pricing, which refers to tolls that are higher during peak periods and lower during off-peak periods in order to reduce traffic congestion. NCHRP (2006) and Spears, Boarnet and Handy (2010) provide good summaries of recent road pricing experience and the resulting travel impacts, particularly in North America. 

 

Matas and Raymond (2003) summarize previous estimates of toll road elasticities, and develop a model of toll road demand using data from toll roads in Spain, 1980-1998. They find that demand varies depending on several factors, including economic activity (GDP), tourist activity, fuel prices, and travel conditions on parallel roads. Short-term toll road price elasticities range from -0.21 to -0.83, a somewhat higher and broader range than indicated in previous studies. They find that elasticities are greater where there are uncongested parallel roads.

 

Since February 2003 a congestion pricing fee (initially £5 and raised to £8 in 2005) has been charged for driving in downtown London during weekdays, which reduced private automobile traffic in the area by 38% and total vehicle traffic (including buses, taxis, and trucks) by 18%, a greater reduction than planners predicted indicating a higher price elasticity than economists expect (Litman 2003).

 

Holguín-Veras, Ozbay and de Cerreño (2005) investigated the response of automobile and truck travel to E-ZPass tolls, which provide discounts for off-peak travel. The results indicate modest shift from peak to off-peak periods. The car short-term elasticities range between –0.31 and –1.97 for weekday and –0.55 and –1.68 for weekends depending on the time of the day.

 

Arentze, Hofman and Timmermans (2004) used a public survey to determine traveler response to congestion pricing incentives. They found that for commute trips, route and departure time changes are most likely to occur, while shifts to public transit and working at home are less likely. For non-commute trips, shifts to cycling also occur. This study indicates the price elasticity of overall vehicle travel is -0.13 to -0.19, and -0.35 to -0.39 for a particular congested road that is priced, taking into account shifts in route and time. A CA$5.00 (US$3.00) per round-trip road toll is predicted to reduce automobile commuting by 25%, and a CA$5.00 parking fee would reduce automobile commuting by 20%.

 

Luk (1999) estimates that toll elasticities in Singapore are –0.19 to –0.58, with an average of –0.34. Singapore may be unique, because car ownership is restricted to higher-income residents which tend to make travel less sensitive to price, but this may be offset by the city’s excellent public transit service, which may make car travel more price sensitive than other cities.

 

 

Mileage and Emission Charges

Harvey and Deakin (1998) model the effect of a 2¢ per vehicle-mile fee on transportation impacts in four major urban regions in California. Table 9 summarizes their results for the year 2010. It indicates, for example, that in the South Coast (Los Angeles) region, a 2¢ per mile fee would reduce total vehicle trips by 4.1%, but congestion delay would decline by 10.5%. INFRAS (2000) estimates kilometer fees have elasticities of –0.1 to –0.8, depending on the trip purpose, mode and price level.

 

Table 9            Impacts of 2¢ Per Mile Fee, Year 2010 (Harvey and Deakin, 1998, Table B.9)

Region

VMT

Trips

Delay

Fuel

ROG

Revenue

Bay Area

-3.9%

-3.7%

-9.0%

-4.1%

-3.8%

$1,122

Sacramento

-4.4%

-4.1%

-7.5%

-4.4%

-4.3%

$349

San Diego

-4.2%

-4.0%

-8.5%

-4.2%

-4.1%

$629

South Coast

-4.3%

-4.1%

-10.5%

-5.2%

-4.2%

$3,144

VMT = change in total vehicle mileage. Trips = change in total vehicle trips. Delay = change in congestion delay. Fuel = change in fuel consumption. ROG = a criteria air pollutant. Revenue = annual revenue in millions of 1991 U.S. dollars. See report for additional notes and data.

 

 

In an experiment involving time- and mileage-based pricing of 20 volunteer motorists, with base fees averaging 6.4 Euro per trip which is reduced for shorter and off-peak trips, O'Mahony, Geraghty and Humphreys (2000) found that participants reduced peak period trips by 21.6%, and total trips by 5.7%, peak mileage by 24.8% and total mileage by 12.4%.

 

 

Generalized Costs

Transport modelers have developed various “generalized cost” coefficients that include combined values of travel time, vehicle costs, toll prices, fuel taxes, transit fares, and parking prices. These are usually determined empirically for a specific community based on local travel behavior and user survey data. A typical value is –0.5 (NHI, 1995). Booz, Allen, Hamilton (2003) estimate the generalized cost of travel in the Canberra, Australia region to be –0.87 for peak, -1.18 for off-peak, and –1.02 overall (peak and off-peak combined). TRL (2004) calculates generalized cost elasticities of –0.4 to –1.7 for urban bus transit, -1.85 for London underground, and -0.6 to –2.0 for rail transport.

 

Lee (2000) estimates the elasticity of vehicle travel with respect to Total Price (including fuel, vehicle wear and mileage-related ownership costs, tolls, parking fees and travel time, which is equivalent to generalized costs) is –0.5 to –1.0 in the short run, and –1.0 to –2.0 over the long run.

 

 

Travel Time

A study by leading U.K. transportation economists concludes that the elasticity of travel volume with respect to travel time is -0.5 in the short term and -1.0 over the long term (SACTRA, 1994). This means that reducing travel time on a roadway by 20% typically increases traffic volumes by 10% in the short term and 20% over the long term. Another study found the elasticity values for vehicle travel with respect to travel time shown in Table 10. Pratt (1999) estimates the effects of service speed, frequency and reliability on public transit use, including the effects of HOV facilities.

 

Table 10          Elasticity of Vehicle Travel With Respect to Travel Time (Goodwin, 1996)

 

Short Run

Long Run

Urban Roads

-0.27

-0.57

Rural Roads

-0.67

-1.33

 

 

TRACE (1999) provides detailed estimates of the elasticity of various types of travel (car-trips, car-kilometers, transit travel, walking/cycling, commuting, business trips, etc.) with respect to car travel times under various conditions (e.g., level of vehicle ownership and transit use, type of trip, etc.). Table 11 summarizes long-term car travel time elasticities of kilometers traveled in areas with high vehicle ownership (more than 450 vehicles per 1,000 population). Booz, Allen, Hamilton (2003) estimate travel time costs, including separate values for car and bus in-vehicle time, and for walking and waiting times for transit travel.

