Transportation system management (TSM) refers to a set of strategies that largely aim to reduce GHG emissions by reducing congestion, primarily by improving transportation system capacity and efficiency. TSM strategies may also address a wide range of other externalities associated with driving such as pedestrian/driver safety, efficiency, congestion, travel time, and driver satisfaction. Some TSM strategies are designed to reduce total and systemic congestion and improve system-wide efficiency, while other strategies target particularly problematic areas where improvements could greatly affect congestion, safety, efficiency, and GHG emissions.
Transportation System Management Strategies Reviewed in This Report
This review covers the following eight reduction strategies:
The above strategies seek to reduce congestion and promote efficiency through infrastructure, operational, and technological improvements. Infrastructure strategies seek to reduce GHG emissions by improving the transportation system infrastructure through new or improved construction. The production of pavement materials requires significant amounts of energy and produces significant GHGs. "Green" construction materials are lower-energy alternatives to conventional construction materials and can reduce the life-cycle GHG emissions of transportation construction projects. Resurfacing roads decreases the roughness of road surfaces and allows vehicles to travel more efficiently, which in turn reduces GHGs. Capacity expansion and roundabouts may reduce congestion and allow for more free-flowing traffic with less stoppage and idling time.
Operational strategies focus on minimizing inefficient travel that increases GHG emissions. Proper incident management strategies detect and clear incidents to reduce congestion and promote safer post-accident operations. Speed enforcement and reduction programs seek stricter enforcement of speed regulations as well as lower limits so that travel speeds coincide with speed ranges that promote optimal fuel efficiency.
Technological strategies seek to use automated systems to optimize free-flow of traffic and thus reduce non-VMT production of GHGs. Traffic signal optimization improves the operation, maintenance, timing and location of traffic signals to promote smoother traffic flow and reduce GHG emissions. Ramp metering controls the rate of vehicles entering freeways to reduce congestion around ramps and discourage use of highways/freeways for short trips.
Current FHWA research efforts address the short- and long-term impact of highway operations on travel and GHG emissions. Strategies of particular interest include signal timing, ramp metering, incident management, speed harmonization, and congestion pricing. The travel behavior component of this work will examine key factors affecting travelers' responses to these treatments, such as demand changes from changes in travel time, travel time variability, and travel cost. An important outcome of this research will be to characterize what we know about travel in the months and years following implementation of these strategies. Ultimately, this work will support travel experiments to estimate the network-level travel and GHG impacts of individual and bundled highway measures.
States, regions, counties and municipalities have implemented many TSM strategies over the years because they address a wide range of externalities associated with transportation operations. Recently, TSM strategies have been proposed as a way to address GHG emissions in particular.
However, the literature on the GHG effects of the TSM strategies reviewed in this study is largely inconclusive. There are several reasons for this. First, most TSM strategies seek to improve the efficiency or capacity of the transportation system, which enables people to travel at higher speeds and with less congestion, reducing the time and comfort costs of driving. Reducing the cost of driving also induces demand, which may not be characterized in studies of GHG effects. For example, some research on capacity expansion has shown that it significantly induces demand and may result in a net increase in GHG emissions. This may be particularly so in growing areas where capacity increases attract further development or change development patterns. This suggests that TSM strategies should be closely coordinated with urban planning and other land use concerns. Other TSM strategies that one would expect to be affected by induced demand are road resurfacing, traffic signal optimization, and incident management, though it may be less significant than for capacity expansion.
Second, many TSM strategies seek to improve system performance through construction projects. This includes road resurfacing, roundabout construction, and capacity expansion. The construction process itself emits significant GHGs, and the life-cycle emissions are largely not taken into account in the literature. For example, research suggests that road resurfacing emits 9.7 tons of CO2 per lane mile for the construction alone (see discussion of road resurfacing) which can reduce or negate the fuel efficiency benefits of a smoother road, particularly when coupled with the higher speeds that smooth roads enable. Constructing new capacity can produce several thousand tons of CO2 per lane mile. Without knowing the life-cycle emissions, one cannot know the GHG reductions that are possible.
Induced demand and unaccounted-for life-cycle emissions are both examples of unintended GHG emissions that arise from these strategies. There are other kinds of unintended GHG emissions as well. As noted, some of these strategies enable faster driving. At speeds higher than approximately 55mph, however, GHG emissions from vehicles increase significantly. This has been shown to offset some of the gains from improving flow, for example with ramp meters and road resurfacing. Additionally, while ramp metering improves flow on highways, it increases idling at the ramp, which also produces GHGs. These unintended emissions also reduce the effectiveness of these strategies.
Because of these factors, the evidence for road resurfacing, capacity expansion, roundabouts, and ramp metering is mixed - some studies show a reduction in emissions while others show an increase in emissions. This is not to suggest that they should never be used; in some cases, they may indeed be effective at reducing GHG emissions. For example, targeted capacity expansion at existing bottlenecks could reduce congestion, with limited induced demand. Rather, the evidence speaks to a need for carefully assessing all of the sources of GHG emission reductions and increases, both intended and unintended.
Two strategies may be particularly promising in reducing GHG emissions and counteract sources of unintended emissions. The reduction and stricter enforcement of highway speed limits could significantly reduce GHG emissions because they encourage drivers to travel at more fuel-efficient speeds, typically between 45 and 55 mph. Their effect on congestion will vary depending on the context. Where congestion is increased or travel is delayed, GHG emissions may increase because of stop-and-go traffic but may also decrease because of reduced demand. Where congestion decreases, the inverse may be true. Research suggests that speed reductions overall are likely to reduce GHG emissions. Speed reduction is likely to interact positively with other TSM strategies that would otherwise encourage faster speeds. This strategy would, however, require active support from state DOTs, DMVs, law enforcement officials, communities, and drivers, and may be unpopular among drivers who are accustomed to driving at high speeds.
The use of "green" construction materials could also significantly reduce GHGs relative to other strategies because it offsets the use of GHG-intensive materials that would otherwise be used, without affecting capacity, efficiency, or speed that would induce demand. Using green construction materials in other construction-based GHG mitigation strategies, such as road resurfacing or capacity expansion, would help to reduce the life-cycle emissions from these strategies and may be necessary in some cases to achieve net reductions.
Like transportation demand management strategies, TSM strategies have the most significant effect on GHGs when the emissions from driving are high. For example, as vehicle engines improve to be more efficient at a wider range of speeds, the absolute GHG effect of speed reduction programs will decline. This implies that the effectiveness of TSM strategies may decline as vehicles and fuels improve. All of these strategies are important in combating climate change, but their combined effect will be less than the sum of their individual effects. Additionally, TSM strategies may undermine the effectiveness of TDM strategies by reducing the cost of driving and therefore inducing demand. The exception is fuel taxes and road pricing which would counteract the induced demand from improved system performance because they make driving more expensive and reduce VMT.
Finally, TSM strategies have common co-benefits and negative effects. Strategies that encourage faster driving such as traffic signal optimization may reduce safety for pedestrians, cyclists, and also drivers. Those that calm traffic, such as speed reduction programs and roundabouts, may improve safety. Strategies that decrease travel times and congestion, or improve driving comfort, are likely to improve driver satisfaction. Those that improve the efficiency of the system may increase travel time reliability or satisfy greater demands without the need for added capacity, which is both costly and may induce demand still further.
Policy: Traffic signals can increase stop-and-go driving, causing aggressive acceleration and deceleration, congestion, and excess idling, all of which reduce fuel efficiency and increase GHG emissions. Traffic signal optimization is the process of improving the operations, maintenance, timing, and location of traffic signals to promote smoother traffic flow, which simultaneously reduces GHG emissions.
Emissions Benefits and Costs: Where traffic signal optimization has been implemented and studied, the literature shows 3-12% fuel savings and GHG emissions at signalized intersections. These results must be interpreted cautiously, however, because, like other strategies that improve traffic flow, signal optimization may induce demand and reduce the stated benefits, something not clearly accounted for in the studies cited. The literature also suggests that when optimization is undertaken at intersections that are already signalized, costs may range from $25 to $34 per MTCO2.
Implementation Concerns: Signal optimization is often undertaken to improve traffic flow, and reductions in GHG emissions are seen as an added benefit. Given this, signal optimization is likely to be supported by the public. Coordination across jurisdictions may be necessary, but challenging, for large signal optimization projects.
Traffic signals can increase stop-and-go driving, causing sudden acceleration and deceleration, congestion, and excess idling, all of which reduce fuel efficiency and increase GHG emissions. Traffic signal optimization is the process of improving the operations, maintenance, timing, and locations of traffic signals to promote smoother traffic flow and mitigate these effects.
A key traffic signal optimization tactic is the coordination of signals (i.e., the length of green and red signals and the timing of signal changes) in a corridor to maximize green light time for vehicles traveling at the speed limit. This creates smoother traffic along the corridor. Such optimization can be static or dynamic. In static optimization, signals are timed to operate according to a fixed schedule, while dynamic optimization uses real-time traffic data to adapt signal timing.
Other traffic signal optimization tactics include:
The literature, and hence this review, focuses almost entirely on signal coordination approaches to traffic signal optimization.
Traffic signal optimization is typically undertaken at the local and regional level and can involve the retiming of one intersection or the coordination of signals at multiple intersections. MPOs typically coordinate signal optimization projects, while DOTs primarily provide funding, and local jurisdictions implement and maintain the lights. Many local agencies may need to coordinate when signal optimization projects span multiple jurisdictions.
This strategy affects roads with signalized intersections and indirectly affects the behavior of drivers.
Traffic signal optimization benefits (including emissions and fuel reductions) have been studied since the early 1980s. Earlier system models primarily estimated fuel savings from decreases in congestion. More recently, models have evolved to take into account the effects of reduced acceleration and deceleration to provide a more complete estimate of the effects on fuel consumption and, therefore, GHG emissions. Although GHG reduction estimates appear to be consistent, most of the practical research has been conducted in large cities and the results cannot necessarily be generalized to other areas. In addition, the benefits of traffic signal optimization vary depending on a number of factors, including the previous level of congestion and traffic, and the specifics of the optimization approach. Finally, the improvement in traffic flow may induce demand, which is not explicitly taken into account in most studies, but which may reduce the benefits of signalization.
