Congestion Reduction Strategies

Identifying and Evaluating Strategies To Reduce Traffic Congestion

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TDM Encyclopedia

Victoria Transport Policy Institute

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Updated 4 November 2008

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This chapter describes methods for measuring congestion, factors that affect traffic congestion, and potential strategies for reducing congestion problems, including TDM strategies that reduce peak-period travel demand or improve transportation alternatives, and various ways to increase roadway capacity.

Measuring Congestion

Traffic Congestion refers to the incremental costs resulting from interference among road users. These impacts are most significant under urban-peak conditions when traffic volumes approach a road’s capacity. The resulting congestion reduces mobility and increases driver stress, vehicle costs and pollution (TTI, 2001; INRIX, 2008). Traffic congestion is considered one of the main urban transportation problems (in this case, “urban” includes suburbs, and even small resort communities during tourist season or other major events), with an estimated cost of approximately $100 billion annually in the U.S., and comparable costs in other countries (Congestion Costs).

Table 1 Roadway Level-Of-Service (Homburger, Kell and Perkins, 1992; Wikipedia, 2008)

LOS	Description	Speed (mph)	Flow (veh./hour/lane)	Density (veh./mile)
A	Traffic flows at or above the posted speed limit and all motorists have complete mobility between lanes.	Over 60	Under 700	Under 12
B	Slightly congested, with some impingement of maneuverability. Two motorists might be forced to drive side by side, limiting lane changes.	57-60	700-1,100	12-20
C	Ability to pass or change lanes is not assured. Most experienced drivers are comfortable, and posted speed is maintained. but roads are close to capacity. This is often the target for urban highways.	54-57	1,100-1,550	20-30
D	Typical of a urban highway during commuting hours. Speeds are somewhat reduced, motorists are hemmed in by other cars and trucks.	46-54	1,550-1,850	30-42
E	Flow becomes irregular and speed varies rapidly, but rarely reaches the posted limit. On highways this is consistent with a road over its designed capacity.	30-46	1,850-2,000	42-67
F	Flow is forced; every vehicle moves in lockstep with the vehicle in front of it, with frequent drops in speed to nearly zero mph. A road for which the travel time cannot be predicted.	Under 30	Unstable	67-Maximum

This table summarizes roadway Level of Service (LOS) rating.

Congestion can be Measured in various ways, including roadway Level of Service (LOS), average traffic speed, and average congestion delay compared with free-flowing traffic (“Congestion Costs,” Litman, 2005). The capacity of a road depends on various design factors such as lane widths and intersection configurations. Tables 1 and 2 show the relationships between traffic speed, volume and density for a highway, and how these factors relate to Level of Service ratings. Traffic speed and flow on urban streets are determined primarily by intersection capacity, which is affected by traffic volumes on cross streets and left turn signal phases.

Table 2 Maximum Traffic Volumes (Passenger Cars Per Hour Per Lane)

	LOS A	LOS B	LOS C	LOS D	LOS E
4-lane Freeway	700	1,100	1,550	1,850	2,000
2-lane Highway	210	375	600	900	1,400
4-lane Highway	720	1,200	1,650	1,940	2,200

This table shows maximum traffic volume per lane for various types of roadways.

A vehicle’s road space requirements increase with speed, because drivers must leave more shy distance between their vehicle and other objects on or beside the roadway. Traffic flow (the number of vehicles that can travel on a road over a particular time period) tends to be maximized at 30-55 mph on highways with no intersections, and at even lower speeds on arterials with signalized intersections. When a roadway approaches its maximum capacity, even small Speed Reductions can significantly increase flow rates.

As these tables indicate, traffic congestion is a non-linear function, meaning that a small reduction in urban-peak traffic volume can cause a proportionally larger reduction in delay. For example, a 5% reduction in traffic volumes on a congested highway (for example, from 2,000 to 1,900 vehicles per hour) may cause a 10-30% increase in average vehicle speeds (for example, increasing traffic speeds from 35 to 45 miles per hour). As a result, even relatively small changes in traffic volume or capacity on congested roads can provide relatively large reductions in traffic delay. Modeling by Deakin and Harvey (1998) indicate that a percentage reduction in urban vehicle mileage tends to produce about twice the percentage reduction in traffic congestion delays. Of course, when, where and what type of travel changes will affect these congestion reduction impacts.

Analysis by Zupan (2001) indicates that each 1% increase in VMT in a U.S. urban region was associated with a 3.5% increase in congestion delays in that region during the 1980’s, but this relationship disappeared during the 1990s. This change may reflect increased ability of travelers to avoid peak-period driving, through flextime, telework and suburbanization of destinations, allowing VMT growth without comparable increases in congestion delay. The relationship between vehicle travel and congestion delay is probably must stronger when evaluated at a more disaggregated level, for example, on individual corridors or roads.

“Traffic incidents” (disabled vehicles and accidents) account for an estimated 60% of delay hours. Although random events, they tend to cause the greatest delays where traffic volumes approach road capacity and so are considered congestion costs. In uncongested conditions an incident causes little or no traffic delay, but a stalled car on the shoulder of a congested road can produce 100-200 vehicle hours of delay on adjacent lanes.

Larger and heavier vehicles tend to require more road space and are slower to accelerate, and so cause more traffic congestion than smaller, lighter vehicles. The relative congestion impact of different vehicles is measured in terms of “Passenger Car Equivalents” or PCEs. Large trucks and buses tend to have 1.5-4 PCEs, depending on roadway conditions, as shown in Table 3, and even more through intersections or under stop-and-go driving conditions. A large SUV imposes 1.4 PCEs and a van 1.3 PCEs when traveling through an intersection (Shabih and Kockelman, 1999).

Table 3 Passenger Car Equivalents (TRB, 2000, Exhibits 20-9 and 21-8)

	Traffic Flow	Level Terrain	Rolling Terrain	Mountainous Terrain
Two-Lane Highways	Passenger Cars/lane/hr	Passenger Car Equivalents
Trucks & Buses	0-300	1.7	2.5	N/A
Trucks & Buses	300-600	1.2	1.9	N/A
Trucks & Buses	> 600	1.1	1.5	N/A
Recreational Vehicles	0-300	1.0	1.1	N/A
Recreational Vehicles	300-600	1.0	1.1	N/A
Recreational Vehicles	> 600	1.0	1.1	N/A
Multi-Lane Highways	Passenger Cars/lane/hr	Passenger Car Equivalents
Trucks & Buses	Any	1.5	2.5	4.5
Recreational Vehicles	Any	1.2	2.0	4.0

This table indicates the Passenger Car Equivalents (PCEs) imposed by larger vehicles under various conditions.