 

Table 11          Long Term Elasticities Of Kilometres With Respect to Car Travel Time (TRACE, 1999)

Term/Purpose

Car Driver

Car Passenger

Public Transport

Slow Modes

Commuting

-0.96

-1.02

+0.70

+0.50

Business

-0.12

-2.37

+1.05

+0.94

Education

-0.78

-0.25

+0.03

+0.03

Other

-0.83

-0.52

+0.27

+0.21

Total

-0.76

-0.60

+0.39

+0.19

 

 

Vehicle Price and Income

A number of studies have examined how vehicle ownership and use are affected by price and income (Jansson, 1989; Golob, 1989). The elasticity of vehicle ownership with respect to price is estimated to be -0.4 to -1.0, meaning that a 10% increase in total vehicle costs reduces vehicle ownership by 4-10%. This is based on various studies, including analysis by Goodwin, Dargay and Hanly (2003) showing that a 10% increase in fuel prices reduces vehicle ownership 1.0 in the short-run and 2.5% over the long-run, and fuel represents about 25% of total vehicle costs. Glaister and Graham (2000) conclude that the long-run elasticity of vehicle fuel consumption with respect to income is 1.1 to 1.3, and the long-run elasticity of vehicle travel with respect to income is 1.1 to 1.8, with lower short-run values. 

 

Generally, as people become wealthier vehicle ownership increases, but at a declining rate (Schafer and Victor, 2000). Kopits and Cropper (2003) find that vehicle ownership nearly levels off at about $16,000 (2003 dollars) per capita annual income, and some researchers suggest that above a certain level (estimated at $21,000 U.S. by Talukdar), automobile ownership levels may even decline slightly (Newman and Kenworthy, 1998). Karlaftis and Golias (2002) find that the purchase of a household’s first vehicle is primarily dependent on socioeconomic factors (as income increases, so does the ownership of a vehicle), but the purchase of second and third vehicles is primarily dependent on the quality of travel alternatives (walking and transit service) in their community (if urban driving is faster and cheaper than transit, households will tend to own more automobiles).

 

 

Transit Elasticities

Several publications have summarized public transit elasticity estimates, including Pham and Linsalata (1991); Oum, Waters, and Yong (1992); Goodwin (1992); Luk and Hepburn (1993); Pratt (1999); Dargay and Hanly (1999), TRACE (1999), Nash (2002), Booz Allen Hamilton (2003), Wardman and Shires (2003), and TRL (2004).

 

Table 12 shows transit fare elasticity values published by the American Public Transportation Association, and widely used for transit planning and modeling in North America. This was based on a study of the short-term (less than two years) effects of fare changes in 52 U.S. transit systems during the late 1980s.

 

Table 12          Bus Fare Elasticities  (Pham and Linsalata, 1991).

 

Large Cities (More than One Million Population)

Smaller Cities (Less than One Million Population)

Average for All Hours

-0.36

-0.43

Peak Hour

-0.18

-0.27

Off-Peak

-0.39

-0.46

Off-peak Average

-0.42

Peak Hour Average

-0.23

This table summarizes U.S. transit fare elasticities published by the American Public Transit Association, which are widely used in North America.

 

 

Based on extensive research, TRL (2004) calculates that bus fare elasticities average around –0.4 in the short-run, -0.56 in the medium run, and 1.0 over the long run, while metro rail fare elasticities are –0.3 in the short run and –0.6 in the long run. Bus fare elasticities are lower (-0.24) during peak than off-peak (-0.51). The table below summarizes transit elasticity estimates, based on a review of previous studies.

 

Table 13          Factors Affecting Transit Ridership (Kain & Liu, 1999)

Factor

Elasticity

Regional employment

0.25

Central city population

0.61

Service (transit vehicle miles)

0.71

Fare price

-0.32

This table shows the elasticity of transit use with respect to various factors. For example, a 1% increase in regional employment is likely to increase transit ridership by 0.25%, while a 1% increase in fare prices will reduce ridership by 0.32%, all else being equal.

 

 

Several TDM strategies involve transit fare reductions. Commuter Transit Benefit programs, in which employers encourage and sometimes subsidize transit passes, are effective at increasing ridership (Commuter Check, www.commutercheck.com). Deep Discount transit passes can encourage occasional riders to use transit more frequently (Oram and Stark, 1996), and if implemented when fares are increasing, can avoid ridership losses. Many Campus Transport Management programs include free or discounted transit fares. Not all increased transit travel that results from fare discounts represents a reduction in automobile travel. A portion represents shifts from walking, cycling and ridesharing, or absolute increases in personal travel.

 

 

Vanpool Elasticity Studies

Analysis by Wambalaba, Concas and Chavarria (2004) indicates that the parameter of vanpool ridership with respect to fees is -2.6% to –0.148, which indicates that a one dollar decrease in vanpool price is associated with a 2.6% to 14.8% increase in the predicted odds of choosing vanpool with respect to drive alone. York and Fabricatore (2001) estimate the price elasticity of vanpooling at about 1.5, meaning that a 10% reduction in vanpool fares increases ridership by about 15%. For example, if vanpool fares that are currently $50 per month are reduced to $40 (a 20% reduction), ridership is likely to increase by about 30% (20% x 1.5).

 

 

Cross Elasticities

Cross-elasticity refers to the changes in demand for a good that results from a change in the price of a substitute good. This includes changes in automobile travel due to transit fare changes, changes in transit ridership due to changes in automobile operating costs, and changes in one type of transit (such as bus) in response to price changes in another type of transit (such as rail). Lago et al. (1992) found the mean cross-elasticity of auto travel demand with respect to bus fares is 0.09 (±0.07), and 0.08 (±0.03) with respect to rail fares. Hensher developed a model of elasticities and cross-elasticities between various forms of transit and car use, illustrated in Table 14.

 

Table 14          Direct and Cross-Share Elasticities (Hensher, 1997, Table 8)

 

Train

Train

Train

Bus

Bus

Bus

Car

 

Single Fare

Ten Fare

Pass

Single Fare

Ten Fare

Pass

 

Train, single fare

-0.218

0.001

0.001

0.057

0.005

0.005

0.196

Train, ten fare

0.001

-0.093

0.001

0.001

0.001

0.006

0.092

Train, pass

0.001

0.001

-0.196

0.001

0.012

0.001

0.335

Bus, single fare

0.067

0.001

0.001

-0.357

0.001

0.001

0.116

Bus, ten fare

0.020

0.004

0.002

0.001

-0.160

0.001

0.121

Bus, pass

0.007

0.036

0.001

0.001

0.001

-0.098

0.020

Car

0.053

0.042

0.003

0.066

0.016

0.003

-0.197

This table indicates how various changes in transit fares and car operating costs affects transit and car travel demand. For example, a 10% increase in single fare train tickets will cause a 2.18 reduction in the sale of those fares, and a 0.57% increase in single fare bus tickets. This is based on a survey of residents of Newcastle, a small Australian city.

 

 

TRACE (1999) provides detailed estimates of transit ridership with respect to fuel and parking prices for various types of travel and conditions, based on numerous European studies (see data in sections on fuel and parking price elasticities). It estimates that a 10% rise in fuel prices increases transit ridership 1.6% in the short run and 1.2% over the long run, although this varies by regional per capita automobile ownership (this declining elasticity value is unique to fuel, due to motorists purchasing more efficient vehicles when fuel prices increase).

