Congestion Reduction Strategies
Identifying and Evaluating Strategies To Reduce Traffic Congestion
~~~~~~~~~~~~~~
Victoria Transport Policy Institute
~~~~~~~~~~~~~~~~~~~~
Updated 10 May 2011
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.
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 2009). 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, 2009). 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.
The INRIX Corporation (2009) uses a “Smart Dust Network” of GPS-enabled vehicles which report roadway travel conditions to evaluate highway traffic congestion. Their 2008 annual report indicates that U.S. traffic congestion decreased nearly 30% from 2007 to 2008, apparently due to a 4% reduction in total traffic volumes. The study concludes:
Demand management can have sizeable impact on congestion, even if total volume changes are modest. Massive increases in fuel prices had effects similar to policy initiatives under consideration such as variable pricing, managed lane strategies and better travel information. When a road network is at capacity, adding or subtracting even a single vehicle has disproportionate effects for the network. This phenomenon has been well known for a long time, but this data illustrates it in real-world terms on a nationwide basis.
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, 2009)
|
Indicator |
Description |
Considers 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.
The following TDM strategies tend to be particularly effective at reducing traffic congestion.
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 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 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 can be effective congestion reduction strategies, particularly if implemented with other incentives to shift mode, such as HOV Priority and Road Pricing. Public transit service that attracts a sufficient number of travelers who would otherwise drive on congested roads can reduce the point of equilibrium, reducing total congestion delays, as discussed in the box on the following page. To reduce traffic congestion, transit services must:
· Serve a major share of major urban corridors and destinations.
· Offer high quality service (relatively convenient, fast, frequent and comfortable) that is attractive to peak-period travelers.
· Be grade separated (with bus lanes or separated rail lines), so transit travel is relatively fast compared with driving under congested conditions.
· Be relatively affordable, with low fares and discounts targeted at peak-period travelers.
Special care is needed to accurately evaluate transit congestion reduction impacts (“Congestion Costs,” Litman 2003; Aftabuzzaman, Currie and Sarvi 2010 and 2011). Some congestion indicators, such as roadway level-of-service or a travel time index, measure the intensity of congestion experienced by motorists and fail to account for the congestion delays avoided by travelers who shift to alternative modes, or from more compact development that reduces travel distances. It is therefore better to evaluate congestion using per capita congestion costs.
Smart Growth tends to increase traffic congestion intensity (the delay that motorists experience when driving during peak periods) but tends to reduce per-capita Congestion delays because residents drive less and take shorter trips. Compact development supports Road Pricing. The Traffic Choices Study found the elasticity of Seattle-area home-to-work vehicle trips to be approximately -0.04 (a 10% price increase causes a 0.4% reduction in automobile commutes), but increased to -0.16 (a 10% price increase causes a 1.6% reduction in automobile commute trips) for workers with the 10% best transit service (PSRC 2008). Similarly, the Oregon Road User Fee Pilot Program, which rewarded motorists for avoiding congested conditions, found that households in denser, mixed use, transit-accessible neighborhoods reduced their peak-hour and overall travel significantly more than comparable households in automobile dependent suburbs, and that congestion pricing increase the value of more accessible and multi-modal locations (Guo, et al. 2011). These indicate that high quality public transit service significantly reduces the price (road toll or parking fee) required to achieve a given reduction in traffic congestion, a reflection the smaller incremental cost to travelers (less consumer surplus loss) when they shift from driving to high quality public transit.
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 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 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 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. |
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 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 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 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)
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.
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 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.
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 congestion reduction strategies are described below.
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 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.
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 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.
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 can include changeable message signs, radio reports and Internet information about traffic conditions. These can reduce motorist stress by letting them anticipate conditions.
Ramp meters control the number of vehicles that can enter a highway ramp. This tends to maintain smoother traffic flow on highways.
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.
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). Murphy (2005) describes these narrow lanes from a highway engineering perspective.
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.
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.
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.
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 (ICCT 2010). 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.
· 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.
|
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. |
For more examples see Litman (2005) and ICCT (2010).
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.