Various indices described in Table 4 are used to quantify, monetize (measure in monetary units) and evaluate congestion. These represent different perspectives and assumptions, which can favor one group or set of solutions over others. Some congestion indicators, such as roadway LOS and the Travel Time Index, only consider delays to motorists. Percent Travel Time declines if the total amount of driving on uncongested roads increases, implying that congestion declines if per capita VMT increases, for example, due to increased sprawl. These indicators ignore the benefits to travelers who shift to alternative modes, or from Smart Growth that increase land use Accessibility by clustering common destinations closer together. Indicators that reflect impacts per capita rather than per vehicle are more suitable for evaluating overall congestion costs.

Table 4 Congestion Indicators (Litman, 2005)

Indicator	Description	TDM?
Roadway Level Of Service (LOS)	Congestion intensity on a particular roadway or at an intersection, rated from A (uncongested) to F (extremely congested).	No
Travel Time Rate	The ratio of peak period to free-flow travel times, considering only reoccurring delays (normal congestion delays).	No
Travel Time Index	The ratio of peak period to free-flow travel times, considering both reoccurring and incident delays (e.g., traffic crashes).	No
Percent Travel Time In Congestion	Portion of peak-period vehicle or person travel that occurs under congested conditions.	No if for vehicles, yes if for people.
Congested Road Miles	Portion of roadway miles that are congested during peak periods.	No
Congested Time	Estimate of how long congested “rush hour” conditions exist	No
Congested Lane Miles	The number of peak-period lane miles that have congested travel.	No
Annual Hours Of Delay	Hours of extra travel time due to congestion.	No if for vehicles, yes if for people.
Annual Delay Per Capita	Hours of extra travel time divided by area population.	Yes
Annual Delay Per Road User	Hours of extra travel time divided by the number of peak period road users.	Yes
Excess Fuel Consumption	Total additional fuel consumption due to congestion.	Yes
Fuel Per Capita	Additional fuel consumption divided by area population	Yes
Annual Congestion Costs	Hours of extra travel time multiplied times an travel time value, plus the value of additional fuel consumption. This is a monetized congestion cost.	Yes
Congestion Cost Per Capita	Additional travel time costs divided by area population	Yes
Average Traffic Speed	Average speed of vehicle trips for an area and time (e.g., peak periods).	No
Average Commute Travel Time	Average commute trip time.	Yes
Average Per Capita Travel Time	Average total time devoted to travel.	Yes

This table summarizes various congestion cost indicators. Some only consider impacts on vehicle traffic and ignore the benefits of shifts to alternative modes or reductions in travel distances, and so are unsuited for evaluating the congestion reduction benefits of most TDM strategies.

How congestion is measured can affect the evaluation of congestion reduction strategies. For example, increased development density tends to increase congestion measured as roadway LOS or delay per vehicle trip, since more trips tend to be generated per acre. From this perspective, Smart Growth tends to be harmful and sprawl tends to be helpful for reducing congestion problems (Taylor, 2002; Litman, 2003). However, higher density tends to increase land use Accessibility and Transportation Options, resulting in shorter trip distances and shifts to alternative modes such as walking and public transit. Although streets in higher density urban areas may experience more LOS E or F, implying serious congestion problems, urban residents spend less time delayed by congestion because they have closer destinations and better travel options. As a result, per capita (as opposed to per-vehicle trip or per-driver) congestion delay tends to be greater in lower-density, automobile-dependent suburban areas such as Los Angeles and Houston than in higher-density urban areas such as New York and San Francisco, because low-density areas have more per-capita vehicle mileage (STPP, 2001).

Similarly, HOV Priority, Walking and Cycling Improvements, Speed Reductions and Traffic Calming may increase congestion when measured as roadway LOS, but reduce it when measured as per capita congestion delay, because they reduce total vehicle mileage and allow traffic to flow more smoothly. In general, use of roadway LOS, average traffic speeds and travel time index to evaluate traffic congestion tends to favor roadway capacity expansion solutions, while indicators such as per-capita congestion delay and vehicle costs tend to favor multi-modal and land use management solutions.

Flannery, McLeod and Pedersen (2006) identify factors besides Volume/Capacity ratios that affect roadway quality as perceived by motorists, and so recommend be incorporated into roadway Level-of-Service ratings, including traffic mix (number of trucks and buses), speed differentials, number of stops, number of signals, lane widths, number of lane changes, travel speed and delay, driveway frequency, presence of sidewalks and pedestrian, quality of traveler information, and aesthetic conditions.

Traffic congestion is usually defined and measured only in terms of the delays that motor vehicle traffic imposes on other motor vehicles (TRB, 1997). Traffic impacts on cyclists and pedestrians are usually ignored, although in some areas they represent a major share of travel delay (Evaluating Nonmotorized Transport). Ignoring these impacts on nonmotorized travel tends to understate the benefits of TDM strategies that reduce vehicle traffic volumes, and overstate the benefits of roadway capacity expansion that create barriers to nonmotorized travel.

Multi-Modal Level-of-Service rating systems can be used to evaluate the quality of various transport modes, including walking, cycling and public transit. This helps create a more neutral planning decisions that involve tradeoffs between different transport modes, such as the disbenefits to nonmotorized travel (and therefore transit access) that results from increases roadway widths and higher traffic speeds and volumes.

Winston and Langer (2004) find that highway spending is not a cost effective way of reducing congestion costs. Some congestion reduction strategies, such as HOV Priority and Transit Improvements, are most effective when automobile traffic experiences the greatest delay (Litman, 2004). Such strategies generally will not eliminate traffic congestion, since automobile congestion is what makes these alternatives relatively attractive, but they can significantly reduce the degree of congestion delay experienced both by people who shift mode and those who continue driving. For example, they may improve a roadway from LOS E to LOS D, which is a significant improvement, but by themselves will never result in LOS B.