 

Several studies indicate that parking prices (and probably road tolls) tend to have a greater impact on transit ridership than other vehicle costs, such as fuel, typically by a factor of 1.5 to 2.0, because they are paid directly on a per-trip basis. Hensher and King (1998) calculate elasticities and cross-elasticities for various forms of transit fares and automobile travel in the Sydney, Australia city center.

 

 

Transit Service

Service elasticity refers to how much transit ridership increases (decreases) in response to an increase (reduction) in transit vehicle-mileage, vehicle-hours or frequency. Of course, many factors affect service elasticities, including demographic factors (i.e., the portion of the population that is transit dependent or lower-income), geographic factors (i.e., population density, employment density and pedestrian accessibility), service quality (i.e., speed, comfort and schedule information) and fare price. New transit quality of service indices that better account for these factors may be used in the future to better define transit service elasticity factors (Transit Evaluation).

 

Evans (2004) provides information on the effects of various types of service improvements on transit ridership. The elasticity of transit use to service expansion (e.g. routes into new parts of a community) is typically in the range of 0.6 to 1.0, meaning that each 1% of additional service (measured in vehicle-miles or vehicle-hours of service) increases ridership by 0.6-1.0%, although much lower and higher response rates are also found (from less than 0.3 to more than 1.0). The elasticity of transit use with respect to transit service frequency (called a headway elasticity) averages 0.5. There is a wide variation in these factors, depending on the type of service, demographic and geographic factors. Higher service elasticities often occur with new express transit service, in university towns, and in suburbs with rail transit stations to feed. It usually takes 1 to 3 years for ridership on new routes to reach its full potential. Completely new bus service in a community that previously had no public transit service typically achieves 3 to 5 annual rides per capita, with 0.8 to 1.2 passengers per bus mile.

 

 

Transit Elasticities Summary

No single transit elasticity value applies in all situations: various factors affect price sensitivities including type of user and trip, geographic conditions and time period. Transit dependent people are generally less price sensitive and discretionary riders more price sensitive. As per capita wealth, drivers, vehicles and transport options increase, transit elasticities are likely to increase.

 

Commonly used transit elasticity values are based on studies performed 10-30 years ago, when real incomes where lower and a greater portion of the population was transit dependent. These studies primarily reflect short-term impacts. The resulting elasticity values are probably lower than what would accurately predict medium and long-term changes under current conditions in most North American urban areas. Although residents of Canadian cities are somewhat more transit dependent than residents of comparable size U.S. cities, virtually all other factors identified in this research tend to increase transit elasticities relative to the standard values.

 

 

Taxi Service Elasticities

Schaller (1999) finds that in New York City, the elasticity of taxi demand with respect to fares is –0.22, the elasticity of service availability with respect to fares is 0.28, and the elasticity of service availability with respect to total supply of service is 1.0. Based on these values he concludes that fare increases tend to increase total industry revenues and service availability, and that the number of taxi licenses can often be expanded without reducing the revenue of existing operators.

 

 

Commute Trip Reduction Programs

Models are now available which can predict the travel impacts of a specific Commute Trip Reduction program, taking into account the type of program and worksite. These include the CUTR_AVR Model (www.cutr.usf.edu/tdm/download.htm), the Business Benefits Calculator (BBC) (www.commuterchoice.gov) and the Commuter Choice Decision Support Tool (www.ops.fhwa.dot.gov/PrimerDSS/index.htm).   

 

The Trip Reduction Tables provide more information on the impacts that financial incentives can have on commute travel under various circumstances. The two tables below are examples. They show the effects of transit and HOV financial subsidies for various worksite settings, taking into account location (suburban, activity center, central business district [CBD]), and whether carpooling or transit are favored as alternative modes. For example, Table 37 indicates that a $1 (in 1993 U.S. dollars) per day transit subsidy provided to employees at a transit-oriented activity center is likely to result in a 10.9% reduction in commute trips, while in a rideshare-oriented Central Business District, the same subsidy would only cause a 4.7% trip reduction.

 

Table 15          Percent Vehicle Trips Reduced by Daily Transit Subsidy (Comsis Corporation, 1993)

Worksite Setting

$0.50

$1

$2

$4

Low density suburb, rideshare oriented

0.1

0.2

0.6

1.9

Low density suburb, mode neutral

1.5

3.3

7.9

21.7

Low density suburb, transit oriented

2.0

4.2

9.9

23.2

Activity center, rideshare oriented

1.1

2.4

5.8

16.5

Activity center, mode neutral

3.4

7.3

16.4

38.7

Activity center, transit oriented

5.2

10.9

23.5

49.7

Regional CBD/Corridor, rideshare oriented

2.2

4.7

10.9

28.3

Regional CBD/Corridor, mode neutral

6.2

12.9

26.9

54.3

Regional CBD/Corridor, transit oriented

9.1

18.1

35.5

64.0

This table can be used to predict how transit subsidies are likely to affect automobile commute trips. See Trip Reduction Tables for more information.

 

 

Shoup (1997) found that solo driving declined from 76% to 63% after Parking Cash Out was introduced, representing a 17% reduction in vehicle trips. Figure 2 illustrates before and after commute mode. Travel impacts tend to increase over time: one employer that offered Parking Cash Out for three years found that solo commuting continued to decline each year, as more employees found opportunities to reduce their driving. Transit voucher programs typically shift 20 percentage points of recipients’ commute travel from auto to transit (Oram Associates, 1995; Schwenk, 1995).

 

Figure 2          Cashing Out Impacts on Commute Mode (Shoup, 1997)

 

This figure illustrates the effects Parking Cash Out had on commute mode choice.

 

 

Travel impacts are affected by the magnitude of the benefit and the quality of travel choices. Mode shifts tend to be greatest if current transit use is low. In New York City, where transit commute rates are already high, transit benefits only increased transit use by 16% to 23%, while in Philadelphia, transit commuting increased 32% among recipients (Schwenk, 1995). Similarly, only 30% of employees who received transit benefits who work in San Francisco increased their transit use, while 44% of those in other parts of the region commuted by transit more (Oram Associates, 1995). These probably represent the lower range of mode shifts since they are marketed primarily as an employee benefit and are therefore most attractive to firms with high current levels of transit commuting.

 

 

Mode Shifts

Increases in vehicle operating charges (fuel, parking, tolls, etc.) tend to reduce vehicle use, as described in the previous sections of this chapter. Some of this travel simply disappears, due to fewer and shorter trips, and more use of mobility alternatives such as telework and delivery services. A portion of reduced automobile use consists of shifts to other travel modes.