New York City Department of Transportation (NYC DOT) sponsored a pilot program undertaken with the trucking industry found that trucks making off-hour deliveries between 7 p.m. and 6 a.m. instead of at peak hours experienced fewer delays, easier parking, reduced congestion and significant savings. The study found that businesses benefit significantly, with travel speeds improved as much as 75% and a sharp reduction in parking tickets and fines.
"New York is a city that never stops, and neither should its businesses," said Commissioner Sadik-Khan. "Time is money and this program is a signal to the entire industry that there’s an economic model for off-hour deliveries that also helps reduce congestion and pollution."
Freight deliveries into the borough exceed 100,000 daily, with 80% made to wholesale, retail and food enterprises. The pilot participants included a diverse group of eight delivery companies and 25 business locations who participated in the pilot for at least one month, including restaurants and retail stores. The project was funded with a $1.2 million dollar grant from the RITA and $640,000 from the project’s coordinator Rensselaer Polytechnic Institute (RPI). The pilot also included participation by Rutgers University, New York University’s Rudin Center for Transportation Policy and Management at NYU-Wagner and ALK Technologies.
"This is an excellent—and probably one of the most important and prominent—example of what can be accomplished when academia, the public sector and private companies join forces to tackle challenging goals such as achieving sustainable urban freight deliveries," said RPI’s Director of the Center for Infrastructure, Transportation and the Environment Professor Jose Holguin-Veras. "In essence, off-hour deliveries lead to reduced congestion and environmental pollution, increased competitiveness of the urban area and an increase in quality of life conditions. Everybody wins."
The pilot found that travel speeds from the truck depot to delivery drivers’ first stop in Manhattan improved by up to 75% compared to travel speeds during the evening rush hours, while subsequent trips averaged travel speeds up to 50% faster. With less competition for parking spaces accessible to the delivery location, trucks spent only 30 minutes stopped at the curbside making deliveries, instead of 100 minutes before the pilot. From beginning to end, delivery routes averaged 48 minutes faster during the pilot.
The project also focused on encouraging businesses to accept off-hour shipments through financial incentives and strategies to make the process easier, such as allowing "unassisted" deliveries—providing a key to the delivery team for a direct delivery or for delivery to a holding area, saving money for businesses that no longer had to have employees present to accept goods.
All participants saw savings during the four-month pilot, which ended in January. By having fresh food products waiting for them each morning, restaurants saw cost savings as staff were able to prepare food upon arriving rather than wait to assist in deliveries that are often delayed due to traffic congestion.
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:
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.
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.
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).
Md Aftabuzzaman, Graham Currie and Majid Sarvi (2010), “Evaluating the Congestion Relief Impacts of Public Transport in Monetary Terms,” Journal of Public Transportation, Vol. 13, No. 1, pp. 1-24; at www.nctr.usf.edu/jpt/pdf/JPT13-1.pdf.
Md Aftabuzzaman, Graham Currie and Majid Sarvi (2011), “Exploring The Underlying Dimensions Of Elements Affecting Traffic Congestion Relief Impact Of Transit,” Cities, Vol. 28, Is. 1 (www.sciencedirect.com/science/journal/02642751), February, Pages 36-44.
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.
Booz Allen Hamilton (2006), Study of Successful Congestion Management Approaches and the Role of Charging, Taxes, Levies and Infrastructure and Service Pricing in Travel Demand Management, Council Of Australian Governments (www.bitre.gov.au); at www.bitre.gov.au/publications/56/Files/Congestion_Management_Approaches.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).
Cambridge Systematics (2008), Performance Measurement Framework for Highway Capacity Decision Making, Strategic Highway Research Program (SHRP 2) Report S2-C02, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/shrp2/shrp2_S2-C02-RR.pdf.
Joe Cortright (2010), Driven Apart: How Sprawl is Lengthening Our Commutes and Why Misleading Mobility Measures are Making Things Worse, CEOs for Cities (www.ceosforcities.org); at www.ceosforcities.org/work/driven-apart.
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). 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).
Zhan Guo, et al. (2011), The Intersection of Urban Form and Mileage Fees: Findings from the Oregon Road User Fee Pilot Program, Report 10-04, Mineta Transportation Institute (http://transweb.sjsu.edu); at http://transweb.sjsu.edu/PDFs/research/2909_10-04.pdf.