The type of analysis used can significantly affect the evaluation of congestion reduction strategies. If urban roadway capacity is not expanded traffic volumes reach a point of equilibrium, in which congestion delays discourage further growth in peak-period vehicle trips (Rebound Effects). Adding a general traffic lane significantly reduces short-term congestion, but traffic volumes grow over time so congestion nearly returns to its pervious level within a few years (Litman, 2006a). A transit improvement, such as grade separated rail, a busway or HOV facility, provides little short-term congestion reduction, but congestion reduction benefits increase over time as delays on parallel highways make alternative modes increasingly attractive (Litman, 2006b). Although roadway congestion continues, it never becomes as bad as would occur without this relief. As a result, shorter-term analysis of congestion reduction benefits tends to favor roadway capacity expansion, while longer-term analysis tends to favor transit and HOV improvements.

Demand Management Strategies

The following TDM strategies tend to be particularly effective at reducing traffic congestion.

Road Pricing

Road Pricing involves charging motorists directly for driving on a particular road or in a particular area. Congestion Pricing is Road Pricing with higher rates during congested periods. It can reduce traffic congestion on a particular roadway, particularly if implemented as part of a comprehensive TDM program, for example, with Transit Improvements and Rideshare Programs. Road Pricing applied on just one roadway may cause traffic to shift routes, increasing traffic congestion on other roads.

Commute Trip Reduction Programs

Commute Trip Reduction programs encourage commuters to use alternative modes for trips to work and school. Such programs tend to be particularly effective if they incorporate suitable Financial Incentives, such as Transit Benefits or Parking Pricing. In most areas, commute traffic represents the majority of traffic on congested roads so Commute Trip Reduction programs can be particularly effective at reducing traffic congestion.

Flextime

Flextime means that employees are allowed some flexibility in their daily work schedules. For example, rather than all employees working 8:00 to 4:30, some might work 7:30 to 4:00, and others 9:00 to 5:30. This shifts travel from peak to off-peak periods, which can reduce traffic congestion directly; and can assist commuters in matching transit and rideshare schedules, allowing mode shifts.

Transit Improvements and Rideshare Programs

Transit Improvements and Rideshare Programs can be effective congestion reduction strategies, particularly if implemented with other incentives to shift mode, such as HOV Priority and Road Pricing. Since they experience economies of scale, these modes tend to be particularly cost effective under urban-peak travel conditions, when congestion problems are most severe. Some studies find that communities with good transit systems have significantly less traffic congestion than those that do not, because they tend to encourage more efficient overall transportation and land use patterns.

In general, if a corridor has enough vehicle traffic to experience traffic congestion there is enough demand for transit and ridesharing to provide congestion reduction benefits. However, simply operating buses or a rideshare matching service will not necessarily achieve this benefit in developed countries where most households own an automobile, and automobile travel is supported by low fuel prices and free parking. Although owning an automobile is expensive, most costs are fixed, giving motorists an incentive to drive rather than use alternatives. Only by giving discretionary riders (travelers who have the option of driving, also called choice riders) suitable incentives to shift mode can transit and ridesharing achieve their full congestion reduction benefits.

How Transit and HOV Reduces Traffic Congestion (Transit Evaluation)

Urban traffic congestion tends to maintain equilibrium. If congestion increases, people change destinations, routes, travel time and modes to avoid delays, and if it declines they take additional peak-period trips (Rebound Effects). Reducing this point of equilibrium is the only way to reduce congestion over the long run. The quality of travel alternatives has a significant effect on the point of congestion equilibrium: If alternatives are inferior, few motorists will shift mode and the level of equilibrium will be relatively high. If travel alternatives are relatively attractive, motorists are more likely to shift modes, resulting in a lower equilibrium.

The actual number of motorists who shift from driving to transit may be relatively small, just a few percent of total travelers on the corridor, but that is enough to reduce roadway congestion delays. Congestion does not disappear, but it never gets as bad as would occur if quality transit service did not exist.

To attract discretionary riders (travelers who have the option of driving), public transit must be fast, comfortable, convenient and affordable. Grade-separated transit (such as rail on its own right-of-way or buses with HOV Priority features) provides a travel time advantage that tends to attract discretionary riders. When transit is faster than driving, a portion of travelers shift mode until the highway reaches a new congestion equilibrium (that it, until congestion declines to the point that transit is no longer faster). As a result, the faster the transit service, the faster the traffic speeds on parallel highways. Other types of Transit Improvements can also encourage motorists to shift to transit.

Shifting traffic from automobile to transit on a particular highway not only reduces congestion on that facility, it also reduces the amount of vehicle traffic discharged onto surface streets, providing “downstream” congestion reduction benefits. For example, when comparing the congestion reduction benefits of a highway widening project with some sort of transit service improvement, the analysis should not be limited to just the highway that is expanded. It is important to also account for the additional congestion on surface streets where highway traffic discharges resulting from increased traffic volumes, and the reduction in surface street traffic congestion that would result if the transit improvement attracts highway drivers out of their cars.

Improving travel options can therefore benefit all travelers on a corridor, both those who shift modes and those who continue to drive.

HOV Priority

HOV Priority strategies favor bus, vanpool and carpool travel, including dedicated traffic lanes, queue-jumping lanes (other vehicles must wait in line to enter a highway or intersection, but HOVs enter directly), priority in traffic signal controls, favorable building access and parking (such as bus stops and HOV parking located close to the front of buildings).

HOV Priority congestion reduction effectiveness depends on maintaining significant travel advantage for HOVs. For example, HOV lanes should maintain Level Of Service A or B, which means less than about 1,000 vehicles per hour on a grade-separated highway and half that on a surface street. There is often pressure to compromise this advantage to achieve other objectives, such as political pressure by special interest groups to reduce HOV requirements (such as from 3+ to 2+, and to allow single occupant vehicles such as motorcycles, hybrid cars, taxis), and financial pressure by transportation agencies to allow more vehicles that pay a toll.

Access Management

Access Management is a term used by transportation professionals for coordination between roadway design and land use to improve transportation. It involves changing land use planning and roadway design practices to limit the number of driveways and intersections on arterials and highways, constructing medians to control turning movements, encouraging clustered development, and creating more pedestrian-oriented street designs. This reduces “friction” along the roadway, which tends to increase traffic speeds, reduce congestion delays and reduce crashes.