A few studies report the fuel consumption effects per signalized intersection:
Other studies, dating as far back as the 1980s, offer project-wide estimates of fuel or CO2 savings:
A few regional studies reported a percentage of average fuel savings for a series of intersection improvements. While this is equivalent to the percent CO2 savings, it does not inform us about absolute CO2 savings:
Traffic signal optimization costs include hardware (traffic detectors and new signal equipment, such as solid state electronic controllers), maintenance, signal timing plans, and remote capabilities to manage signals. Recent estimates show that optimization of existing signals costs between $2,600 and $4,000 per intersection (NTOC, 2007; Kittelson and Associates, 2008; Sunakari, 2009). Considering that traffic signals should be retimed every three years (NTOC, 2007), this results in approximate costs of $1,000 to $1,300 per year per intersection. The annual CO2 reduction estimates of 37, 39, and 115 metric tons per intersection observed in the literature suggest costs from between $8 and $35 per metric ton of CO2. This does not consider the societal benefits from signal optimization, including fuel savings to drivers, costs of delay, and other potential savings. However, this also does not consider the costs of installing new traffic signals, which can range from $86,600 to $202,000 (Sorensen et al., 2008).
There are several sources of uncertainty in analyzing traffic signal optimization. First, efficiencies in the system may induce greater demand for vehicular traffic and create a rebound effect, which would not be reflected in short-term studies of intersection performance. The CCAP guidebook (2006) assumes a 20% rebound effect, and most studies do not articulate the effects of induced demand from signal optimization (these include Boillot et al., 1992; Boillot et al., 2000; Midenet, et al., 2004; Pandian, et al., 2009; Rahka at al., 2000). It would be possible for an area considering this strategy to review historical traffic count data for "before" and "after" conditions in corridors where signal optimization has been applied in the past. However, such analysis would also need to account for exogenous factors such as changes in population and employment, fuel prices, and diverted demand from other facilities.
Second, estimates and models simplify patterns of fuel consumption and vehicle travel and cannot account for the wide array of vehicles in operation. Even basic differences between vehicles, such as make and age, are simplified in models, affecting the outcomes of the studies (Stevanovic, 2009).
Third, an important aspect of the cost/benefit analysis is whether it includes travel delay costs. Considering delay costs and fuel savings in the analysis increases the cost effectiveness of this strategy (since signal optimization reduces delay and fuel consumption). One study estimated that signal coordination would save 19.6 million hours of delay and save $418,751,000 if implemented in all 429 urban areas (Schrank and Lomax, 2009).
A variety of software can aid in implementing signal optimization across several intersections. Older techniques such as TRANSYST (Roberston, 1969) optimize signals by pre-timing them. New techniques (e.g., CRONOS or the Sydney Coordinated Adaptive Traffic System (SCATS)) use real time data to "match" the current traffic conditions to the "best" pre-calculated off-line timing plan (Yu and Recker, 2006).
Similarly, a variety of commercial modeling systems can be used to determine delay, fuel consumption, and emissions from signalized intersections. These include, for example, SYNCHRO and TRANSYT-7F (for traffic flow), VISSIM-CMEN-VISCAOST (for scenario based fuel consumption), aaMOTION (a single vehicle software package for modeling fuel, emissions, and costs), PASSER II (measures cycle lengths in algorithm to estimate delay) and aaSIDRA (an intersection analysis software package). If changes in vehicular travel activity can be measured or modeled, EPA's MOVES model can also be used to estimate changes in emissions.
Transportation and public works agencies (local, regional, and state levels) use local, regional, state, and federal funds to undertake traffic optimization. Major costs include hiring specialists internally or as consultants to implement traffic optimization plans, and obtaining software and signalization technology.
Certain technologies such as fiber-optic networks to relay real-time traffic information may be costly, yet overall most traffic signalization projects are not considered very expensive-$2,600 to $4,000 per intersection (NTOC, 2007; Sunakari, 2009)-assuming that modern signals already exist at the intersections in question. However, according to the Institute of Transportation Engineers, signal re-timing should be considered no less than every three years-and preferably every year-to take into account new traffic patterns and demands (Sunakari, 2009). Overall, signal optimization technology has been proven, the cost is relatively low, and agencies are familiar with the implementation methods (TTI, 2007). These costs are for optimization of the signals and not the implementation costs of installing new signals.
In areas where corridors span several jurisdictions (e.g., city or county lines), achieving effective signalization may require agreements and coordination between multiple agencies.
There is likely to be little opposition to these programs given the added benefits of reduced congestion and shorter travel times (Sorensen et al., 2008).
Signalization may raise concerns about the negative impacts on pedestrian/bike crossings if longer green lights lead to shorter and fewer crossings opportunities.
The most obvious co-benefit is travel time savings due to smoother traffic flow and costs savings due to lower fuel consumption.
If a traffic signal optimization project reduces the pedestrian and bike crossing time to improve motor vehicle flow, pedestrians and bicyclists' usability of such crossings could decrease, potentially limiting the walkability and bicycle-friendliness of the area.
Use is widespread, although in most places the impetus is more likely improvements in traffic flow than emissions reduction. Some specific examples include:
It would be beneficial to address the many uncertainties associated with traffic signal optimization, particularly induced demand caused by improved mobility, to account for the full effect of these projects.
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Policy: Ramp meters control the rate of vehicles entering the freeway in order to create more space between vehicles so that they do not collide or disrupt the highway traffic flow. They reduce congestion on the freeway but increase idling on the ramp, both of which affect fuel consumption and CO2 emissions.
Emissions Benefits and Costs: The benefits from ramp metering to reduce GHG emissions are uncertain. Some studies report decreases in CO2 emissions, primarily from smoother traffic, while others report increases, in part because of idling at meters. Costs also range widely. One study estimated approximately $267,000 per ramp; another estimated that it annually costs $7,650 per ramp, which includes installation, maintenance, and operational costs. The costs per unit of GHG reduction cannot be estimated.
Implementation Concerns: Ramp metering may be expensive, and the public may oppose it due to delays at the ramp and perceptions of inequity.
Ramp meters control the rate of vehicles entering the freeway in order to create more space between vehicles entering the freeway so that those vehicles do not collide or disrupt the highway traffic flow. This is achieved through the use of queue detectors and traffic signals at freeway on-ramps to allow only one vehicle to enter the freeway per some short time interval (e.g., every five seconds). Ramp meters allow freeways to accommodate more vehicles with fewer collisions and greater reliability (TTI, 2007). They also reduce the number of entering vehicles by encouraging drivers to use parallel streets for short distance trips in order to avoid the ramp wait time (Cambridge Systematics, 2001). In these ways, ramp meters typically reduce congestion on the freeways.
The effects of ramp meters on fuel consumption and emissions are unclear. The reduced congestion on the freeway allows for greater fuel efficiency and reduced emissions once on the throughway. However, the decrease in congestion may increase speeds and induce demand. Vehicles idling at ramp meters and then accelerating from a full stop also have increased rates of fuel consumption and emissions. In addition, most ramp meters react to, rather than predict, bottlenecks (Pearson et al., 2003); the time delay between detection and corrective action can cause traffic fluctuations. All of these factors may reduce or negate GHG benefits from reduced congestion.
Ramp meters may be installed by local/municipal, regional (MPO), and state transportation agencies.
Ramp meters affect highway traffic, and may also affect local traffic by re-routing some trips to local roads.
While there are several studies of the effects of ramp metering on congestion and safety, only a few studies consider the impacts of ramp meters on fuel consumption and CO2 emissions. Moreover, these studies have varying results. Some practical studies of ramp metering systems have confirmed that ramp meters increase fuel use and GHG emissions, while other studies, including theoretical research, have found that ramp meters can decrease fuel consumption (Bogenberger et al., 2001; Piotrowictz and Robinson, 1995; Oregon DOT, 1982).
The literature on the effects of ramp meters varies due in part to differences in what is actually accounted for in the studies; for example, whether idling at ramps, induced demand, or increased speeds from improved traffic flow are considered.
Collectively, this literature suggests that CO2 effects are ultimately unknown but may vary greatly, from positive to negative net effects.
No estimated costs per metric ton of CO2 were reported in the literature. Moreover, this cannot be reliably calculated given that few studies report costs of ramp meters and that CO2 effects are uncertain and vary greatly, from both positive to negative net effects.
Ramp meters affect fuel consumption in ways that are often not included in calculations or models, including:
As a result, the effectiveness of ramp meters as a mitigation strategy is unknown.
Traditionally, ramp meters use fixed-time or traffic-responsive algorithms, which not only have the same features as fixed-time meters, but also have some ability to adapt to current conditions. Increasingly, sophisticated system-wide adaptive ramp metering (SWARM) algorithms that account for real-time traffic conditions are being used (Ahn, et al., 2007), although this requires a computerized communication center to calculate real-time adjustments along with a communication system to relay adjustments back to ramp meters (Sorensen, 2008).
One limitation is that most modeling techniques have not been capable of capturing off-cycle conditions (e.g., hard accelerations) and, in turn, have been unable to accurately analyze the air quality impacts of many traffic management strategies, including ramp metering (Guensler et al., 2001). If changes in vehicular travel activity due to ramp metering can be measured or modeled, EPA's MOVES model can be used to estimate changes in emissions.
Transportation and public works agencies at local, regional, and state levels use local, regional, state, and federal funds to implement ramp meters. Costs include the participation of traffic engineers and ITS specialists to implement traffic optimization plans, as well as technology costs such as fiber-optic networks to relay real-time traffic information.
Costs to the implementing agency include the ITS costs of model calibration, infrastructure implementation, and maintenance. More extensive ramp metering systems have a centralized control center. The cost of a particular ramp metering system varies widely according to the sophistication of the algorithm used to set the metering rate and the number of ramps included in the system (Pearson et al., 2003).
One example of capital costs for a proposed ramp metering system in the northern San Joaquin Valley of California in Stanislaus, California shows a range between $50,000 (where other interchange improvements were already being conducted) and $267,000 per ramp, which includes design, construction (including ramp improvements), installation, and technology (DKS Associates, 2008). On average, the cost to construct ramp meters was $155,000 per ramp for the 212 ramps in the system.
The Minnesota DOT also conducted a cost analysis of ramp meters. Ramp meters were estimated to cost approximately $3.2 million per year for the 430 ramp meters, or about $7,500 per year per ramp meter (Cambridge Systematics, 2001). This is an annualized cost, which enabled the study to compare cost to annualized benefits (including hours saved in travel time, reliability, fatalities, property damage and emissions). Their analysis suggested a cost-benefit ratio of 5:1.