 

Which changes occur depends on specific conditions, such as the type of trip, the travel route, the quality of travel alternatives, the type of traveler, etc. In general, a larger share of shorter distance, non-work trips shift to walking and cycling, while a larger share of longer distance trips shift to transit (particularly for urban destinations) and ridesharing (particularly for suburban commutes). A disincentive to driving (say, higher parking fees or a road toll in urban areas) generally causes 20-60% of automobile trips to shift to transit, while other trips will shift to nonmotorized modes, ridesharing, or be avoided altogether when travelers consolidate errands or shift destinations. Conversely, when transit service is improved, typically 20-80% of the added trips will substitute for automobile trips, with higher shifts for longer-distance commute trips. Other new transit passengers will consist of people who would have walked or shared a car ride, gone to a different destination, or not traveled at all for that trip. Pratt (1999) and Kuzmyak, Weinberger and Levinson (2003) provide information on the mode shifts that result from various incentives, such as transit service improvements. Table 16 provides one example. Also see Pratt, Table 10-22 and Kuzmyak, Weinberger and Levinson, Table 18-34.

 

Table 16          Mode Shifts By New Transit Users (Pratt, 1999, Table 9-10)

Riders Attracted By Increased Bus Frequency

Riders Attracted By Increased Commuter Rail Frequency

Prior Mode

Percentage

Prior Mode

Percentage

Own Car

18-67%

Own Car

64%

Carpool

11-29%

Carpool

17%

Train

0-11%

Bus

19%

Taxi

0-7%

 

 

Walking

0-11%

 

 

 

 

A survey by Mackett (2001) of UK residents evaluated the potential of shifting short trips (less than 8 kms) from driving to alternative modes. Survey respondents indicated that 31% to short vehicle trips could be shifted to bus, 31% to walking, 7% to bicycle and 3% to taxi; respondents often indicated more than one possible alternative mode for particular trips. The TravelSmart program in the city of Perth, Australia used TDM Marketing and a variety of incentives to encourage residents to use alternative travel modes. The goal of the program is to encourage residents to increase the portion of total trips made by environmentally friendly modes (walking, cycling and public transit) from 10% to 25% of trips by 2029. This goal is considered feasible, based on detailed market research and transportation surveys. Before-and-after surveys of pilot projects found the following results (Transport WA, 2001):

 

Trips By                       Change

Car-as-driver                      Down 14%

Public transit                      Up 17%

Cycling                                  Up 61%

Walking                                Up 35%

Car mileage                        Down 17%

 

 

Freight Elasticities

The price elasticity of freight transport (measured in ton-miles) in Denmark is calculated to be –0.47, while the elasticity of freight traffic (measured in truck-kilometers) is –0.81, and the elasticity of freight energy consumption is only about –0.1 according to a study by Thomas Bue Bjørner (1999). A 10% increase in shipping costs reduces truck traffic by 8%, but total shipping volume by only 5%. Some freight is shifted to rail, while other freight is shipped using existing truck capacity more efficiently. Hagler Bailly (1999) estimate the long-run price elasticity of rail and truck freight transport at –0.4, with a wide range depending on the type of freight. Small and Winston summarize various estimates of freight elasticities, as summarized in the table below.

 

Table 17          Freight Transport Elasticities (Small & Winston, 1999, Table 2-2)

 

Rail

Truck

Aggregate Mode Split Model, Price

-0.25 to –0.35

-0.25 to –0.35

Aggregate Mode Split Model, Transit Time

-0.3 to –0.7

-0.3 to –0.7

Aggregate Model from Tanslog Cost Function, Price

-0.37 to –1.16

-0.58 to –1.81

Disaggragate Mode Choice Model, Price

-0.08 to -2.68

-0.04 to –2.97

Disaggragate Mode Choice Model, Transit Time

-0.07 to –2.33

-0.15 to –0.69

These elasticities vary depending on commodity group.

 

 

Related Chapters and Resources

For more information on issues related to price effects see Transportation Costs, Transportation Statistics, Evaluating TDM, Measuring Transportation, TDM Planning, Comprehensive TDM Evaluation, Market Principles and Evaluating Pricing Strategies. For more information on this subject see the comprehensive report Transportation Elasticities at www.vtpi.org/elasticities.pdf.

 

Wit and Humor

A man walks into a doctor’s office with a huge wart shaped like a frog growing on top of his forehead. The doctor says, “Good grief sir, let me remove that awful-looking growth from your scalp!”

To which the frog replied, “My head feels fine, but this wart on my butt is killing me!”

 

 

References and Resources for More Information

For more information see “Transportation Elasticities” at www.vtpi.org/elasticities.pdf. Good summaries of transportation elasticities are available in Johansson and Schipper (1997); the BTE Transport Elasticities Database; Goodwin, Dargay and Hanly (2004); Louis Berger Group (2004); Pratt (2004); Spears, Boarnet and Handy 2010; and TRACE (1999).

 

J. Agras and D. Chapman (1999), “The Kyoto Protocol, CAFE Standards, and Gasoline Taxes,” Contemporary Economic Policy, 17:3; cited in Oregon Dept. of Transportation, Policy Section, “Policy Notes: A Brief Reference on Fuel Costs and Fuel Efficiency,” Vol. 5, Issue 1, Jan. 2001, at www.odot.state.or.us/tdb/policy/Policy_Notes/jan2001.pdf

 

Apogee Research (1997), Guidance on the Use of Market Mechanisms to Reduce Transportation Emissions, USEPA (www.epa.gov/omswww/market.htm).

 

Theo Arentze, Frank Hofman and Harry Timmermans (2004), “Predicting Multi-Faceted Activity-Travel  Adjustment Strategies In Response To Possible Congestion Pricing Scenarios Using An Internet-Based Stated Adaptation Experiment,” Transport Policy, Vol. 11, No. 1 (www.elsevier.com/locate/tranpol), Jan. 2004, pp. 31-41.

 

Philippe Barla, Nathanaël Lapierre, Ricardo Alvarez Daziano and Markus Herrmann (2012), Reducing Automobile Dependency on Campus : Evaluating the Impact of TDM using Stated Preferences, Working paper 2012-3, CREATE, University of Laval (www.create.ulaval.ca); at www.create.ulaval.ca/uploads/tx_centrerecherche/CREATE2012-3.pdf.

 

Thomas Bue Bjørner (1999), “Environmental Benefits from Better Freight Transport Management: Freight Traffic in a VAR Model,” Transportation Research D, Vol. 4, No. 1, January 1999, pp. 45-64.

 

Booz Allen Hamilton (2003), ACT Transport Demand Elasticities Study, Canberra Department of Urban Services (www.actpla.act.gov.au/plandev/transport/ACTElasticityStudy_FinalReport.pdf).