Parking Management and Pricing

Parking Management and Parking Pricing are effective ways to reduce automobile travel, and tend to be particularly effective in urban areas where congestion problems are greatest. Driving and parking are virtually perfect complements: you need a parking space at virtually every destination (except when driving a vehicle on its final trip to a dismantling facility or to be teleported into space). In particular, since most urban-peak highway trips are for commuting, employee parking pricing can have a similar effect as a road toll. Analysis by Roth (2004) indicates that more efficient pricing of on-street parking would make urban driving more expensive but more efficient, due to lower levels of traffic congestion and the relative ease in finding a parking space near destinations, as well as providing new revenues. He theorizes that over the long-term this can benefit urban areas overall.

Pricing Impacts on Traffic Congestion

When traffic volumes approach a road’s maximum capacity, a reduction in demand tends to cause proportionally larger reductions in congestion delays. As a result, TDM strategies that reduce a relatively small percentage of urban-peak vehicle travel can provide significant mobility improvement. For example, one comprehensive traffic modeling study (Harvey and Deakin, 1996) predicted that in Southern California:

· A 10¢ per vehicle-mile congestion fee would reduce VMT 2.3% and congestion delay 22.5% (a ratio of 9.8).

· A $3.00 (1991 dollars) per day parking fee would cause a 2.7% reduction in VMT and a 7.5% reduction in congestion delay (a ratio of 2.8).

· A 2¢ per vehicle-mile VMT fee would reduce VMT 4.4% and congestion delay 9.0% (a ratio of 2.0).

· A $0.50 fuel tax increase would reduce VMT 4.1%, and congestion delay 6.5% (a ratio of 1.6).

· A 1¢ per vehicle-mile emission fee would reduce VMT 2.2% and congestion delay 3.0% (a ratio of 1.4).

This analysis indicates that the most effective pricing strategy for reducing traffic congestion is congestion pricing, followed by parking fees, VMT fees, fuel taxes and emission fees. Of course, these other fees may be more cost effective at achieving other objectives, such as parking cost savings and emission reductions.

Such pricing incentives tend to be most effective when implemented in conjunction with other TDM strategies, such HOV Priority and Transit Service Improvements.

Distance Based Fees

Converting vehicle insurance and registration fees into distance-based charges provides a significant financial incentive to reduce driving, comparable to nearly doubling fuel prices. Unlike Road Pricing, distance-based fees affects all travel, not just travel on certain highways, and so provides congestion reduction benefits on surface streets without shifting traffic to other routes.

Fuel Pricing

Fuel price increases (for example, due to higher fuel taxes) can help reduce traffic congestion. 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. A 28% increase in average fuel prices during the first half of 2008 contributed to a 3% reduction in average national Travel Time Index values.

Freight Transport Management

Freight trucks represent a relatively small portion of total traffic but can make a relatively large contribution to congestion, due to their large size and slow acceleration. A large truck can contribute as much congestion as 3-6 passenger cars. Freight transport management can reduce total freight traffic and shift freight to less congested routes.

Traffic Calming and Roundabouts

Traffic Calming includes a variety of roadway design features that reduce vehicle traffic speeds and volumes. Some Traffic Calming result in smoother traffic and more optimal speeds, causing overall reductions in congestion delays. In particular, Modern Roundabouts are an alternative to stop signs and traffic signals at small and medium-size intersections that can reduce stopping requirements and avoid traffic “platoons” (vehicles bunching up at intersections. For more information see Roundabouts USA (www.RoundaboutsUSA.com)

Speed Reductions

Reducing traffic speeds to 55 mph or less on congested roads can often increase traffic flow and reduce conflicts and driver stress. This may be achieved by reducing posted speed limits, improving enforcement of existing limits, or implementing road design features that discourage excessive speeds.

Car-Free Planning and Vehicle Restrictions

Comprehensive Car-free Planning and Vehicle Restrictions that support other TDM strategies (nonmotorized transport, transit, efficient land use, etc.) can reduce vehicle use in an area, although if applied on a small scale they may simply shift traffic from one area to another. In some areas, certain types of vehicles (such as freight trucks) are only allowed during off-peak periods.

Telework

Telework involves the use of telecommunications to substitute for physical travel. It includes telecommuting, employees with mobile work (e.g., sales staff or field workers who rely heavily on telecommunications), and people who are self-employed and able to work from a home office due to efficient communications. This gives people a way to avoid traveling under congested conditions.

Smart Growth

Lower-density, automobile dependent land use tends to increase total traffic congestion and roadway costs. Although high-density cities tend to have the slowest traffic speeds, suburban areas have the greatest per capita traffic delay because residents drive more miles and have no viable transportation alternatives. A USEPA study (2004) found that regardless of population density, transportation system design features such as greater street connectivity, a more pedestrian-friendly environment, shorter route options, and more extensive transit service tend to reduce per-capita vehicle travel, congestion delays, traffic accidents and pollution emissions.

Residents of automobile dependent suburban areas such as San Bernardino County tend to experience greater per capita congestion delay than dense cities such as New York and Chicago (STPP, 1999). U.S. automobile commute travel times are lowest for residents of communities with moderate to high densities (11-16 residents per acre), while transit commute times decrease with density (Levinson and Kumar, 1997). Empirical evidence indicates that higher-density development does not necessarily increase congestion (Ewing, Pendall and Chen, 2002), and Smart Growth strategies that improve Accessibility and Transportation Diversity can further reduce per capita congestion delay.

Congestion Costs Tend to Increase With Wealth

Traffic congestion costs tend to increase with wealth because consumers tend to purchase more vehicles, which greatly increases the amount of space needed for travel (a car trip typically requires an order of magnitude more space than the same trip made by walking, cycling or transit), while also increasing demand for land for residential, commercial and recreational activities. Although increased wealth allows a community to afford more facility construction costs, the supply of land does not increase. Roadway and parking capacity expansion must compete for land that is increasingly expensive, so land costs become the limiting factor in expanding roadways. Although sprawl may seem to overcome this problem by expanding roads at the urban fringe where land costs are lower, dispersed development increases per-capita vehicle travel, and therefore more lane-miles and parking spaces per capita, so land costs continue to be a major constraint. As a result, congestion costs tend to increase and alternative modes and demand management tend to become more important as a community becomes wealthier.

Other Strategies

Other congestion reduction strategies are described below.

Road Capacity Expansion

Road widening is often advocated as ways to reduce traffic congestion. However, it tends to be expensive, and may provide only modest congestion reduction benefits over the long run, since a significant portion of added capacity is often filled with induced peak period vehicle traffic (Rebound Effects). A large amount of additional capacity would be needed to reduce urban traffic congestion. One study, that highway capacity would need to increase by about 70% over two decades to maintain optimal traffic flow in the Twin Cities (Sanderson and Davis, 2002), representing billions of dollars in financial costs, plus environmental and social costs from roadway construction and increased vehicle use.