Most ramp metering systems have been implemented by partnerships between state and regional/municipal agencies. The cases reviewed did not reveal any significant institutional barriers or inter-agency concerns.
Implementation of ramp metering is often initially opposed by the public because of increased queues at on-ramps (FHWA, 2006), undesirable levels of traffic diversion to surface streets, and increased emissions and fuel consumption at ramps (Pearson et al., 2003).
Ramp meters may also produce benefits for suburban motorists at the expense of those that live within areas that have ramp meters. This perception of inequity is based on the assumption that the suburban motorist lives outside a metered area and therefore is not delayed by ramp meters when entering a freeway (Nevada DOT, 2006).
Campaigns to educate the public on the benefits of ramp metering have helped public acceptance of ramp meters. Without these education campaigns, ramp meters may be viewed as costly and ineffective, which may lead to problems with receiving funding for ramp metering systems (Nevada DOT, 2006). In addition to this initial opposition, equity issues may arise due to the fact that ramp metering often benefits longer trips rather than shorter ones (Pearson et al., 2003).
Co-benefits may include:
Ramp metering may create long waits to enter freeways, which can divert traffic to alternative routes. While this benefits freeway traffic, it can increase traffic on these alternative routes, which may have other negative effects.
Use is widespread, although in most places the impetus is more likely improvements in traffic flow than emissions reduction. Some specific examples include:
Further research is required to determine if ramp metering is actually a viable GHG reduction strategy. Such research needs to consider the effects of idling and acceleration at ramp meters, reduced congestion, increased speed on highways, and any other effects of ramp meters.
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Policy: The Texas Transportation Institute (2009) estimated that traffic incidents account for nearly 60% of the traffic delay experienced in the 50 largest U.S. cities. Incident management programs use patrols or ITS to quickly detect and clear traffic incidents, thereby reducing delays and congestion and, in turn, reducing fuel consumption and CO2 emissions.
Emissions Benefits and Costs: Studies of individual urban incident management programs across the U.S. show varying impacts on GHG emissions, with calculated reductions ranging from 2 MTCO2 to 23 MTCO2 per incident (compared to situations with no incident management). Costs are not well known and depend on the technology and approaches used. One report estimated a cost of $15 per MTCO2, which reflects the effects of reduced congestion and operating costs but not technology costs. It is unclear whether this accounts for induced demand.
Implementation Concerns: Incident management programs are generally acceptable given the time and fuel saving benefits they offer. They may require inter-agency coordination across jurisdictions and transportation facilities.
The Texas Transportation Institute (2009) estimated that traffic incidents account for nearly 60% of the traffic delay experienced in the 50 largest U.S. cities. In 1998, incident-related congestion delay in the 10 most congested U.S. urban areas ranged from 218,000 to 1,295,000 person-hours. The additional fuel consumed during the same period in these areas because of incidents alone ranged from 214 to 1447 million liters (56.5 to 382.3 million gallons) (PB Farradyne, 2000). This translates to between 0.5 and 3.4 million MTCO2.
Incident management is the process of quickly detecting and clearing incidents in order to reduce delays and congestion. Because incident management can reduce congestion, it may also reduce fuel consumption and GHG emissions. According to Schrank and Lomax (2009), there are a total of 272 incident management programs in the 439 U.S. urban areas. Incident management is one of five prominent types of operational treatments implemented to mitigate congestion. Other treatments include ramp metering (see the section on Ramp Metering), signal coordination (see the section on Traffic Signal Optimization), access management, and high-occupancy vehicle lanes (see the section on Ridesharing and HOV Lanes). It is estimated that these treatments collectively saved 308 million hours of driver delay caused by congestion in 2007; half of these savings are a direct result of incident management programs.
Agencies may implement incident management efforts through roadway service patrols and/or Intelligent Transportation Systems (ITS). Service patrols tour congested and/or high incident freeway sections to identify incidents and/or disruptions in the traffic stream and minimize their duration, thereby restoring full capacity to the facility and reducing risks of secondary crashes to motorists and injury to responders (PB Farradyne, 2000). ITS infrastructure, including dynamic message signs, computer-aided dispatch, and closed-circuit television, are used to detect accidents and sometimes help avoid accidents. In 2004, 32% of freeway miles in the U.S. were monitored by video to detect incidents, and 45% were covered by service patrols (USDOT, 2007).
The public sector or public-private partnerships run most of these programs. Incident management programs can be undertaken by local, state, or regional transportation agencies and involve the cooperation of law enforcement and emergency services. Transportation planning and programming agencies (e.g., MPOs) may also support incident management programs with funding. Some state DOTs are establishing incident response programs in collaboration with law enforcement agencies; DOTs and MPOs can work together with elected officials, police agencies, and city/county transportation agencies to undertake these programs and coordinate them among jurisdictions (PB Farradyne, 2000).
Incident management programs affect traffic flow on highways and major arterials where they are present. These programs have implications for both passenger and freight traffic.
While the bulk of incident management literature focuses on safety, congestion, and intelligent transportation systems, there is also research on the effects of incident management on fuel consumption and GHG emissions. Quantifiable benefits primarily include reduced incident clearance times (how long it takes to clear an accident), reduced crash frequency, and reduced delays. While many estimates have been made regarding the reduction of fuel consumption due to accident management programs, estimates on benefits for a per-incident basis vary greatly.
The effectiveness of a particular incident management program depends on numerous factors, including the number and type of incidents that occur in the region, the level of congestion that results, and the speed with which incidents can be cleared. Most studies do not explicitly account for induced demand and it is still unknown whether non-capacity expanding programs that improve travel time (e.g., ITS and operational strategies) actually induce demand (Neudorff, 2010). Data from the following programs illustrate the variation in absolute and per-incident effects.
The costs of incident management programs and per incident costs are likely to vary. The Florida Road Ranger Program cost $2,500,000 in 2005, equivalent to $2,760,000 in 2009 USD, which is approximately $93 per incident. Based on fuel savings of 707 gallons and 6.3 metric tons of CO2 per incident, this program costs approximately $15/MTCO2. Note that this reflects operational costs only and does not include start-up technology or data management center costs, which can be high.
Estimates of traffic delay reductions depend on prevailing traffic conditions (traffic volume, incident topology, and roadway characteristics) in the corridor or region where services are provided, and so are difficult to generalize across programs. In addition, data from these programs do not explicitly account for induced demand, which may reduce or negate the benefits.
Agencies' costs for incident management programs include service patrol (e.g., vehicles, staff) and ITS infrastructure (U.S. DOT, 2007). Certain technology costs can be high, such as fiber-optic networks to relay real-time traffic information. For example, in 2006 the Florida DOT District IV spent approximately $15.5 million on 55 CCTV cameras, 224 detectors, and 55 miles of in-ground fiber optic communications (USDOT, 2007). Costs of Transportation Management Centers depend on the design and size of the facilities. Annual costs of incident clearance programs can be as high as $21.3 million per year (2009 USD), such as the Los Angeles Metro Freeway Service Patrol program (RITA, 2006).
Incident management programs may require coordination across multiple jurisdictions (Johnson and Thomas, 2001). Programs may include a variety of actors and relationships across and within scales of government (e.g., municipal-municipal; municipal-state), which may complicate the implementation process.
Evaluations of incident management programs have shown that the public is supportive of these programs (PB Farradyne, 2000). However, public relations campaigns are necessary to maintain the high levels of support needed to protect the program from budget cuts and improve relationships among partnering agencies (USDOT, 2001).
Transportation planning agencies have recognized the severity of travel time reliability problems and have been choosing operational strategies such as incident management programs that focus on mitigating non-recurring traffic congestion and improving reliability (Cambridge Systematics, et al. 2005). As transportation agencies devote more resources to non-recurring traffic congestion, the funding for incident management programs may increase.
Co-benefits include increased safety and reduced delay, as found in the following studies.
Slightly more than half of major urban areas have incident management programs, although in most places the impetus is more likely improvements in traffic flow than emissions reduction. Some specific examples include:
There is very little fuel consumption/GHG research on other causes of non-recurring traffic congestion, such as work zones, weather, and special events. Since these causes of non-recurring congestion can be influenced by DOT/MPO action, it would be beneficial in a separate effort to study all causes of non-recurring congestion to determine the GHG effects.
Bertini, Robert, Rose, Michael, and El-Geneidy, Ahmed. (2005). Using Archived ITS Data Sources to Measure the Effectiveness of a Freeway Incident Response Program. Submitted for presentation 84th Annual Meeting of the Transportation Research Board January 9-13, 2005. http://tram.mcgill.ca/Research/Publications/CometTRB.pdf.
Cambridge Systematics, et al. (2005). Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation. Prepared for Federal Highway Administration.
Chang, G., Liu, Y., Lin, P., Zou, N and Point-Du-Jour, J. (2003). Performance Evaluation of CHART: Coordinated Highways Action Response Team, Year 2002 (Final Report). November 2003. Available: http://www.chart.state.md.us/readingroom/readingroom.asp.
City of Houston. (2007).Houston SAFEclear Program Overview. Available: http://www.houstontx.gov/safeclear/index.html.
COMSIS Corporation. (2006). CHART Incident Response Evaluation Final Report. Silver Spring, MD.
Fenno, D. and Ogden, M. Freeway Service Patrols: A State of the Practice. Transportation Research Record No. 1634, Transportation Research Board. Washington, D.C. 1998.
Florida DOT. (2005). Annual Report: Smart SunGuide TMC. http://www.smartsunguide.com/PDF/Annual%20Report%2006_JAN_31%20FINAL.pdf.
Jacobson, L., et al. (1992). Incident Management Using Total Stations, Seattle, WA.
Johnson, Christine and Thomas, Edward. (2001). Regional Traffic Incident Management programs: Implementation Guide. FHWA-OP-01-002. Intelligent Transportation Systems, USDOT, Washington DC.
Henk, Russel H., et al. (1997). Before-and-After Analysis of the San Antonio TransGuide System. Paper presented at the 76th Annual Meeting of the Transportation Research Board, Washington, DC, January 1997.
Minnesota Department of Transportation. (2002). Highway Helper 2002 Summary Report.