 

George Bresson, Joyce Dargay, Jean-Loup Madre and Alain Pirotte (2004), “Economic And Structural Determinants Of The Demand For Public Transport: An Analysis On A Panel Of French Urban Areas Using Shrinkage Estimators,” Transportation Research A, Vol. 38, Issue 4 (www.elsevier.com/locate/tra), May 2004, pp. 269-285.

 

BTE, Transport Elasticities Database Online (www.bitre.gov.au/tedb) contains approximately 200 separate bibliographic references and 400 table entries, from international literature on transportation elasticities.

 

BTS (2001), National Transportation Statistics, Bureau of Transportation Statistics (www.bts.gov).

 

Ralph Buehler (2010), “Transport Policies, Automobile Use, and Sustainable Transport: A Comparison of Germany and the United States,” Journal of Planning Education and Research, Vol. 30/1, pp. 76-93 (http://jpe.sagepub.com/content/30/1/76); presentation at www.ciens.no/data/no_NO/file/5411.pdf.

 

Kenneth Button (1993), Transport Economics, Second Edition, Edward Elgar (Aldershot).

 

CBO (2008), Effects of Gasoline Prices on Driving Behavior and Vehicle Markets, Congressional Budget Office (www.cbo.gov); at www.cbo.gov/ftpdocs/88xx/doc8893/01-14-GasolinePrices.pdf.

 

Centre for Data and Analysis in Transportation (www.cdat.ecn.ulaval.ca) is dedicated to improving knowledge about energy use in the Canadian transportation sector in order to better understand the factors affecting energy efficiency and consumption in this sector, and the economic and environmental impacts of transportation policies.

 

Peter J. Clinch and Andrew Kelly (2003), Temporal Variance Of Revealed Preference On-Street Parking Price Elasticity, Department of Environmental Studies, University College Dublin (www.environmentaleconomics.net).

 

Comsis Corporation (1993), Implementing Effective Travel Demand Management Measures: Inventory of Measures and Synthesis of Experience, USDOT and Institute of Transportation Engineers (www.ite.org); at www.bts.gov/ntl/DOCS/474.html.

 

Joe Cortright (2008), Driven to the Brink: How the Gas Price Spike Popped the Housing Bubble and Devalued the Suburbs, CEOs for Cities (www.ceosforcities.org); at www.ceosforcities.org/newsroom/pr/files/Driven%20to%20the%20Brink%20FINAL.pdf.

 

Carol Dahl and Thomas Sterner (1999), “Analyzing Gasoline Demand Elasticities: A Survey,” Energy Economics, Vol. 13, July 1991, pp. 203-210.

 

J.M. Dargay and P. B. Goodwin (1995), “Evaluation of Consumer Surplus with Dynamic Demand,” Journal of Transport Economics and Policy, Vol. 29, No. 2, May 1995, pp. 179-193.

 

Joyce Dargay and Dermot Gately (1997), “Demand for Transportation Fuels: Imperfect Price-Reversibility?,” Transportation Research B, Vol. 31, No. 1, 1997, pp. 71-82.

 

Joyce Dargay (1992), “Demand Elasticities,” Journal of Transport Economics,” January 1992.

 

Joyce Dargay and Mark Hanly (1999), Bus Fare Elasticities, ESRC Transport Studies Unit

University College London, for UK Department of the Environment, Transport and the Regions (www.ucl.ac.uk).

 

Joyce Dargay, Mark Hanly, G. Bresson, M. Boulahbal, J.L. Madre and A. Pirotte (2002), The Main Determinants of the Demand for Public Transit: A Comparative Analysis of Great Britain and France, ESRC Transport Studies Unit, University College London (www.ucl.ac.uk).

 

Marc D. Davidson, Ron C.N. Wit, Jos M.W. Dings (2003), Meeting External Costs in the Aviation Industry, Commission for Integrated Transport (www.cfit.gov.uk).
 

Elizabeth Deakin and Greig Harvey (1996), Transportation Pricing Strategies for California, California Air Resources Board (Sacramento), Contract No. 92-316. The same analysis is described in USEPA, 1998.

 

B. DeBorger, et al (1997), “Alternative Travel Pricing and Regulatory Policies,” Transportation Research D, Vol. 2, No. 3, Sept. 1997, pp. 17-198.

 

John DeCicco and Deborah Gordon (1993), Steering with Prices: Fuel and Vehicle Taxation and Market Incentives for Higher Fuel Economy, American Council for an Energy Efficient Economy (www.aceee.org).

 

DFT (2006), Transport Analysis Guidance, Integrated Transport Economics and Appraisal, Department for Transport (www.webtag.org.uk/index.htm). This website provides comprehensive guidance on how to identify problems, establish objectives, develop potential solutions, create a transport model for the appraisal of the alternative solutions, how to model highway and public transport, and how to conduct economic appraisal studies that meet DoT requirements.

 

Gerard de Jong and Hugh 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, May 2001, pp. 137-160.

 

DKS Associates (2003), Modeling TDM Effectiveness, Washington Department of Transportation (www.wsdot.wa.gov/Mobility/TDM/520casev1/execsummary.pdf).

 

M. Nagy Eltony (1993), “Transport Gasoline Demand in Canada,” Journal of Transport Economics and Policy, May 1993, pp. 193-208.

 

Molly Espey (1996), “Explaining The Variation In Elasticity Estimates Of Gasoline Demand In The United States: A Meta-Analysis,” Energy Journal, Vol. 17, No. 3, pp. 49-60; at www.allbusiness.com/marketing-advertising/567873-1.html.

 

John E. Evans (2004), “Transit Scheduling and Frequency,” Traveler Response to Transportation System Changes, TCRP Report 95, Chapter 9, TRB (www.trb.org); at http://gulliver.trb.org/publications/tcrp/tcrp_rpt_95c9.pdf.

 

FTA (2002), Transit Performance Monitoring System (TPMS) Results, American Public Transit Association (www.apta.com) and the Federal Transit Administration (www.fta.dot.gov); at www.fta.dot.gov/transit_data_info/reports_publications/reports/16031_ENG_HTML.htm.

 

Stephen Glaister and Dan Graham (2000), The Effect of Fuel Prices on Motorists, AA Motoring Policy Unit (www.theaa.com) and the UK Petroleum Industry Association (http://195.167.162.28/policyviews/pdf/effect_fuel_prices.pdf).

 

Stephen Glaister and Dan Graham (2002), “The Demand for Automobile Fuel: A Survey of Elasticities,” Journal of Transport Economics and Policy, Vol. 36, No. 1, pp. 1-25; at www.ingentaconnect.com/content/lse/jtep/2002/00000036/00000001/art00001.

 

Thomas F. Golob (1989), “The Casual Influences of Income and Car Ownership on Trip Generation by Mode”, Journal of Transportation Economics and Policy, May 1989, pp. 141-162.