Adding urban highway capacity typically costs 10-50¢ or more per additional vehicle-mile of travel, plus 5-10¢ per vehicle-mile for road maintenance and traffic services, indicating roadway costs of $3-10 for a commute trip that involves 10-miles of travel under congested urban-peak roadway conditions (Transportation Costs). Some research indicates that roadway capacity expansion provides only slight reductions in urban traffic congestion (STPP, 2001).

Grade Separation

Grade separation can significantly increase roadway capacity, since intersections are a major cause of traffic delay. A typical arterial lane can carry less than 1,000 vehicles per hour, while a grade separated freeway can carry more than twice that amount. Grade separation of rail lines can increase traffic flow where railroad crossings are a major cause of traffic delay.

Intersection Improvements

Various strategies that increase intersection capacity can reduce delays, since intersections are often a limiting factor in roadway traffic flow. These include additional lanes at the intersection approach, left- and right-turn lanes, and improved signal synchronization.

Intelligent Transportation Systems

Intelligent Transportation Systems include the application of a wide range of new technologies, including driver information, vehicle control and tracking systems, transit improvements and electronic charging (see ITS Online and ITS America). These can provide a variety of transportation improvements, including driver convenience, reduced congestion, increased safety, more competitive transit, and support for pricing incentives.

Incident Detection and Management

A significant portion of traffic congestion results from some sort of traffic incident, such as a disabled vehicle, a crash or dangerous driving. Many urban regions have coordinated programs that prevent, identify and respond to such events quickly and efficiently. These may include centralized traffic management centers, video traffic surveillance, emergency response teams and special resources for dealing with specific problems, such as cranes and even helicopters to move disabled vehicles.

Motorist Information Systems

Motorist information can include changeable message signs, radio reports and Internet information about traffic conditions. These can reduce motorist stress by letting them anticipate conditions.

Ramp Metering

Ramp meters control the number of vehicles that can enter a highway ramp. This tends to maintain smoother traffic flow on highways.

One-Way Streets

In some situations, converting from two-way to one-way streets. This can increase traffic flows and simplify intersections, although it may make access to buildings less convenient.

Narrow Vehicles

Motorcycles and ultra narrow cars (less than 42 inches wide) can travel side-by-side, particularly under lower-speed conditions, and so allow more vehicles to travel per lane (www.commutercars.com).

Reversible Lanes

In some situations it is possible to have a traffic lane that is reversed to carry traffic in the direction of maximum flow, for example, into a city center during the morning rush hour and outward during the evening rush hour.

Evaluating Congestion Reduction Strategies

Traffic congestion, and the effects of congestion reduction strategies, and highly variable and site specific. A particular strategy may be highly effective in one situation but provides no benefit in another. Traffic engineering models are used to predict the impacts of a congestion reduce strategy in a particular situation.

Capacity Expansion or Demand Management

Current transportation planning practices tend to favor roadway capacity expansion over demand management solutions to traffic congestion problems. These practices must be changed for TDM strategies to be implemented when it is the most cost effective solution overall.

· Least-Cost Planning is a planning framework that allows capacity expansion and demand management options to be considered equally.

· Capacity expansion often has dedicated funding that cannot be used for TDM alternatives even if they are more cost effective, so Institutional Reforms may be required.

· TDM Programs provide an institutional framework for implementing specific TDM strategies.

· Comprehensive Transportation Planning considers additional impacts that are often ignored in conventional transportation planning, including additional benefits and costs, and the effects of generated traffic.

Implications of Generated Traffic

In areas with high travel demand, urban traffic congestion tends to maintain self-limiting equilibrium: vehicle traffic volumes increase to fill available capacity until congestion limits further growth (Rebound Effects). Any time a consumer makes a travel decision based on congestion (“Should I run that errand now? No, I’ll wait until later when traffic will be lighter”) they help maintain this equilibrium.

Generated Traffic is the additional vehicle travel that results from increased roadway capacity (Litman, 2001). This consists of a combination of diverted vehicle trips (trips shifted in time, route and destination), and induced vehicle travel (shifts from other modes, longer trips and new vehicle trips). Over the long run, Generated Traffic often fills a significant portion (50-90%) of added urban roadway capacity.

It is important to consider Generated Traffic when Evaluating congestion reduction strategies. Generated Traffic does not eliminate the benefits of capacity expansion projects, but it can significantly change the nature of their benefits. It often means that congestion reduction benefits are smaller and shorter lived than projected, that more benefits consist of increased consumer mobility and urban fringe property values, and induced vehicle travel can exacerbate problems such as downstream congestion, crashes, Pollution Emissions, urban sprawl and overall Automobile Dependency. Evaluation that ignores the effects of Generated Traffic tends to overstate the true benefits of roadway capacity expansion and understate the benefits of demand management strategies.

Not all congestion reduction strategies cause induced travel. Some types of TDM strategy do not contribute to generated traffic and so tend to be particularly effective at providing long-term congestion reduction benefits. Strategies that increase the costs of driving or make alternative travel options more attractive under urban-peak conditions can change the point of congestion equilibrium. For example, Congestion Pricing, Parking Pricing, Distance-Based Charges, HOV Priority and grade separated Transit Improvements can reduce overall traffic congestion. Roadway capacity expansion or Flextime (which frees up peak-period road space) is likely to generate traffic, and so will provide relatively little long-term congestion reduction, depending on circumstances. Strategies that improve transportation choices, such as Ridesharing or Transit Improvements without HOV Priority are unlikely to provide significant congestion reduction if implemented on a small scale, but may provide some benefit if implemented on a large scale that affects a major portion of total peak-period travelers.

Table 5 Generated Traffic

Significant Generated Traffic	Depends on Circumstances	Little or No Generated Traffic
Flextime Roadway Capacity Expansion Highway Grade Separation Intersection Improvements Incident Detection & Management Motorist Information Systems Ramp Metering One-Way Streets Reversible Lanes	Access Management ITS Commute Trip Reduction Programs Transit Improvements Rideshare Programs Traffic Calming & Roundabouts Vehicle Restrictions	Road Pricing HOV Priority Distance Based Fees Freight Transport Management Speed Limit Enforcement

This table indicates whether a strategy is likely to induce additional vehicle travel.