National Traffic Incident Management Coalition. (2006). Benefits of Traffic Incident Management. http://ntimc.transportation.org/Documents/Benefits11-07-06.pdf.
Nee, J. and Hallenbeck, M. (2001). Evaluation of the Service Patrol Program in the Puget Sound Region. Report WA-RD 518.1. FHWA, U.S. Department of Transportation, 2001.
Neudorff, Louis G. (2010). 'Moving Cooler' - An Operations and ITS Perspective. http://www.movingcooler.info/Library/Documents/Moving%20Cooler_ITS%20Perspective_Neudorff_Final_02202010.pdf.
Olmstead, T. (2001). Freeway management systems and motor vehicle crashes: a case study of Phoenix, Arizona, Accident Analysis and Prevention 33, 2001 pp. 433-447.
Farradyne, PB. November 2000. Traffic Incident Management Handbook. Prepared for the Federal Highway Administration Office of Travel Management. http://floridaapts.lctr.org/pdf/incident%20mgmt_handbook%20Nov00.pdf.
Pearson, R. (2003). Incident Management. Web document hosted by the Institute of Transportation Studies at the University of California at Berkeley and Caltrans http://www.calccit.org/itsdecision/serv_and_tech/Incident_management/incident_management_overview.html.
RITA. (2006). The Los Angeles County Metro budgeted $20.5 million for the 2005 service patrol program. http://www.itscosts.its.dot.gov/its/benecost.nsf/ID/ABC500E2613898FE852572CA004F43C1?OpenDocument&Query=State.
Schrank, David, and Lomax, Tim (2009). Texas Transportation Institute, Urban Mobility Report. The Texas A&M University System. http://www.sanangelompo.org/text_files/UMReport%202009%20WEB%20July%2009.pdf.
Skabardonis, A.; Noeimi, H. Petty, K.; Rydzewski, D.; Varaiya, P. and Al-Deek, H. (1995). Freeway Service Patrol Evaluation. PATH Research Report UCB-ITS-PRR-95-5 University of California, Berkeley.
USDOT (2001). Regional Traffic Incident Management Programs. FHWA-OP-01-002 http://ntl.bts.gov/lib/jpodocs/repts_te/13149.pdf.
USDOT (2007). Intelligent Transportation Systems for Traffic Incident Management. FHWA-JPO-07-001 http://ntl.bts.gov/lib/jpodocs/brochure/14288.htm.
Policy: A vehicle's speed affects its fuel consumption and, consequently, its GHG emissions. The optimal speed for most motor vehicles with internal combustion engines is approximately 45-55 mph, and traveling at higher speeds quickly increases fuel use. This policy seeks to reduce vehicle speeds on highways and throughways (and thus reduce GHG emissions) by lowering and/or enforcing speed limits.
Emissions Benefits and Costs: Speed reduction programs are estimated to increase fuel efficiency (and hence reduce emissions) by 2-15% depending on the actual speed reductions achieved. Costs are approximately $9 to $12 per ton of CO2 and consist mainly of enforcement costs.
Implementation Concerns: Motorists in most U.S. states are accustomed to speed limits of 65 mph or higher, coupled with a moderate margin for speeding. Official and public resistance across the country undermined national speed limit compliance and enforcement in past years and led to Congress's repeal of the 55 mph speed limit requirement in the 1995 National Highway Designation Act. Similar resistance could be a major obstacle to reinstating new, lower state or national speed limits.
A vehicle's speed affects fuel consumption and GHG emissions due to air resistance and engine design. The optimal speed for most motor vehicles with internal combustion engines is approximately 45-55 mph, and traveling at higher speeds quickly increases fuel use (American Association of State Highway and Transportation Officials, 2008; Center for Clean Air Policy, 2004). However, most highways and throughways in the U.S. currently have speed limits of 65 mph or higher, significantly less efficient than the optimal range. Therefore, speed limit reduction and speed control programs have been suggested as a GHG mitigation strategy.
The policy is to reduce highway speeds of 65 to 75 mph to 55 or 60 mph, with the aim of ultimately reducing actual driving speeds. This speed reduction could be implemented on a national, state, and/or highway level. Although legislatures control speed limits, law enforcement agencies are responsible for enforcing these limits, and state DOTs are responsible for changing speed limit signs. In addition, law enforcement agencies and state DOTs can provide information and analyses in support of effective speed management and can incorporate infrastructural and legislative features that discourage high speeds (Burbank, 2009).
This policy targets all highway and throughway travel.
Research shows that vehicles' fuel efficiency increases as speeds approach approximately 55 mph and then drops dramatically (FHWA, 2002), by between 1-2%, for each mile per hour (mph) traveled above 55 mph (Garcia, 1996; Center for Clean Air Policy, 2004). GHG emissions increase inversely, declining until about 55mph and then increasing at higher speeds.
There is substantial theoretical and practical research showing that a reduction in speed reduces GHG emissions. However, few studies have examined the efficacy of speed reduction programs (i.e., how a particular campaign or program at a particular level of enforcement affects driver behavior and achieves GHG reductions). The effects of such programs will vary and depend on the actual reduction in speed that occurs from the program, the number of vehicle miles traveled (VMT) at reduced speeds, and the fuel economy of the vehicles affected by the program. The studies highlighted below, a mix of U.S. and European research efforts, are some of the few that have examined the relationship between reduced speeds and fuel savings or GHG reductions, in theory or in practice:
Finally, the effects of reduced speeds on travel time and delays are unknown, but may also affect GHG emissions. Speed reduction in some places such as Rotterdam, Holland also reduced congestion and bottlenecks (European Environmental Agency, 2008), which could additionally reduce GHGs. In other places, reduced speeds could increase congestion or reduce capacity, which could increase GHGs. Alternatively, reductions in the speed of traffic can reduce total vehicle miles traveled by increasing travel time, resulting in a 2-5% reduction in vehicle travel in the initial years after implementation of a 10% decrease in speeds (CCAP, 2006). The limited research literature on this topic suggests that these effects have not been extensively studied and could vary significantly.
Only a few of the aforementioned studies include the inherent fiscal responsibilities of these programs, most of which stem from increased enforcement. The New York Greenhouse Gas Task Force estimated a cost of $12 per metric ton of carbon for increased enforcement. The Government of Alberta estimated an annual cost of $90 million for increased enforcement. This equals a cost of $9 per metric ton CO2 if projected reductions are actually achieved; this cost would increase if compliance is less and therefore GHG reductions are less.
The effect of speed reduction programs ultimately depends on whether and to what extent drivers reduce their speed in practice. This depends in part on how strongly the limits are enforced and on driving culture, since in many areas it is customary for drivers to travel faster than posted limits. It additionally depends upon how reduced speeds affect congestion and VMT, which may positively or negatively affect emissions.
Motor Vehicle Emissions Simulator (MOVES) is EPA's recommended tool for mobile source GHG emissions analysis. This model can be used to estimate the effects of speed changes on emissions. Many agencies interested in GHG emissions reduction strategies already use this model for other types of analysis, including transportation conformity for criteria pollutants.
The Comprehensive Modal Emissions Model (CMEM) is also sometimes used to determine the effects of speed on emissions. CMEM is a public domain model that can interface with a wide variety of transportation models (e.g., TRANSIM) and data sets (e.g., location, speed, and acceleration) in order to perform fuel consumption analysis (Barth and Boriboonsomsin, 2008).
The costs for speed programs are primarily from law enforcement and borne by law enforcement agencies. There are also small costs to transportation agencies for signs, data collection and other support costs. Some agencies (especially departments of motor vehicles) may also incur costs if they undertake public relations campaigns regarding the programs. The Government of Alberta estimated a total annual cost of $90 million for increased enforcement of its reduced speed-a total of $1.8 billion over 20 years (Government of Alberta, 2008). Winkelman and Dierkers (2003) estimate a cost of $1.4 million for a GHG reduction of 0.117 MMtCE.
Transportation and law enforcement agencies often work together to implement speed management programs. These programs usually cross local jurisdiction and sometimes cross regional boundaries, which requires inter-agency coordination. Another concern is that speed limit reduction policies are only effective with motorist compliance, and therefore enforcement is critical.
Speed limit reductions were historically set by each state and states may not be amenable to lowering their speed limits, particularly to a nationally-imposed level. Programs may also be unpopular if the public believes speed reduction will increase travel times. Whether speed reduction programs actually increase travel time is unknown; in the study of speed reduction in Rotterdam, the program actually improved congestion downstream, presumably decreasing travel time (European Environmental Agency, 2008).
No U.S. state has endorsed a state-wide speed limit reduction, though a few areas have done analyses of the GHG savings that speed limit reductions might yield. The Washington State Climate Action Team recommended a state-wide speed limit reduction (Garcia, 1996). So far, the WS DOT has reduced speed limits on select roads. The UK offers one example of a nationwide speed reduction program.
Further research is needed to determine the relationship between speed limit reductions and changes in travel time, congestion, and VMT to help assess the net effects of these programs.
American Association of State Highway and Transportation Officials (AASHTO) (2008, April). Primer on Transportation and Climate Change.
Barth, Mathew and Boriboonsomsin, Kanok. (2008). Real-world carbon dioxide impacts of traffic congestion. Transportation Research Record, (2058):163-171, 2008.
Burbank, Cindy (Feb. 2009). Special report 299: Reducing transportation greenhouse gas emissions and energy consumption: A research agenda.
Burbank, Cindy (Oct. 2009). NCHRP Project 20-24(59) - Strategies for Reducing the Impacts of Surface Transportation on Global Climate Change. A Synthesis of Policy Research and State and Local Mitigation strategies.
Center for Clean Air Policy (2006). Transportation Emissions Guidebook. http://www.ccap.org/safe/guidebook/guide_complete.html.
Center for Clean Air Policy (2004). 'Urban Form and Climate Protection': http://www.ccap.org/Presentations/Winkelman%20TRB%202004%20(1-13-04).pdf. (link inactive 10/2012)
David Suzuki Foundation (1998). Canadian Solutions: Practical and Affordable Steps to Fighting Climate Change, David Suzuki Foundation and the Pembina Institute for Appropriate Technology (www.davidsuzuki.org).
Delepierre, Camille (2008). Slowing Down? Why cities should decrease car speed and why they do not. Lund University Master's Thesis, Lund, Sweden.