 

Phil Goodwin (1992), “Review of New Demand Elasticities With Special Reference to Short and Long Run Effects of Price Changes,” Journal of Transport Economics, Vol. 26, No. 2, May 1992, pp. 155-171; at www.bath.ac.uk/e-journals/jtep/pdf/Volume_XXV1_No_2_155-169.pdf.

 

Phil Goodwin (1996), “Empirical Evidence on Induced Traffic,” Transportation, Vol. 23, No. 1, pp. 35-54.

 

Phil Goodwin, Joyce Dargay and Mark Hanly (2003), Elasticities Of Road Traffic And Fuel Consumption With Respect To Price And Income: A Review, ESRC Transport Studies Unit, University College London (www.transport.ucl.ac.uk), commissioned by the UK Department of the Environment, Transport and the Regions (now UK Department for Transport).

 

Phil Goodwin, Joyce Dargay and Mark Hanly (2004), “Elasticities of Road Traffic and Fuel Consumption With Respect to Price and Income: A Review,” Transport Reviews (www.tandf.co.uk), Vol. 24, No. 3, May 2004, pp. 275-292; at http://economics.about.com/gi/dynamic/offsite.htm?zi=1/XJ&sdn=economics&zu=http%3A%2F%2Fwww.cts.ucl.ac.uk%2Ftsu%2Fpapers%2Ftransprev243.pdf.

 

Kenneth Gillingham (2010), Identifying the Elasticity of Driving: Evidence from a Gasoline Price Shock in California, Stanford University (www.stanford.edu); at www.stanford.edu/~kgilling/Gillingham_IdentifyingElasticityofDriving.pdf.

 

Kenneth Gillingham (2010), How Do Consumers Respond to Gasoline Price Shocks? Heterogeneity in Vehicle Choice and Driving Behavior, Stanford University (www.stanford.edu); at http://www.stanford.edu/~kgilling/Gillingham_JMP.pdf.

 

Hagler Bailly (1999), Potential for Fuel Taxes to Reduce Greenhouse Gas Emissions from Transport, Transportation Table of the Canadian National Climate Change Process (www.tc.gc.ca/Envaffairs/subgroups1/fuel_tax/study1/final_Report/Final_Report.htm). 

 

Mark Hanly, Joyce Dargay and Phil Goodwin (2002), Review of Income and Price Elasticities in the Demand for Road Traffic, ESRC Transport Studies Unit, University College London (www.cts.ucl.ac.uk/tsu/elasfinweb.pdf).

 

Greig Harvey (1994), “Transportation Pricing and Travel Behavior,” Curbing Gridlock, National Academy Press, TRB (www.trb.org), 1994, pp. 89-114.

 

Greig Harvey and Elizabeth Deakin (1998), “The STEP Analysis Package: Description and Application Examples,” Appendix B, in USEPA, Technical Methods for Analyzing Pricing Measures to Reduce Transportation Emissions, USEPA Report #231-R-98-006, (www.epa.gov/clariton).

 

David A. Hensher (1997), Establishing a Fare Elasticity Regime for Urban Passenger Transport: Non-Concession Commuters, Working Paper, ITS-WP-97-6, Institute of Transport Studies, University of Sydney, Sydney.

 

David Hensher and Jenny King (1998), “Establishing Fare Elasticity Regimes for Urban Passenger Transport: Time-Based Fares for Concession and Non-Concession Market Segmented by Trip Length,” Journal of Transportation Statistics, Vol. 1, No. 1, January 1998, pp.43-57.

 

David A. Hensher and Jenny King (2001), “Parking Demand and Responsiveness to Supply, Price and Location in Sydney Central Business District,” Transportation Research A, Vol. 35, No. 3 (www.elsevier.com/locate/tra), March 2001, pp. 177-196.

 

Daniel B. Hess (2001), The Effects of Free Parking on Commuter Mode Choice: Evidence from Travel Diary Data, Lewis Center for Public Policy Studies, UCLA (www.sppsr.ucla.edu/lewis/WorkingPapers.html).

 

José Holguín-Veras, Kaan Ozbay and Allison de Cerreño (2005), Evaluation Study of Port Authority of New York and New Jersey's Time Of Day Pricing Initiative, Rensselaer Polytechnic Institute, for the New Jersey Department of Transportation (www.rpi.edu/~holguj2/PA/index.html).

 

Jonathan E. Hughes, Christopher R. Knittel and Daniel Sperling (2006), Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand, National Bureau of Economic Research, Working Paper No. 12530 (http://papers.nber.org/papers/W12530).

 

Kent M. Hymel, Kenneth A. Small and Kurt Van Dender (2010), “Induced Demand And Rebound Effects In Road Transport,” Transportation Research B (www.elsevier.com/locate/trb), Vol. 44, No. 10, December, pp. 1220-1241; summary at www.socsci.uci.edu/~ksmall/Rebound_congestion_27.pdf.

 

ICF (1997), Opportunities to Improve Air Quality Through Transportation Pricing, Office of Mobile Sources, EPA (www.epa.gov/omswww/market).

 

INFRAS (2000), Variabilisation and Differentiation Strategies in Road Taxation; Theoretical and Empirical Analysis European Conference of Ministers of Transport (www1.oecd.org/cem/online/pubpdf.htm), OECD (www.oecd.org).

 

INRIX (2008), The Impact of Fuel Prices on Consumer Behavior and Traffic Congestion, INRIX (http://scorecard.inrix.com/scorecard).

 

Institute for Transport Studies (2004), Fuel Taxes First Principles Assessment, KonSULT, Institute for Transport Studies, University of Leeds, (www.socscinet.com/transport/konsult/private/level2/instruments/instrument022/l2_022b.htm).

 

J.O. Jansson (1989), “Car Demand Modeling and Forecasting,” Journal of Transport Economics and Policy, May 1989, pp. 125-129.

 

Olof Johansson and Lee Schipper (1997), “Measuring the Long-Run Fuel Demand for Cars,” Journal of Transport Economics and Policy, Vol. 31, No. 3, 1997, pp. 277-292.

 

John F. Kain and Zvi Liu (1999), “Secrets of Success,” Transportation Research A, Vol. 33, No. 7/8, Sept./Nov. 1999, pp. 601-624.

 

Matthew Karlaftis and John Golias (2002), “Automobile Ownership, Households Without Automobiles, and Urban Traffic Parameters: Are They Related,” Transportation Research 1792, Transportation Research Board (www.trb.org), pp. 29-35.

 

Charles Komanoff (2008), Gasoline Price-Elasticity Spreadsheet, Komanoff  Energy Consulting (www.komanoff.net/oil_9_11/Gasoline_Price_Elasticity.xls). This spreadsheet uses U.S. gasoline price and consumption data from 2004 through the most recent available monthly data to estimate the short-term price-elasticity of demand. The analysis indicates that a 10% fuel price increase reduces short-term fuel consumption by 1-2%.