Best Practices

· Congestion reduction programs should consider a wide range of possible solutions, including demand management.

· The impacts of generated and induced travel should be considered when evaluating potential congestion reduction strategies.

· Congestion reduction programs should favor strategies that provide long-term congestion reductions:

- Grade separated Transit Improvements and HOV facilities can reduce traffic congestion on parallel highways (Social Benefits of Public Transit)

- Pricing strategies such as Road Pricing, Distance-Based Fees and Comprehensive Market Reforms tend to shift the demand curve, reducing the overall point of congestion equilibrium.

- Land use management strategies such as New Urbanism and Smart Growth may increase local traffic congestion (within a neighborhood), but reduce per capita vehicle travel, and reductions in regional traffic congestion, resulting in overall reductions in congestion costs.

Wit and Humor

All but forgotten in recent years, bridge gateway lions are now staging an important comeback. Leonine Features to Enhance Bridge Capacity outlines the historic role of bridge lions, summarizes current research in the field and offers a state-of-the-art method for computing their impacts on traffic capacity. Three illustrations, one “Ferocity Factors” table, one case study.

Examples and Case Studies

Travel Choices Study (http://psrc.org/projects/trafficchoices/index.htm)

Four hundred Puget Sound-area residents are participating in a study to determine how variable tolls would change driving habits. The study is being conducted by the Puget Sound Regional Council. It began July 1, 2005 and will continue through March 2006. Each participant is given $1,016 in a debit account. A meter similar to those used in taxis was installed in their car and, with the help of global positioning satellites that keep track of where and when they drive, it subtracts a toll that varies depending on the time of day and the route. For instance, if participants drive on Interstate 405 on a weekday between 4 p.m. and 7 p.m. – peak commuting hours – a computer subtracts 50 cents a mile from their account. If they make the same trip using city streets after 7 p.m. the computer subtracts only 5 cents a mile. That means the 17-mile trip to the Greenwood neighborhood cost as much as $8.50 during peak periods, as little as 85¢ during evenings, and there are no tolls between 10 p.m. and 5 a.m. The dash-board meter keeps track of what each trip costs.

Examples of Successful Traffic Reduction Programs (Nelson\Nygaard, 2006, Appendix A)

• Arlington County, Virginia, Rosslyn-Ballston Corridor: In the 1960s and 1970s, this suburban corridor consisted largely of tired strip malls with the ubiquitous free parking, a surrounding fabric of single-family homes, and sharply declining population and retail sales. Today, development in the corridor is booming, but with little growth in traffic. Traffic counts from 1997 to 2004, for example, show that while office and residential development grew by 17.5% and 21.5% respectively, traffic along the Rosslyn-Ballston corridor grew by only 2.3%. Census Journey-to-Work Survey data show that over 47% of corridor residents now take transit to work.

• Bellevue, Washington: In downtown Bellevue, Washington, the drive alone commute rate fell by 30% from 1990 to 2000, falling from 81% driving alone to 57%.

• Boulder, Colorado: Since 1995, the drive-alone rate for employees in downtown Boulder has fallen almost 36%, from 56% driving alone to 36%, while the transit mode share has more than doubled from 15% to 34%.

• Cambridge, Massachusetts: Cambridge's Travel Demand Management Ordinance requires that developers reduce the drive alone rate for their development to 10% below the average rate for the census tract in which their development sits. Although the ordinance applies only to new development and building expansions, by two years after the adoption of the ordinance, citywide drive alone rates had declined, even as the state of Massachusetts experienced increasing drive alone rates.

• London, United Kingdom: Since the beginning of 2003, when congestion pricing was
• introduced in central London, the number of vehicle trips in the congestion pricing zone has fallen by 17%. Congestion, measured in terms of person-hours of delay per mile traveled, has fallen by 26%.

• Lloyd District, Portland, Oregon: In the nine years since the baseline figures were measured (in 1997), the drive alone rate among all Lloyd District employees has fallen almost 29%, from 60% to 43%.

• Portland, Oregon: In 1975, the City of Portland set a cap of roughly 40,000 parking spaces downtown, later replaced with tight maximum parking requirements. City officials credit these limits with helping to increase downtown's transit mode split from about 20% in the early 1970s to 48% in the mid-1990s.

• San Francisco: Employment in downtown San Francisco doubled between 1968 and 1984, while the number of cars traveling into the downtown stayed the same.

• Stockholm, Sweden: Six months into the trial of congestion pricing the average traffic reduction across the control points between 6:30 AM and 6:29 PM is 22%.

• Vancouver, Canada: As a deliberate transportation strategy, Vancouver tremendously increased housing capacity in the downtown area to reduce commuting times and congestion, in what became known as the “living-first strategy”. From 1991 to 2002, the number of residents living downtown increased by 62%, to 76,000, but car trips into downtown remained essentially constant. In 1994, walking and cycling trips made up 20 percent of all daily trips into the downtown and together made up the third-highest used mode behind auto and transit trips: by 1999, walking and cycling trips made up 35 percent of all daily trips and are now the most frequently used mode.

Municipal Traffic Reduction Program (Nelson\Nygaard, 2006)

After the City of Pasadena, California commissioned a detailed study of potential traffic reduction strategies, the city manager and Transportation Advisory Committee recommended the following:

• Establish a target occupancy rate for on-street parking meters and request staff and develop a program to adjust meter prices to achieve that target.
• Investigate the conversion of an existing Residential Preferential Parking District to a Residential Parking Benefit District (so revenues are dedicated to that neighborhood).
• Prepare a budget proposal that would: (a) use a portion of parking meter revenues for transportation demand management programs within those districts, (b) use a portion of parking meter revenues to purchase Universal Transit Passes for employees within those districts, and (c) use a portion of parking meter revenues to support Car Sharing programs within those districts.
• Study the feasibility of removing minimum parking requirements for new development in the Central District and other suitable locations in the city.
• Study the feasibility of requiring the unbundling of parking costs for the second parking space per unit for new residential structures in the Central District and in the Transit Oriented Development Zones.
• Require proof of compliance with the State of California's Parking Cash-Out Law prior to issuing occupancy permits for new commercial development.
• Develop a proposal to implement Parking Cash-Out for City employees.
• Support efforts to establish a Bus Rapid Transit Route linking the Metro Orange and Gold Lines.
• Establish a priority for use of funds collected through the Traffic Reduction and Transportation Improvement Fee for the projects needed to implement a transit network.
• Study the feasibility of implementing a Congestion Pricing mechanism within the Central District Area.

