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
August 27, 2007
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). 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
Table 1 Highway Speed, Flow and Density (Homburger, Kell and Perkins, 1992)
|
LOS |
(mph) |
|
(veh./mile) |
|
A |
Over 60 |
Under 700 |
Under 12 |
|
B |
57-60 |
700-1,100 |
12-20 |
|
C |
54-57 |
1,100-1,550 |
20-30 |
|
D |
46-54 |
1,550-1,850 |
30-42 |
|
E |
30-46 |
1,850-2,000 |
42-67 |
|
F |
Under 30 |
Unstable |
67-Maximum |
This table shows the speed, flow and density of traffic under each Level of Service (LOS) rating, a standard measure of traffic congestion.
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
“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
|
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
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.
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 time frame used for analysis can significantly affect the evaluation of congestion reduction strategies. Figure 1 compares how road widening and transit improvements affect congestion. If no project is implemented, traffic volumes increase to equilibrium, when congestion delays discourage further growth in peak-period vehicle trips. 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 (Rebound Effects). 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 (see box below). 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.
Figure 1 Road Widening and Transit/HOV Improvement Congestion Impacts
Adding a general traffic lane causes an immediate reduction in congestion, but this declines over time, resulting in little long-term benefit. Grade separated transit or HOV Priority provides relatively little short-term congestion reduction, but greater benefits over the long-term as these modes become relatively attractive to peak-period travelers.
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.
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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.
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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 · 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). |