Department for Transport (UK): http://www.dft.gov.uk/stellent/groups/dft_rdsafety/documents/page/dft_rdsafety_504682-03.hcsp#P105_10751.
Environmental Protection Agency (1996). 'Information from the EPA Office of Mobile Sources: Emissions Impact of Elimination of the National 55 mph Speed Limit.' http://www.arb.ca.gov/cc/scopingplan/submittals/transportation/emissions_impact_of_elimination_of_the_national_55_mph_speed_limit.pdf.
European Environmental Agency (2008). Success Stories within the Road Transport Sector on Reducing Greenhouse Gas Emissions and Producing Ancillary Benefits. EEA Technical Report No. 2.
Federal Highway Administration (2002). 'Transportation Air Quality - Selected Facts and Figures': http://www.fhwa.dot.gov/environment/air_quality/publications/fact_book/page13.cfm.
Federal Highway Administration (FHWA) (2008). Speed Enforcement Program Guidelines. National Highway Traffic Safety Administration. http://www.nhtsa.gov/.
Garcia, Nicholas. (1996). Greenhouse Gas Mitigation Options for Washington State. Prepared for the Environmental Protection Agency by the Washington State Energy Office, www.muni.org/Departments/health/environment/Adobe%20Documents%20for%20ESD%20Site/WA_Action_Plan.pdf.
Government of Alberta Transportation (2008). Backgrounder: Highway Speeds and Greenhouse Gas Emissions Reductions. Air quality and Climate Change Briefing. http://www.transportation.alberta.ca/Content/docType57/Production/SpeedLimitsBrief.pdf.
Green, David and Schafer, Andreas (2003). Reducing Greenhouse Gas emissions from the U.S. Transportation Sector. Pew Center on global climate Change.
Grimes, Paul, 'Practical Traveler: The 55-M.P.H. Speed Limit.' New York Times, December 26, 1982, http://www.nytimes.com/1982/12/26/travel/practical-traveler-the-55-mph-speed-limit.html.
International Energy Agency/OECD, Saving Oil in a Hurry (2005).
Jones, Crystal and Sedor, Joanne (2006). Improving the Reliability of Freight Travel. Public Roads. Volume 70, No. 1.
Natural Resources Canada (NRCAN): http://oee.nrcan.gc.ca/transportation/autosmart/9980.
OECD and European Conference on Ministers of Transport (2004). Speed Management: Summary Document.
Schafer, Andreas. (2000). Carbon Dioxide Emissions from World Passenger Transport Reduction Options. Transportation Research Record 1738 paper number 00-1182.
United States Government Accountability Office (2008). GAO-09-153R; November 7, 2008. http://www.gao.gov/new.items/d09153r.pdf.
Victoria Transportation Policy Institute TDM Encyclopedia. http://www.vtpi.org/tdm/tdm59.htm.
Winkelman, Steven and Dierkers, Greg (2003). Reducing the Impacts of Transportation on Global Warming: Summary of the New York Greenhouse Gas Task Force Recommendations. Transportation Research Record No. 1842, Paper No. 03-4053.
Policy: Traffic signals can increase stop-and-go driving, causing sudden acceleration and deceleration, congestion, and excess idling, all of which reduce fuel efficiency and increase GHG emissions. Roundabouts are alternatives to traffic signals. Roundabouts are circular road junctions in which traffic enters a continuous one-way stream around a central island. Such traffic routing can reduce vehicle idle times and improve traffic flow, thereby reducing fuel consumption and emissions.
Emissions Benefits and Costs: Substituting a roundabout for a conventional signalized or signed intersection may reduce fuel consumption and CO2 emissions by vehicles traversing that roundabout. Estimates suggest reductions of 16% to 30% in fuel consumption and fewer emissions at roundabouts than conventional intersections. However, the net GHG effect of replacing intersections with roundabouts remains largely unknown because fuel-efficiency benefits may be reduced or negated by emissions from roundabout construction. The cost effectiveness of roundabout construction is also unknown.
Implementation Concerns: Roundabouts are expensive to implement and the public may resist them if the benefits are not recognized. In addition, current driver behavior in roundabouts in the U.S. is tentative, which affects overall performance and reduces capacity (NCHRP 572, 2007).
Roundabouts are circular road junctions in which traffic enters a one-way stream around a central island. Roundabouts are safer than traditional signalized and signed intersections because all traffic moves in the same direction and generally moves slowly and evenly. Roundabouts may also reduce GHG emissions by reducing vehicle idling and fast acceleration and deceleration that is typical of stop-and-go traffic at intersections (Mandavilli et al., 2008). However, fuel savings, if any, depend on the amount of traffic at a given intersection and the type of intersection that is being replaced (Isabrands et al., 2008). Net GHG savings also depend on the emissions from constructing the roundabout.
State and local transportation agencies can use public money to replace signalized or signed intersections with roundabouts.
Roundabouts can be applied to any road intersection with traffic flow control needs. Roundabouts indirectly affect driver behavior.
While much of the literature on roundabouts focuses on safety and traffic flow benefits, several studies from the U.S., Europe, and Australia assess the effects on fuel consumption and GHG emissions. These studies find that roundabouts can reduce fuel consumption and GHG emissions by 16-30% when replacing signalized and signed intersections. However, caution should be used when interpreting these figures. Israbands (2008) noted that model-based analyses of roundabouts can be flawed due to the use of outdated non-EPA (USEPA) emission factors to calculate findings. In addition, GHG production through roundabout construction is uncertain and could reduce or negate post-construction benefits. Therefore, the effectiveness of roundabouts as a GHG mitigation strategy is unknown.
A study from Northern Virginia examined 10 signalized intersections and estimated the effects on traffic delay and safety if these intersections had been constructed as roundabouts. Annual fuel savings were estimated to be more than 200,000 gallons in total from the ten roundabouts (20,000 gallons per roundabout per year on average, equivalent to 177 metric tons of CO2). The annual average daily traffic on the 10 intersections ranged from 14,000 to 46,000 vehicles, with an average of 27,000 vehicles per intersection (Bergh et al., 2005).
Other studies examine the percentage of fuel savings and GHG emission reductions that occur when roundabouts replace conventional intersections. These studies have found that such emission reductions range from 16% to 59% at the site of the intersection being replaced, with most studies reporting that the benefits include GHG emission reductions between 16% and 30% as compared to the emissions at the site of the original intersection.
One study that tested emissions from vehicles directly found that the type of intersection being replaced, the amount of traffic, and the time of day affects whether roundabouts reduce fuel consumption (Zuger and Porchet, 2001). The study evaluated four locations in Switzerland with varying traffic density. While the roundabout that replaced a signalized intersection reduced emissions, the roundabouts that replaced non-signalized intersections did not decrease fuel consumption. Therefore, roundabouts may increase fuel consumption when previous smooth flow is replaced by the deceleration and acceleration of roundabouts.
Also, Kakooza et al. (2005) found that with lighter traffic, roundabouts have less waiting time (hence less stop and go traffic that causes increased fuel consumption) than un-signalized and signalized intersections in terms of easing congestion. However, with heavy traffic, signalized intersections may have less waiting time than roundabouts, due to the long queue time at the entrance of roundabouts in heavy traffic.
Note that none of these studies consider the emissions from roundabout construction. If emissions from capacity expansion projects are any indication, these emissions could significantly reduce or even negate the benefits in fuel savings.
Recent roundabout projects in the United States have shown a wide range in reported construction costs. Assuming 2009 US dollars in the following examples, costs ranged from $13,000 for retrofitting an existing traffic circle into a roundabout, to $667,000 for replacing a traffic signal with a roundabout at the junction of two state highways, with an average cost of approximately $333,000 per roundabout (NCHRP, 1998; Federal Highway Administration, 2000). Roundabouts built by state agencies on state highways generally cost more because they can involve substantial grading and drainage, as well as relatively long splitter islands and many curbs. These state-built roundabouts cost approximately $465,000 to $667,000 each (NCHRP, 1998).
To estimate the fuel savings from roundabouts, it is necessary to know the fuel consumption from the replaced intersection as well as the type of intersection being replaced. Fuel consumption at signalized intersections varies. Furthermore, roundabouts replace a variety of intersections, including signalized intersections, all-way stop controlled intersections, and yield-sign controlled intersections. The amount of traffic at these intersections is also a key factor.
In most studies, the emissions from roundabout construction, operations, and maintenance are not known, but this may be a significant source of GHG emissions and reduce or even negate the benefits of roundabouts. In addition, the operations and maintenance costs are uncertain, and, although they appear to be less than the same costs for signalized intersections, they must be included in order to have an accurate estimate of overall costs and cost effectiveness.
SIDRA is a signalized and un-signalized intersection design and research aid that is often used in roundabout development projects.
Roundabout implementation poses several costs to transportation agencies, including construction costs, engineering and design fees, land acquisition, and maintenance costs. The reported costs of installing roundabouts have been shown to vary significantly from site to site (Federal Highway Administration, 2000). As noted earlier, costs ranged from $13,000 for retrofitting an existing traffic circle into a roundabout to $667,000 for replacing a traffic signal with a roundabout at the junction of two state highways, with an average cost of approximately $333,000 per roundabout (NCHRP, 1998; Federal Highway Administration, 2000). Roundabouts built by state agencies on state highways generally cost more because they can involve substantial grading and drainage, as well as relatively long splitter islands and many curbs. These state-built roundabouts cost approximately $465,000 to $667,000 each (NCHRP, 1998).
A roundabout can be more expensive to construct than the two-way or all-way stop-controlled intersection alternatives, although it is difficult to compare the two since cost for roundabouts varies widely based on site-specific factors (Bergh et al., 2005).
No significant agency implementation concerns anticipated.
Typically, roundabouts are supported for safety and congestion reasons, which are important to the public (GHG emissions are not usually a consideration). However, public acceptance can be one of the biggest challenges to implementing roundabouts because of misconceptions about roundabouts as outdated systems (Federal Highway Administration, 2000). Nevertheless, this can be mitigated with outreach efforts, and acceptance generally increases after roundabouts are implemented (NCHRP, 1998).
U.S. drivers are also less familiar with roundabouts than individuals in some other countries. This affects the overall capacity of roundabouts, because drivers are tentative when they drive through, reducing capacity and efficiency (NCHRP 572, 2007). This research identified that driver behavior was the largest factor in estimating roundabout performance.