Highway Congestion Reduction Plan

Doherty (2006) analyzes existing traffic patterns on Highway 1 near Vancouver, BC, a congested highway scheduled for expansion. A large proportion of highway traffic is shown to have both origins and destinations in areas that could be served efficiently by public transit. An assessment of the present transit system in the region shows that there is no reasonable alternative to the automobile for many people in the traffic catchment area studied, indicating that plans to provide transportation options in the urban are have not being met. The report suggests a set of possible transit investments that could significantly improve mobility while reducing highway congestion, all of which are included in regional transportation plans but not yet implemented. These proposed investments include:

Increased Frequent Service Coverage

A 20% increase in TransLink's bus fleet to provide 10 minute or better frequency bus service on major routes.

Transit Priority Measures

Transit priority measures include bus lanes, traffic signal priority for transit vehicles, high occupancy vehicle lanes (where transit vehicles use the lanes). Transit priority measures are proposed for the following routes:

Surrey-Coquitlam Link

A Surrey to Coquitlam bus route would require a queue jumper lane on the westbound approach to the Port Mann Bridge.

King George Busway

A busway on the King George Highway would serve the Growth Concentration Area in Surrey and connect to SkyTrain, the Coquitlam- Surrey Link and other important transit routes.

Fraser Highway

The integration of transit priority measures into TransLink's current widening of the Fraser Highway from Surrey to Langley could include measures such as high occupancy vehicle lanes and transit signal priority.

By improving transportation options, these or similar transit investments could significantly reduce traffic congestion on Highway 1, if combined with other effective transportation demand management measures. The capital cost of these proposed measures would be on the order of $300 to 500 million, far less than the $1,500 to $2,500 million estimated cost of widening Highway 1.

Congestion Relief Analysis (WSDOT, 2006)

A study by the Washington State Department of Transportation compared various potential congestion reduction strategies in its major urban areas, including highway capacity expansion, transit service improvements, High Occupant Vehicle priority lanes, and congestion pricing. The analysis found that the benefits of the other strategies increase if implemented with congestion pricing.

References And Resources For More Information

ACT (2004), The Role Of Demand-Side Strategies: Mitigating Traffic Congestion, Association for Commuter Transportation, for the Federal Highway Administration (http://tmi.cob.fsu.edu/act/FHWA_Cong_Mitigation_11%202%2004.pdf).

Jim Beamguard (1999), “Packing Pavement,” Tampa Tribune (www.tampabayonline.net/bguard/home.htm). Compares the road space used by transit patrons, motorists and cyclists.

Robert L. Bertini (2005), You Are the Traffic Jam: An Examination of Congestion Measures, Department of Civil & Environmental Engineering, Portland State University, presented at the Transportation Research Board Annual Meeting (www.trb.org); at www.its.pdx.edu/pdf/congestion_trb.pdf.

Cambridge Systematics (2005), Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation, Federal Highway Administration (www.ops.fhwa.dot.gov/congestion_report/index.htm).

Elizabeth Deakin, Greig Harvey, Randal Pozdena and Geoffrey Yarema (1996), Transportation Pricing Strategies for California: An Assessment of Congestion, Emissions, Energy and Equity Impacts, California Air Resources Board (www.arb.ca.gov/cgi). The same analysis is available in USEPA (1996), Technical Methods for Analyzing Pricing Measures to Reduce Transportation Emissions, USEPA Report #231-R-98-006, (www.epa.gov/clariton).

Elizabeth Deakin and Greig Harvey (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).

DFT (2006), Transport Analysis Guidance, Integrated Transport Economics and Appraisal, Department for Transport (www.webtag.org.uk/index.htm).

Eric Doherty (2006), Transportation for a Sustainable Region: Transit or Freeway Expansion?, The Livable Region Coalition (www.livableregion.ca); at

www.livableregion.ca/pdf/Transport_for_a_Sustainable_Region.pdf.

EIU (2006), Driving Change: How Policymakers Are Using Road Charging To Tackle Congestion, Economist Intelligence Unit (http://graphics.eiu.com/files/ad_pdfs/eiu_ibm_traffic_wp.pdf)

Reid Ewing, Rolf Pendall and Don Chen (2002), Measuring Sprawl and Its Impacts, Smart Growth America (www.smartgrowthamerica.org).

FHWA, Management and Operations Toolbox, (http://plan2op.fhwa.dot.gov/toolbox/toolbox.htm) provides information and techniques for evaluating transportation systems management strategies.

Aimee Flannery, Douglas McLeod and Neil J. Pedersen (2006), “Customer-Based Measures of Level of Service,” ITE Journal, Vol. 76, No. 5 (www.ite.org), May 2006, pp. 17-21.

GAO (2005), Highway And Transit Investments: Options for Improving Information on Projects’ Benefits and Costs and Increasing Accountability for Results, Report 05-172, Government Accountability Office (www.gao.gov/new.items/d05172.pdf).

Homburger, Kell and Perkins (1992), Fundamentals of Traffic Engineering, 13th Edition, Institute of Transportation Studies, UBC (www.its.berkeley.edu).

Humphrey Institute (1996), Buying Time; Research and Policy Symposium on the Land Use and Equity Impacts of Congestion Pricing, Humphrey Institute (www.hhh.umn.edu).

INRIX (2008a), National Traffic Scorecard, INRIX (http://scorecard.inrix.com/scorecard).

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

ITE (1997), A Toolbox for Alleviating Traffic Congestion and Enhancing Mobility, Institute of Transportation Engineers (www.itsdocs.fhwa.dot.gov/jpodocs/repts_te/5dz01!.pdf).

ITS (2007), USDOT ICM Resource Compendium, Intelligent Transportation Systems, USDOT (www.its.dot.gov/icms/compendium.htm), provides information on Integrated Corridor Management programs, which include various strategies to reduce congestion and improve travel reliability.

John N. LaPlante (2007), “Strategies for Addressing Congestion,” ITE Journal, Vol. 77, No. 7 (www.ite.org), July 2007, pp. 20-22.

David Levinson and Ajay Kumar (1997), “Density and the Journey to Work,” Growth and Change, Vol. 28, No. 2, pp. 147-72 (www.ce.umn.edu/~levinson/papers-pdf/doc-density.pdf).