HDR (2008), Moving at the Speed of Congestion - The True Costs of Traffic in the Chicago Metropolitan Area, Metropolitan Planning Council (www.metroplanning.org), at www.metroplanning.org/resource.asp?objectID=4476&keyword=figures+and+finding.
Jose Holguin-Veras, et al. (2010), Integrative Freight Demand Management In The New York City Metropolitan Area, Rensselaer Polytechnic Institute for the USDOT (www.transp.rpi.edu); at www.transp.rpi.edu/~usdotp/DRAFT_FINAL_REPORT.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).
ICCT (2010), Congestion Charging: Challenges and Opportunities, The International Council on Clean Transportation (www.theicct.org); at www.theicct.org/programs/climate_change/congestion_charging_paper_.
INRIX (2008), The Impact of Fuel Prices on Consumer Behavior and Traffic Congestion, INRIX (http://scorecard.inrix.com/scorecard).
INRIX (2009), National Traffic Scorecard Annual Report, INRIX (http://scorecard.inrix.com/scorecard); at http://scorecard.inrix.com/scorecard/pdf/INRIX%20NTSC08%20Report%20-%20low%20res.pdf.
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).
David Lewis (2008), America’s Traffic Congestion Problem: A Proposal for Nationwide Reform, Brookings Institute (www.brookings.edu); at www.brookings.edu/papers/2008/07_congestion_pricing_lewis.aspx.
Robin Lindsey (2007), Congestion Relief: Assessing The Case For Road Tolls In Canada, Commentary 248, C.D. Howe Institute (www.cdhowe.org).
Todd Litman (1996), “Using Road Pricing Revenue: Economic Efficiency and Equity Considerations,” Transportation Research Record 1558, TRB (www.trb.org), pp. 24-28; available at www.vtpi.org/revenue.pdf.
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); at www.vtpi.org/sgcritics.pdf.
Todd Litman (2004), Rail Transit in America: Comprehensive Evaluation of Benefits, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/railben.pdf.
Todd Litman (2005), London Congestion Pricing: Implications for Other Cities, Victoria Transport Policy Institute (www.vtpi.org); available at www.vtpi.org/london.pdf.
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; summarized in “Evaluating Rail Transit Benefits: A Comment,” Transport Policy, Vol. 14, No. 1 (www.elsevier.com/locate/tranpol), January 2007, pp. 94-97.
Todd Litman (2009), “Congestion Costs,” Transportation Cost and Benefit Analysis; Techniques, Estimates and Implications, Victoria Transport Policy Institute (www.vtpi.org/tca).
Todd Litman (2009), Are Vehicle Travel Reduction Targets Justified? Evaluating Mobility Management Policy Objectives Such As Targets To Reduce VMT And Increase Use Of Alternative Modes, VTPI (www.vtpi.org); at www.vtpi.org/vmt_red.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).
William Murphy (2005), “Using Narrow Freeway Lanes to Mitigate Transportation-Related Problems, Public Works Management & Policy, Vol. 9, No. 3, pp. 190-195 (http://pwm.sagepub.com/cgi/content/abstract/9/3/190).
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 (2008), Traffic Choices Study: Summary Report, Puget Sound Regional Council (http://psrc.org); at http://psrc.org/assets/37/summaryreport.pdf. 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).
RAND (2008), Moving Los Angeles: Short-Term Transportation Policy Options for Improving Transportation, Rand Corporation (www.rand.org); at www.rand.org/pubs/monographs/2008/RAND_MG748.pdf.
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).
Sam Staley and Adrian Moore (2009), Mobility First: A New Vision for Transportation In A Globally Competitive Twenty-First Century, Rowman and Littlefield.
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).
Tom Vanderbilt (2008), Traffic: Why We Drive The Way We Do (And What It Says About Us), Vintage (www.howwedrive.com).
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).
Wilbur Smith (2008), Traffic & Transportation Policies and Strategies in Urban Areas in India, Ministry of Urban Development (www.urbanindia.nic.in); at http://urbanindia.nic.in/moud/programme/ut/Traffic_transportation.pdf.
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).
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.
Victoria Transport Policy Institute
www.vtpi.org info@vtpi.org
1250 Rudlin Street, Victoria, BC, V8V 3R7, CANADA
Phone & Fax 250-360-1560
#96