A variety of co-benefits were found in the literature, including:
The majority of roundabouts are found in Europe and Australia. The U.S. has approximately 1,000 roundabouts in the eastern half of the country. A few examples include:
There is a need for research on the energy consumption and GHG emissions from roundabout construction and other factors that may reduce their overall effectiveness in GHG mitigation. Such research is necessary to determine whether roundabouts are ultimately effective or ineffective. Project-level data could also be examined to understand costs and CO2 reductions for roundabouts of various sizes.
Barry, C. (2001). Report on Roundabouts, January 2001, Downloaded from the website http://www.cccnh.org/cintroduction.htm.
Bergh, Casey; Retting, Richard, and Myers, Edward (2005). Continued Reliance on Traffic Signals: The Cost of Missed Opportunities to Improve Traffic Flow and Safety at Urban Intersections. Insurance Institute for Highway Safety. http://www.iihs.org/research/paper_pdfs/mf_1848.pdf.
Coelho, Margarida C., Farias, Tiago L. and Rouphail, Nagui M. (2006). Transportation Research Part D: Transport and Environment, Volume 11, Issue 5, September 2006, Pages 333-34.
European Academy of the Urban Environment (2001). Bern: Air quality management and traffic policy. Switzerland. Downloaded from the website http://www.eaue.de/winuwd/96.htm.
Federal Highway Administration (2000, June). Roundabouts: An Informational Guide FHWA-RD-00-67. http://www.fhwa.dot.gov/publications/research/safety/00068/00068.pdf.
Hench, M. Quantitative Determining the Emissions Reduction benefits of the Replacement of signalized intersection by a roundabout.
Insurance Institute of Highway Safety, 28 July 2001. Status Report vol. 36(7).
Institute of Transportation Engineers. (2004). Signal timing practices and procedures: state of the practice. Washington, D.C.
Isebrands et al. (2008). Toolbox to Evaluate the Impacts of Roundabouts on a Corridor or Roadway Network. Minnesota Department of Transportation. http://www.lrrb.org/PDF/200824.pdf.
Kakooza, R., Luboobi, L.S., Mugisha, J.Y.T. (2005). Modeling traffic flow and management at un-signalized, signalized and roundabout road intersections. Journal of Mathematics and Statistics 1, 194-202.
Kittleson and Associates (2000). Roundabouts: An Informational Guide. US DOT. Publication No. FHWA-RD-00-067. http://www.fhwa.dot.gov/publications/research/safety/00068/00068.pdf.
Mandavilli, S. Rys, M. and Russell, E. (2008). Environmental Impact of Modern Roundabouts. International Journal of Industrial Ergonomics. 38, 135-142.
Mustafa, S., Mohammed, A., Vougias, S. (1993). Analysis of Pollutant Emissions and Concentrations at Urban Intersections. Institute of Transportation Engineers, Compendium of Technical Papers, Washington, D.C.
NCHRP Synthesis 264 (1998). Modern Roundabout Practice in the United States. Transportation Research Board. http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_264.pdf.
NCHRP Report 572 (2007). Roundabouts in the United States, Transportation Research Board, http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_572.pdf.
New York Department of Transportation. https://www.dot.ny.gov/main/roundabouts/files/Emissions_Reduction.pdf.
Niittymaki, J., Hoglund, P.G. (1999). Estimating vehicle emissions and air pollution related to driving patterns and traffic calming. In: Paper for the Conference on Urban Transport Systems, Lund.
Redington, Tony (2001). Modern Roundabouts, Global Warming, and Emissions Reductions: Status of Research and Opportunities for North America.
Schips, Norm, Hale, Terry and Rogers, Hal. NYSDOT Highway Design Manual. New York State Department of Transportation. Chapter 5.9.
Várhelyi, Andrés (2002). The effects of small roundabouts on emissions and fuel consumption: a case study. Transportation Research Part D: Transport and Environment, Volume 7, Issue 1, January 2002, Pages 65-71.
Victoria Transportation Policy Institute TDM Encyclopedia.
Williams-Derry, C. (2007). Increases in Greenhouse-gas Emissions from Highway-widening Projects. Available at: http://smartgrowthamerica.org/RP_docs/Sightline_widening_emissions.pdf.
Zuger, Peter and Andre Porchet (2001). Roundabouts: Fuel Consumption, Emissions of Pollutants, Crossing Times. 1st Swiss Transport Research Conference. Monte Verita, Ascona.
Policy: Expanding road capacity on congested highways can reduce traffic delays and improve mobility, potentially leading to reduced fuel consumption and GHG emissions. However, expansion may also increase demand, which would offset initial benefits and potentially lead to longer-term increases in fuel consumption and GHG emissions. Targeted capacity expansion seeks to reduce GHG by improving traffic flow on highways.
Emissions Benefits and Costs: Capacity expansion may not be an effective GHG mitigation strategy overall because GHG reductions from traffic flow improvements may be partly or totally offset by emissions from induced demand from new capacity. Capacity expansion costs approximately $4.05 to $7.1 million per lane mile, but the costs per unit of reduction are not known given the uncertainties in GHG effects.
Implementation Concerns: While highway capacity expansion is often welcomed as a way to relieve congestion, it may not be effective in reducing GHG emissions in the long run, and is more expensive than other strategies.
Expanding road capacity on congested highways can reduce traffic delay and improve mobility, and expansion projects are components of many urban congestion management programs. Since reduced traffic delay has been linked to reduced fuel consumption and increased GHG emissions, capacity expansion has been considered as a potential GHG mitigation strategy. Yet capacity expansion may simultaneously increase GHG emissions by ultimately generating more travel demand (increased trips and VMT) and increasing vehicle speeds (Niemeier, 2009; Cambridge Systematics, 2009; Cassady et al., 2004; Stathopoulos and Noland, 2003). Moreover, the process of capacity expansion itself can be a significant source of GHG emissions.
Capacity expansion projects require coordination between several agencies. State DOTs, local governments (public works, etc.), and sometimes county governments can implement capacity expansion projects. Federal agencies (US DOT/FHWA) provide funding for certain capacity expansion projects. MPOs plan and allocate funding for these projects as well.
This strategy targets the transportation network and indirectly affects users' travel demands and driving behaviors by providing more capacity for passenger and freight trips on highways.
Capacity expansion projects are undertaken regularly, but the impact of capacity expansion on GHG emissions is controversial. The GHG effect of capacity expansion depends at least on the extent to which traffic flow improves (decreasing GHG emissions), the extent to which system-wide demand increases over time (increasing VMT and perhaps returning to previous congestion levels), and the energy and materials used for the construction project itself (increasing GHG emissions). There is little research evidence to support the conclusion that capacity expansion is effective as a GHG mitigation strategy given that some research has found that capacity expansion significantly induces demand and the emissions from this induced demand, in combination with construction, may outweigh any benefits.
GHG emissions from construction are generally estimated on a per-lane mile basis, though estimates vary depending on the nature of the construction. Some research suggested that building one lane-mile of roadway releases between 1,400 and 2,300 tons of CO2, and long-term maintenance activities release between 3,100 and 5,200 tons of CO2 (Williams-Derry, 2007 citing Graham, 2004). Using a more conservative estimate, Williams-Derry (2007) estimated that constructing one lane mile of highway and maintaining it for 50 years releases approximately 3,500 tons of CO2. Using green construction materials could potentially reduce emissions from capacity construction.
When the emissions from construction are combined with travel effects, it is unclear whether CO2 emissions are increased or decreased, given that research has found that highway capacity additions tend to ultimately increase VMT, particularly in growing areas where capacity increases attract further development (NCR, 1995; Cervero, 2003; Cervero and Hansen, 2002; NCHRP, 2005).
Project-level assessments show inconclusive results, in part because they do not usually account for embedded emissions (from production of materials such as asphalt) and life-cycle/cumulative emissions (e.g., from increased demand) (WSDOT, 2009). Some project-level studies have found the following results:
This is not known given that capacity expansion projects' effects on CO2 are highly uncertain and may increase or decrease.
As suggested, a key factor in capacity expansion analysis is whether induced demand and complete life-cycle emissions (from construction, production of materials, maintenance, and other similar or related activities) are considered.
Capacity expansion is very expensive and costs are often underestimated (Litman, 2009). Estimates range from $4.05 million to $7.1 million per lane mile (2009 USD) for highway widening costs (Cox and Pisarski, 2003; Hartgen and Fields, 2006). Moreover, these projects are mostly in densely populated urban areas, and the costs do not always account for land acquisition, complex intersections, community mitigation, and delay costs during construction (Litman, 2009).
Highway projects that span multiple geographic jurisdictions may present challenges related to cooperation and collaboration across the various governmental agencies that may have a role in decision-making, permitting, planning and/or funding. Thus, lead project sponsor agencies and/or jurisdictions should pursue early and continuing efforts to engage concerned agencies at all governmental levels to facilitate the project development process and minimize conflicts and delays due to miscommunication or inadequate information sharing
The public may favor capacity expansion projects because they appear to reduce congestion and improve travel time. However, there may be low acceptance of this policy as a strategy to mitigate GHG emissions.
Capacity expansion projects can reduce the effectiveness of many TDM strategies, especially those that encourage alternatives to SOV travel.
In addition to GHG effects, capacity expansion may negatively affect the environment by changes in land use and increased development.
Many transportation agencies throughout the U.S. use capacity expansion as a congestion mitigation strategy.
Since embedded emissions (production of materials) are not considered in most project-level analyses (WSDOT, 1999), estimates of capacity expansion are likely to be inaccurate and research should be undertaken to estimate the full effect.
Cambridge Systematics (2009). Moving Cooler: an Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions. Urban Land Institute.
Cassady, A., Dutzik, T., and Figdor, E. (2004). More Highways, More Pollution: Road-Building and Air Pollution in American's Cities, U.S. PIRG Education Fund.
Cervero R. and Hansen M. (2002). Induced Travel Demand and Induced Road Investment, a Simultaneous Equation Analysis. Journal. of Transport Economics and Policy, 36, 3, 469‐490.
Cox, W. and Pisarski, A. (2004). Blueprint 2030: Affordable Mobility and Access for All. Georgians for Better Mobility.