Robin Lindsey (2007), Congestion Relief: Assessing The Case For Road Tolls In Canada, Commentary 248, C.D. Howe Institute (www.cdhowe.org).

Todd Litman (2001), “Generated Traffic; Implications for Transport Planning,” ITE Journal, Vol. 71, No. 4, Institute of Transportation Engineers (www.ite.org), April, 2001, pp. 38-47; at www.vtpi.org/gentraf.pdf.

Todd Litman (2003), Evaluating Criticism of Smart Growth, Victoria Transport Policy Institute (www.vtpi.org).

Todd Litman (2004), Comprehensive Evaluation of Rail Transit Benefits, Victoria Transport Policy Institute (www.vtpi.org).

Todd Litman (2005), “Congestion Costs,” Transportation Cost and Benefit Analysis; Techniques, Estimates and Implications, Victoria Transport Policy Institute (www.vtpi.org/tca).

Todd Litman (2006a), Smart Congestion Reductions: Reevaluating The Role Of Highway Expansion For Improving Urban Transportation, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/cong_relief.pdf.

Todd Litman (2006b), Smart Congestion Reductions II: Reevaluating The Role Of Public Transit For Improving Urban Transportation, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/cong_reliefII.pdf.

Herbert Mohring (1999), “Congestion,” in Essays in Transportation Economics and Policy: A Handbook in Honor of John R. Meyer, (José A. Gómez-Ibáñez, William B. Tye, Clifford Winston, editors) Brooking Institution (www.brookings.edu), pp. 181-222; at (http://brookings.nap.edu/books/0815731817/html/181.html).

NALGEP (2005), Clean Communities on the Move: A Partnership-Driven Approach to Clean Air and Smart Transportation, National Association of Local Government Environmental Professionals (NALGEP), (www.nalgep.org).

Nelson\Nygaard (2006), Traffic Reduction Strategies Study, Report and various appendices, City of Pasadena (www.cityofpasadena.net); at www.cityofpasadena.net/councilagendas/2007%20agendas/Feb_26_07/Pasadena%20Traffic%20Reduction%20Strategies%2011-20-06%20DRAFT.pdf and www.cityofpasadena.net/councilagendas/2007%20agendas/Feb_26_07/Appendix_A_Case%20Studies%2012-1-2006%20DRAFT.PDF.

PSRC (2005), Travel Choices Study, Puget Sound Regional Council (http://psrc.org/projects/trafficchoices/index.htm). This federally funded pilot project tests the effects of pricing on residents travel behavior.

PTI (2003), Unclogging Arterials: Prescriptions for Relieving Congestion and Improving Safety On Major Local Roadways, Public Technology Inc. for the Federal Highway Administration, FHWA-OP-03-069 (www.pti.org).

Gary Roth (2004), An Investigation Into Rational Pricing For Curbside Parking: What Will Be The Effects Of Higher Curbside Parking Prices In Manhattan?, Thesis Columbia University (www.urban.columbia.edu); at www.urban.columbia.edu/people/alumni/2004thesis_pdf/GRothThesis.pdf.

Kate Sanderson and Gary Davis (2002), Building Our Way Out of Congestion? Transportation Research Board 81st Annual Meeting (www.trb.org).

Francois Schneider, Axel Nordmann and Friedrich Hinterberger (2002), “Road Traffic Congestion: The Extent of the Problem,” World Transport Policy & Practice, Vol. 8, No. 1, (http://ecoplan.org/wtpp/wt_index.htm), January 2002, pp. 34-41.

Raheel Shabih and Kara M. Kockelman (1999), Effect of Vehicle Type on the Capacity of Signalized Intersections: The Case of Light-Duty Trucks, UT Austin (www.ce.utexas.edu/prof/kockelman).

STPP (1999), Why Are the Roads So Congested? An Analysis of the Texas Transportation Institute's Data On Metropolitan Congestion, Surface Transportation Policy Project (www.transact.org).

STPP (2001), Easing the Burden: A Companion Analysis of the Texas Transportation Institute's Congestion Study, Surface Transportation Policy Project (www.transact.org).

TransPriceProject (www.cordis.lu/transport/src/transpricerep.htm) is a European study of various pricing strategies for reducing urban traffic congestion and air pollution emissions.

Brian D. Taylor (2002), “Rethinking Traffic Congestion”, Access, Number 21, University of California Transportation Center (www.uctc.net), Fall 2002, p. 8-16.

Brian D. Taylor (2004), “The Politics of Congestion Mitigation” Transport Policy, Vol. 11, No. 3 (www.elsevier.com/locate/transpol), July 2004, pp. 299-302.

TRB (1994), Curbing Gridlock: Peak-Period Fees to Relieve Traffic Congestion, Transportation Research Board (www.trb.org).

TRB (1997), Quantifying Congestion; Final Report and User’s Guide, NCHRP Project 7-13, Transportation Research Board (www.trb.org).

TRB (2000), Highway Capacity Manual, Transportation Research Board (www.trb.org).

TTI (annual reports), Urban Mobility Study, Texas Transportation Institute (http://mobility.tamu.edu).

UCLA (2003), Traffic Congestion Issues and Options, UCLA Extension Public Policy Program (www.uclaextension.edu/unex/departmentalPages/publicpolicy/report.pdf).

USEPA (1996), Technical Methods for Analyzing Pricing Measures to Reduce Transportation Emissions, USEPA Report #231-R-98-006, (www.epa.gov/clariton).

USEPA (2004), Characteristics and Performance of Regional Transportation Systems, Smart Growth Program, US Environmental Protection Agency (www.epa.gov/smartgrowth/performance2004final.pdf).

Glen Weisbrod, Donald Vary and George Treyz (2001), Economic Implications of Road Congestion, National Cooperative Highway Research Program, Report 463, Transportation Research Board (http://gulliver.trb.org/publications/nchrp/nchrp_rpt_463-a.pdf).

Wikipedia (2008), “Level of Service,” Wikipedia (http://en.wikipedia.org/wiki/Level_of_service).

Clifford Winston and Ashley Langer (2004), The Effect of Government Highway Spending on Road Users’ Congestion Costs, Brookings Institute (www.brookings.edu).

WSDOT (2006), Congestion Relief Analysis: For the Central Puget Sound, Spokane & Vancouver Urban Areas, Washington State Department of Transportation (www.wsdot.wa.gov/mobility).

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

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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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