 

Elizabeth Kopits and Maureen Cropper (2003), Traffic Fatalities and Economic Growth, World Bank Research Working Paper 3035 (www.worldbank.org); at http://econ.worldbank.org/files/25935_wps3035.pdf.

 

Richard J. Kuzmyak, Rachel Weinberger and Herbert S. Levinson (2003), Parking Management and Supply: Traveler Response to Transport System Changes, Chapter 18, Report 95, Transit Cooperative Research Program; Transportation Research Board (www.trb.org).

 

Armando Lago, Patrick Mayworm and Jonathan McEnroe (1992), “Transit Ridership Responsiveness to Fare Changes, Traffic Quarterly, Vol. 35, No. 1, January 1992.

 

Douglass Lee (2000), “Demand Elasticities for Highway Travel,” HERS Technical Documents, FHWA (www.fhwa.dot.gov).

 

Louis Berger Group (2004), “Land Use Characteristics And Travel Variable Elasticities,” Emissions Benefits of Land Use Planning Strategies, Federal Highway Administration, USDOT (www.fhwa.dot.gov/environment/conformity/benefits/index.htm).

 

Todd Litman (2003), London Congestion Pricing: Implications for Other Cities, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/london.pdf.

 

Todd Litman (2004), “Transit Price Elasticities and Cross-Elasticities,” Journal of Public Transportation, Vol. 7, No. 2, (www.nctr.usf.edu/jpt/pdf/JPT 7-2 Litman.pdf), pp. 37-58; at www.vtpi.org/tranelas.pdf.

 

Todd Litman (2005), “Efficient Vehicles Versus Efficient Transportation: Comparing Transportation Energy Conservation Strategies,” Transport Policy, Volume 12, Issue 2, March 2005, Pages 121-129; at www.vtpi.org/cafe.pdf.

 

Todd Litman (2008), Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/elasticities.pdf.

 

Todd Litman (2006), Socially Optimal Transport Prices and Markets, VTPI (www.vtpi.org); at www.vtpi.org/sotpm.pdf.

 

Todd Litman (2012), “Changing North American Vehicle-Travel Price Sensitivities: Implications For Transport and Energy Policy,” Transport Policy, (http://dx.doi.org/10.1016/j.tranpol.2012.06.010); full report at  www.vtpi.org/VMT_Elasticities.pdf.

 

Todd Litman (2013), Transport Elasticities: Impacts on Travel Behaviour: Understanding Transport Demand To Support Sustainable Travel Behavior, Technical Document #11, Sustainable Urban Transport Project (www.sutp.org) and GIZ (www.giz.de); at www.sutp.org/index.php/en-dn-tp

 

Gar W. Lipow (2008), Price-Elasticity of Energy Demand: A Bibliography, Carbon Tax Center (www.carbontax.org); at www.carbontax.org/wp-content/uploads/2007/10/elasticity_biblio_lipow.doc.

 

James Luk and Stephen Hepburn (1993), New Review of Australian Travel Demand Elasticities, Australian Road Research Board (Victoria).

 

J.Y.K. Luk (1999), “Electronic Road Pricing in Singapore, Road & Transport Research, Vo. 8, No. 4, Dec. 1999, pp. 28-30.

 

Roger L. Mackett (2001), “Polices to Attract Drivers Out of Their Cars For Short Trips” Transport Policy, Vol. 8, No. 4 (www.elsevier.com/locate/tranpol), Oct. 2001, pp. 295-306.

 

Anna Matas and Jose-Luis Raymond (2003), “Demand Elasticity on Tolled Motorways,” Journal of Transportation and Statistics, Vol. 6, No. 2/3 (www.bts.gov), pp. 91-108.

 

A.D. May and D.S. Milne (2000), “Effects of Alternative Road Pricing Systems on Network Performance,” Transportation Research A, Vol. 34, No. 6, August 2000, pp. 407-436.

 

Inge Mayeres (2000), “The Efficiency Effects of Transport Policies in the Presence of Externalities and Distortionary Taxes, Journal of Transport Economics and Policy, Vol. 34, Part 2, May 2000, pp. 233-260.

 

Brian E. McCollom and Richard Pratt (2004), Transit Pricing and Fares, Chapter 12, TCRP Report 95, Transit Cooperative Research Program, Transportation Research Board, Federal Transit Administration (www.trb.org/publications/tcrp/tcrp_rpt_95c12.pdf).

 

Chris Nash (2002), Passenger Demand Forecasting Handbook (PDFH), Passenger Demand Forecasting Council, UK Department For Transport (www.dft.gov.uk).

 

NCHRP (2006), Estimating Toll Road Demand and Revenue, NCHRP Synthesis 364, Transportation Research Board (www.trb.org); at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_364.pdf.

 

Peter Newman and Jeffrey Kenworthy (1998), Sustainability and Cities; Overcoming Automobile Dependency, Island Press (www.islandpress.org).

 

NHI (1995), Estimating the Impacts of Urban Transportation Alternatives, Participant’s Notebook, National Highway Institute, NHI Course #15257, Federal Highway Administration (www.fhwa.dot.gov).

 

Margaret O’Mahony, Dermot Geraghty and Ivor Humphreys (2000), “Distance and Time Based Road Pricing Trial in Dublin,” Transportation, Vol. 27, pp. 269-283.

 

Oram Associates (1995), Impact of the Bay Area Commuter Check Program: Results of 1994 Employee Survey, Metropolitan Transportation Commission (Oakland; www.commutercheck.com).

 

Richard Oram and Stephen Stark (1996), “Infrequent Riders: One Key to New Transit Ridership and Revenue,” Transportation Research Record 1521, TRB (www.trb.org), pp. 37-41.

 

Tae Hoon Oum, W.G. Waters II, and Jong-Say Yong (1992), “Concepts of Price Elasticities of Transport Demand and Recent Empirical Estimates, Journal of Transport Economics, May, pp. 139-154. This article is based on A Survey of Recent Estimates of Price Elasticities of Travel Demand, Policy Planning and Research, Working Papers, World Bank (www.worldbank.org/transport/publicat/inu-70.pdf), 1990.

 

T.H. Oum, H.P.C. Van Ooststroom, J.H. Yoon (1996), “The Structure of Travel Demands in the Netherlands,” International Journal of Transport Economics, Vol. 23, No. 1, February 1996, pp. 31-62.

 

PETS (2000), Pricing European Transport Systems; Final Report, Institute of Transport Studies, University of Leeds, European Transport Pricing Initiative (www.Transport-Pricing.Net), funded by the European Commission.