Poughkeepsie-Dutchess County Transportation Council (PDCTC) (2007). New Connections, Appendix B: Air Quality and Energy Analysis. Poughkeepsie, NY.
Environmental Defense Fund (2005). Maryland's Intercounty connector: Exacerbating Petroleum Dependence and Global Warming.
Frey, C. and Rouphail, M. (2001). Emissions Reductions Through Better Traffic Management: An Empirical Evaluation Based on On-Road Measurements, North Carolina State University and North Carolina Dept. of Transportation. Available at: .http://www.ncdot.gov/doh/preconstruct/tpb/research/download/1999-08FinalReport.pdf
Graham, J.T. (2004). Hybrid Life-cycle Inventory for Road Construction and Use. Journal of Construction and Management, 130, 1, 43-49.
Hartgen, D. and Fields, M. G. (2006). Building Roads to Reduce Congestion in America's Cities: How much and at what costs? Reason Foundation.
King County (2010). South Park Bridge - Greenhouse Gas Analysis.http://your.kingcounty.gov/kcdot/roads/wcms/tigerII/sustainability/GreenhouseGasAnalysis.pdf.
Litman, T. (2009). Smart Congestion Reductions: Reevaluating the Role of Highway Expansion for Improving Urban Transportation. Available at: http://www.vtpi.org/cong_releif.pdf.
National Research Council (NRC) (1995). Expanding Metropolitan Highways, Implications for Air Quality and Energy Use, Special Report 245.
NCHRP. 2005. Predicting Air Quality Effects of Traffic‐Flow Improvements, Report 535.
Niemeier, Deb A. (2009). Prioritization of Transportation Projects for Economic Stimulus with Respect to Greenhouse Gases: Final. P: repared for the California Department of Transportation. Available at: http://www.catc.ca.gov/programs/rtp/materials/UCD_report_Transp_Projects[amp]GHG%28Final_June09%29.pdf.
Silva-Send, Nilmini (2009). Reducing Greenhouse Gases from the On-Road Transportation in Dan Diego County, Executive Summary. Energy Policy Initiatives Center, University of San Diego Law School.
Strand, et al. (2009). Does Road Improvement Decrease Greenhouse Gas Emissions?, Institute of Transport Economics of the Norwegian Centre for Transport Research. English summary at www.toi.no/getfile.php/Publikasjoner/T%D8I%20rapporter/2009/1027-2009/Sum-1027-2009.pdf.
USEPA (1998). Traffic Flow Improvements, Transportation and Air Quality TCM Technical Overviews, US Environmental Protection Agency.
Stathopoulos, Fotis G. and Noland, Robert (2003). Induced Travel Demand and Emissions From Traffic Flow Improvement Projects. Presented at the 82nd Annual Meeting of the Transportation Research Board.
Victoria Transport Policy Institute (2011). Road Pricing: Congestion Pricing, Value Pricing, Toll Roads and HOT Lanes. Available at: http://www.vtpi.org/tdm/tdm35.htm.
Williams-Derry, C. (2007). Increases in Greenhouse-gas Emissions from Highway-widening Projects. Available at: http://www.sightline.org/research/climate-analysis-gge-new-lanes-10-07/.
WSDOT. (2009). Washington State Department of Transportation Guidance for Project-level Greenhouse Gas and Climate Change Evaluations.
Policy: Resurfacing rough roads reduces friction, thereby improving fuel efficiency and reducing GHG emissions.
Emission Benefits and Costs: Road resurfacing may not significantly decrease and may even increase GHG emissions because the process of resurfacing roads may produce significant CO2, possibly more than the amount saved by the resulting smooth roads. Road resurfacing costs approximately $200,000 per lane mile; cost per metric ton of CO2 is unknown and depends on traffic volume, fleet mix, and net GHG effects.
Implementation Concerns: Resurfacing may not be perceived as an effective GHG strategy given high costs and uncertain effects. However, resurfacing projects for safety and mobility reasons are otherwise well received.
Road roughness results naturally from the gradual deterioration of road surfaces and/or the pavement structure. Not only do rougher roads reduce ride quality, they also reduce driver safety, increase vehicle wear and tear, and increase fuel consumption, which in turn increases GHG emissions (AASHTO, 2009). Road resurfacing has been suggested as a way to improve fuel consumption and reduce GHG emissions. Yet, it is unclear whether resurfacing roads actually reduces GHG emissions due to the energy-intensive process of resurfacing roads (Lepert and Brillet, 2009).
The policy is to adequately maintain and resurface roads so that road conditions are at a lower roughness index. The expected effect is a reduction in GHG emissions due to improved fuel efficiency of vehicles from riding on smoother roads.
The policy could be implemented by any agency that is responsible for resurfacing and maintaining local, arterial, or highway roads, including state DOTs, local governments (e.g. departments of public works), and sometimes county governments (e.g., San Diego Association of Governments). MPOs, although not usually responsible for resurfacing roads, also could implement this strategy by planning for and allocating funds for road resurfacing projects. Many states already spend most of their transportation funds on road maintenance (Smart Growth, 2011).
This strategy affects any road that has a high roughness index that can be lowered by resurfacing. This strategy does not require driver behavior changes, although road users benefit from lower vehicle operational costs and more comfortable travel.
Several studies have examined the fuel economy differences between rough and smooth roads. These studies indicate fuel economy differences (and consequently GHG differences) between 1% and 10%, depending on the type of vehicle and the roughness of the roads considered.
The overall reduction in fuel consumption and GHG from a resurfacing effort depends additionally on the length of road resurfaced and the number and types of vehicles using the road. However, the fuel consumption effects are only part of the effects of resurfacing: CO2 emissions are generated by road construction projects (including resurfacing) because of energy consumption, resource depletion, induced demand incited by the improvements, and increased travel speeds. Studies on the benefits of resurfacing rarely account for these emissions.
For example, one Australian study estimated a national GHG reduction from road resurfacing. The study suggested that from 1996 to 2015, a decrease in roughness by 40% could produce a cumulative reduction of CO2 by 3.09 million metric tons on Australia's national highways (BTCE, 1996). However, emissions from the manufacture of construction materials and the equipment employed in road work were not taken into account and may be significant.
Indeed, a Norwegian report (Strand et al., 2009) suggested that road resurfacing does little to combat climate change. Road resurfacing emits 9.7 tons of CO2 per lane mile for the construction alone. It further found that road resurfacing increases CO2 emissions because an improved quality of roads leads to higher speeds, especially if speeds increase to where the marginal effect of fuel efficiency on emissions is large (e.g., over 55 mph). Consequently, the net effects of road resurfacing on GHG emissions are not known and may be positive or negative.
The research literature did not estimate costs per unit of reduction, and thus it is difficult to estimate this given uncertainties about the level and type of road traffic and, even more importantly, given that the emissions from resurfacing efforts themselves may be high but are often not reported. Agencies would most likely not resurface roads solely to reduce greenhouse gases.
The effects of resurfaced roads vary depending on the number and type of vehicles on the roads and the changes in demand and use patterns that are induced by the improvements. The emissions and costs from road surfacing projects, which may be significant, depend on the type of material used, the transportation of that material to the project site, emissions from machinery, and other similar or related effects.
The International Roughness Index (IRI) is an international standard developed by the World Bank used to measure pavement roughness. The index measures pavement roughness in terms of the number of inches per mile that a laser, mounted on a specialized van, jumps as it is driven. Specifically, the index is based on the 'average rectified slope' (ARS), which is a filtered ratio of a standard vehicle's accumulated suspension motion (mm, inches, etc.) divided by the distance traveled by the vehicle during the measurement (km, mi, etc.). The lower the IRI number is, the smoother the road. It is based on a scale from zero for a true planar surface, increasing to about six (m/km) for moderately rough paved roads, to 12 (m/km) for extremely rough paved roads with potholes and patches, and up to about 20 (m/km) for extremely rough unpaved roads (BTCE, 1996).
Road resurfacing costs are generally high but vary depending on the road's current condition, location, and material. Road resurfacing can vary from less intensive preventative maintenance to more intensive reconstruction, and road resurfacing is generally part of agencies' maintenance and rehabilitations costs.
The average estimate for road reconstruction is $203,000 per lane mile (Venner Consulting and Parsons Brinckerhoff, 2004). Missouri DOT estimated different costs per lane mile for interstate versus non-interstate: $128,700 per land mile for non-interstate roads and $319,000 per lane mile for interstate roads (Missouri DOT, 2008).
Major rehabilitation costs more than preventative maintenance. For example, preventative maintenance typically costs $56,750 to $114,500 per lane mile while reconstruction in urban areas is more expensive, sometimes exceeding $1,013,000 per lane mile (Venner Consulting and Parsons Brinckerhoff, 2004).
Clearly, road resurfacing is expensive. Given the high costs and uncertainty about GHG benefits, road resurfacing is unlikely to be a strategy for reducing GHG, and instead would be undertaken for safety and mobility. Nevertheless, agencies would benefit from tools to measure GHG effects from road resurfacing projects and plans to allow full GHG analysis of agency activity.
This strategy may require some multi-level coordination between state DOTs and local governments, but overall, there are few agency implementation concerns.
The public generally supports road maintenance for its safety and mobility benefits, and because of the public popularity of 'fix it first' efforts. If GHG reductions occur, they would be considered secondary benefits and there would be little opposition. However, it would probably not be feasible (or effective) for agencies to attempt road resurfacing specifically to reduce GHG.
No other costs/barriers were found.
Interactions with other Strategies
Environmental impacts from road resurfacing include energy use that goes into construction materials, resource depletion, and energy and fuel used to resurface roads.
Highway resurfacing also generally causes some temporary disruption to traffic including reduced speed, increased delays, and increased crash risk due to altered road/ traffic conditions and the temporary absence of lane markings. Likewise, resurfacing causes temporary inconvenience, including noise, dust, and airborne particulate matter, to people living close to highways.
While road resurfacing occurs throughout the world, specific examples of road resurfacing efforts to reduce fuel consumption and GHG emissions have been researched in Missouri (Amos, 2006), Australia (BTCE, 1996), Norway (Strand et al., 2009), and France (Du Plessis et al., 1990).
This strategy does not appear to be effective as a GHG reduction strategy since it does not seem to significantly reduce GHG emissions and is cost prohibitive. However, if agencies already have the responsibility of resurfacing roads, then GHG emission reductions calculations can be a useful tool for agencies that need to measure total GHG emissions from agency activity.