 

Larry Pham and Jim Linsalata (1991), Effects of Fare Changes on Bus Ridership, American Public Transit Association (www.apta.com); summary at www.apta.com/info/online/elastic.htm.

 

Richard H. Pratt (1999-2007), Traveler Response to Transportation System Changes, TCRP Report 95, TRB (www.trb.org); at www.trb.org/TRBNet/ProjectDisplay.asp?ProjectID=1034.

 

Richard H. Pratt (2003), “Interim Introduction,” Traveler Response to Transportation System Changes, Interim Handbook, TCRP Web Document 12 (http://trb.org/publications/tcrp/tcrp_rrd_61.pdf), 2003. For updated information see TCRP Report 95 Series (www.trb.org/trbnet/projectdisplay.asp?projectid=1033).

 

Richard Pratt and John Evans (2004), Bus Routing and Coverage: Traveler Response to Transport System Changes, Chapter 10; Report 95, Transit Cooperative Research Program; Transportation Research Board (www.trb.org); at http://gulliver.trb.org/publications/tcrp/tcrp_rpt_95c11.pdf.

 

Jolanda Prozzi, et al. (2009), Actual vs. Forecasted Toll Usage: A Case Study Review, Center for Transportation Research, The University of Texas at Austin (www.utexas.edu); at www.utexas.edu/research/ctr/pdf_reports/0_6044_1.pdf.

 

PSRC (1995), Update of the Metropolitan Transportation Plan for the Central Puget Sound Region, Puget Sound Regional Council, MTP17a, cited in ICF, 1997, p. 33.

 

SACTRA (1994), Trunk Roads and the Generation of Traffic, Standing Advisory Committee on Trunk Road Assessment, UKDoT, HMSO (www.roads.detr.gov.uk/roadnetwork).

 

Andreas Schafer and David G. Victor (2000), “The Future Mobility of the World Population,” Transportation Research A, Vol. 34, No. 3, April 2000, pp. 171-205.

 

Bruce Schaller (1999), “Elasticities for Taxi Cab Fares and Service Availability,” Transportation, Vol. 26, 1999, pp. 283-297.

 

Paul Schimek (1997), “Gasoline and Travel Demand Models Using Time Series and Cross-Section Data from the United States,” Transportation Research Record 1558, TRB (www.trb.org), pp. 83-89.

 

Yoram Shiftan (1999), “Responses to Parking Restrictions: Lessons from a Stated Preference Survey in Haifa and Their Policy Implications,” World Transport Policy And Practice, Vol. 5, No. 4 (www.eco-logica.co.uk/wtpp05.4.pdf), pp. 30-35.

 

Donald Shoup (1997), “Evaluating the Effects of California’s Parking Cash-out Law: Eight Case Studies,” Transport Policy, Vol. 4, No. 4,, pp. 201-216.

 

Kenneth Small and Clifford Winston (1999), “The Demand for Transportation: Models and Applications,” in Essays in Transportation Economics and Policy, Brookings Institute (www.brooking.edu).

 

Kenneth A. Small and Kurt Van Dender (2007), “Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect,” Energy Journal, Vol. 28, No. 1, pp. 25-51; at www.econ.uci.edu/docs/2005-06/Small-03.pdf.

 

Steven Spears, Marlon G. Boarnet and Susan Handy (2010), Draft Policy Brief on the Impacts of Road User Pricing Based on a Review of the Empirical Literature, for Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).

 

Sterner, Dahl and Frazen (1992), “Gasoline Tax Policy, Carbon Emissions and the Global Environment,” Journal of Transport Economics and Policy, Vol. 26, No. 2, p. 109-119.

 

Thomas Sterner (2006), “Fuel Taxes: An Important Instrument for Climate Policy,”

Energy Policy, Vol. 35. pp. 3194–3202; at  www.hgu.gu.se/files/nationalekonomi/personal/thomas%20sterner/a78.pdf.

 

TCRP (2003), Road Value Pricing: Traveler Response to Transport System Changes, Transit Cooperative Research Program Report 95; Transportation Research Board (www.trb.org); at http://trb.org/publications/tcrp/tcrp_rpt_95c14.pdf.

 

TRACE (1999), Elasticity Handbook: Elasticities for Prototypical Contexts, TRACE; Costs Of Private Road Travel And Their Effects On Demand, Including Short And Long Term Elasticities; Prepared for the European Commission, Directorate-General for Transport, Contract No: RO-97-SC.2035, (www.cordis.lu/transport/src/tracerep.htm).

 

Transport WA (2001), TravelSmart 2001, Transport Western Australia (www.travelsmart.transport.wa.gov.au).

 

TRL (2004), The Demand for Public Transit: A Practical Guide, Transportation Research Laboratory, Report TRL 593 (www.trl.co.uk); at www.demandforpublictransport.co.uk. This 240-page document is a detailed analysis of factors that affect transit demand, including demographic and geographic factors, price, service quality and the price of other modes.

 

USEPA (1998), Technical Methods for Analyzing Pricing Measures to Reduce Transportation Emissions, USEPA Report #231-R-98-006, (www.epa.gov/clariton/clhtml/pubtitle.html).

 

Erin Vaca and J. Richard Kuzmyak (2005), Parking Pricing and Fees, Chapter 13, TCRP Report 95, Transit Cooperative Research Program, Transportation Research Board (www.trb.org), Federal Transit Administration (www.fta.dot.gov); at www.trb.org/publications/tcrp/tcrp_rpt_95c13.pdf.

 

Francis Wambalaba, Sisinnio Concas and Marlo Chavarria (2004), Price Elasticity of Rideshare: Commuter Fringe Benefits for Vanpools, National Center for Transportation Research, Center for Urban Transportation Research (www.nctr.usf.edu).

 

Mark Wardman and Jeremy Shires (2003), Review Of Fares Elasticities In Great Britain, ITS Working Paper 573, Institute for Transport Studies, University of Leeds (www.its.leeds.ac.uk/working/downloads/WP573.pdf).

 

Clark Williams-Derry (2011), Toll Avoidance And Transportation Funding: Official Estimates Frequently Overestimate Traffic And Revenue For Toll Roads, Sightline Institute (www.sightline.org); at www.sightline.org/research/sprawl/toll-avoidance-and-transportation-funding.

 

Bryon York and David Fabricatore (2001), Puget Sound Vanpool Market Assessment, Office of Urban Mobility, WSDOT (www.wsdot.wa.gov/mobility/TDM/studyvpmrkt.html).

 

Jeffrey Zupan (2001), Vehicle Miles Traveled in the United States: Do Recent Trends Signal More Fundamental Changes? Surdna Foundation (www.surdna.org).


This Encyclopedia is produced by the Victoria Transport Policy Institute to help improve understanding of Transportation Demand Management. It is an ongoing project. Please send us your comments and suggestions for improvement.

 

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