While more research is needed, it is clear that road resurfacing as a GHG reduction strategy has a very high cost/benefit ratio. As a secondary impact, agencies could calculate the GHG savings on a project-by-project basis. However, energy use from maintenance and excess delay caused from repaving must be considered. If road resurfacing materials are utilized that have lower energy use and environmental impacts, then there is potential for this strategy to mitigate congestion on a more efficient level.
AASHTO (2009). Rough Roads Ahead: Fix them now or pay for it later. http://roughroads.transportation.org/RoughRoads_FullReport.pdf.
Bureau of Transport and Communications Economics (BTCE) (1996). Transport and Greenhouse: Costs and options for reducing emissions, Report 94. Australian Government Publishing Service, Canberra, Australia.
Du Plessis, Hendrick w.; Visser, Alex T.; and Curayne, Peter C. (1990). Fuel Consumption of Vehicles Affected by Road-Surface Characteristics. In Surface Characteristics of Roadways: International Research and Technologies, Eds. W.E Meyer and J. Reichert. American Society for Testing and Materials, Philadelphia, pp. 480-496.
Fernández, P. C., and J. R. Long (1995). Grades and Other Load Effects on On-Road Emissions: An On-Board Analyzer Study. Presented at Fifth CRC On-Road Vehicle Emission Workshop, San Diego, Calif., Coordinating Research Council, Alpharetta, Ga.
Laganier, Robert and Lucas, Jean (1990). The Influence of Pavement Evenness and Macrotexture on Fuel Consumption. In Surface Characteristics of Roadways: International Research and Technologies, Eds. W.E Meyer and J. Reichert. American society for Testing and Materials, Philadelphia, pp. 454-459.
Lepert, Philippe and Brillet, Francois (2009). The overall effects of road works on global warming gas emissions. Transportation Research Part D 14, pp. 576-584.
Amos, Dave (2006). Pavement Smoothness and Fuel Efficiency: An Analysis of the Economic Dimensions of the Missouri Smooth Roads Initiative. Missouri DOT.
Missouri DOT (2008). Better Roads, Brighter Future. http://www.modot.org/BetterRoads/.
Park, S., and H. A. Rakha. (2006). Energy and Environmental Impacts of Roadway Grades. In Transportation Research Record: Journal of the Transportation Research Board, No. 1987, Transportation Research Board of the National Academies, Washington, D.C., pp. 148-160.
Patten, J.D. and Taylor, G.W. (2006). Effects of Pavement on Fuel Consumption. Centre for Surface Transportation Technology, Canada. http://www.mne.psu.edu/ifrtt/conferences/9thISHVWD/Presentations/02_3_Patten_Effects%20of%20Pavement%20Structure%20on%20Fuel%20Consumption%20.pdf.
Smart Growth America (2011). Recent Lessons from the Stimulus: Transportation Funding and Job Creation. http://www.smartgrowthamerica.org/documents/lessons-from-the-stimulus.pdf.
Strand, Arvid et al. (2009). Summary: Does Road Improvement Decrease Greenhouse Gas Emissions? Institute of Transport Economics.
Texas Transportation Institute (1994). Updated Fuel Consumption Estimates for Benefit-Cost Analysis of Transportation Alternatives.
Venner Consulting and Parsons Brinckerhoff (2004). NCHRP Project 25-25 (04) - Environmental Stewardship Practices, Procedures, and Policies for Highway Construction and Maintenance. http://onlinepubs.trb.org/onlinepubs/archive/NotesDocs/25-25(4)_FR.pdf.
Policy: The majority of energy used for transportation construction comes from the production of pavement materials. Cement and asphalt production, in particular, are the largest sources of industrial process-related CO2 emissions in the United States. Transportation agencies may use lower-energy alternatives instead of cement and asphalt to decrease GHG emissions.
Emissions Benefits and Costs: Emission reductions vary based on material but may be high and could be critical to the success of other strategies that depend on construction (e.g., capacity expansion). The costs of alternatives depend on the specific materials being considered, but in many cases costs may be small or negative since many materials are less expensive, or equivalent, to traditional materials.
Implementation Concerns: Barriers are low given the general cost effectiveness of these materials.
The majority of energy used to produce transportation construction materials comes from the production of pavement materials (Huang et al., 2008; Zapata and Gambatese, 2005). Cement and asphalt production, in particular, are the largest sources of industrial process-related CO2 emissions in the United States. In 2007, U.S. cement production emitted approximately 44.5 million metric tons of CO2, slightly more than 0.7% of all CO2 emissions for the year (Environmental Protection Agency, 2009).
Transportation agencies are beginning to use different materials in the construction process to decrease the adverse effects of construction on the environment. The most common alternative materials are forms of alternative pavement: fly ash instead of Portland cement, warm- or cool-mix asphalts instead of hot-mix, and recycled road materials:
State DOTs and local governments (e.g., public works and county governments) can use construction materials that have lower energy requirements in their processing or application, are recycled, and/or have longer lives. Elected officials could pass legislation requiring recycled and environmentally friendlier materials in road construction and maintenance.
This policy affects transportation construction and maintenance projects.
There are several examples of agencies using alternative construction materials, but few research studies specifically calculate the energy savings and GHG reductions from using these types of construction materials. Moreover, energy savings and emissions reductions from the use of alternative materials vary depending on type of material, percent of recycled content, the scope of project, and other factors.
Some types of eco-friendly construction practices reduce costs (recycled materials), some are more expensive, and others are initially more expensive but reduce costs over the life-cycle of the project.
The type of alternative construction materials used varies energy use and GHG emissions, as do the materials they are replacing, so both materials' energy use (from production to construction) must be known in order to conduct an accurate analysis of the reduction. Transportation of construction materials should also be considered in analysis. For instance, due to the nature of fly ash as an industrial by-product, there is no primary cost to its production (because it would be produced regardless of the needs of transportation construction). However, the transportation of fly-ash from source to site has environmental and other costs and could offset the overall benefits.
The EPA and FHWA have published guides about and on the use of fly ash. These are the guides:
The costs depend largely on the type of construction material used, which may affect transportation costs and production costs.
This strategy requires DOTs and other transportation agencies to use different materials and methods than they traditionally use, and this may necessitate a change in agency culture or the adoption of new policies to ensure the use of alternative materials. No significant inter-agency challenges are anticipated.
If the alternative materials are not more expensive than traditional materials, then they should prove to be socially acceptable. To this end, several types of alternative road construction materials are currently used throughout the United States, indicating that this is the case.
These materials (especially recycled asphalt) are in use throughout the United States and Europe. Examples of uses in the U.S. include:
In the U.S. Recycled Materials Resource Center (RMRC) participants include the following transportation agencies: Caltrans, FDOT, Illinois DOT, Mass Highway, Michigan DOT, Mn/DOT, NHDOT, NJDOT, NYSDOT, NCDOT, Ohio DOT, PennDOT, TxDOT, and WisDOT. Participation indicates that DOTs are either interested in, or are currently using, recycled materials (www.recycledmaterials.org).
There is generally a need for estimation tools to help agencies measure energy consumption from road works projects (Miller and Bahia, 2009). Life-cycle analysis (LCA) models capture lifetime costs but may neglect energy consumption and emissions; redefined LCA models are needed that include characteristics such as sustainability indicators and energy consumption (Huang et al., 2008).
AASHTO Center for Environmental Excellence. (2009). NCHRP 25-25 (4) Environmental Stewardship Practices, Procedures, and Policies for Highway Construction and Maintenance. http://environment.transportation.org/environmental_issues/construct_maint_prac/compendium/manual/detailed_toc.aspx.
Adams, T. H. (1988). Marketing of fly ash concrete. In MSU seminar: Fly ash applications to concrete (January), 1.10, 5.10. East Lansing: Michigan State University.
D'Angelo, J. et al. (2008). Warm Mix Asphalt: European Practice. Publication FHWA-PL-08-007. FHWA, U.S. Department of Transportation. http://international.fhwa.dot.gov/pubs/pl08007/pl08007.pdf.
Environmental Protection Agency. (2005, April). Using Coal Ash in Highway Construction: A Guide to Benefits and Impacts. Report EPA-530-K-05-002.
Environmental Protection Agency (2009, April). Inventory of U.S. Greenhouse Gas Emissions and Sinks, 1990-2007.
Estakhri, Cindy K. and Saylak, Donald. (2005). Reducing Greenhouse Gas Emissions in Texas with High-Volume Fly Ash Concrete. Transportation Research Record: Journal of the Transportation Research Board, No. 1941, Transportation Research Board of the National Academies, Washington, D.C. pp. 167-174.
Federal Highway Administration (2004). Recycled Concrete Study identifies current uses, best Practices. In FOCUS, April 2004. http://www.fhwa.dot.gov/publications/focus/04apr/01.cfm.
Federal Highway Administration (2003). Fly Ash Facts for Highway Engineers. FHWA-IF-03-019. http://www.fhwa.dot.gov/pavement/recycling/fafacts.pdf.
Kahn Ribeiro, Suzana, Shigeki Kobayashi, Michel Beuthe, Jorge Gasca, David L. Greene, David S. Lee, Yasunori Muromachi, Peter J. Newton, Steven Plotkin, Daniel Sperling, Ron Wit, Peter J. Zhou (2007). Transportation and its Infrastructure. Climate Change 2007: Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 323 - 386.
Kapur, Amit; van Oss Hendrik G., Keoleian, Gregory; Kesler, Stephen E; and Kendall, Alissa. (2009). The contemporary cement cycle of the United States Journal of Material Cycles and Waste Management. Volume 11, Number 2.
Huang, et al. Development of Lifecycle Assessment Tool for Sustainable Construction of Asphalt Pavement. Eurobitume, (2008).
Lepert, Philippe and Brillet, Francois. (2009). The overall effects of road works on global warming gas emissions. Transportation Research Part D 14, pp. 576-584.
Miller, T and Bahia, H. (2009). Sustainable Asphalt Pavements: Technologies, Knowledge Gaps, and Opportunities. Prepared for the Modified Asphalt Research Center (MARC), University of Wisconsin.
Naik, T.R. and Moriconi, G. (2006). Environmentally-friendly durable concrete made with recycled materials from sustainable concrete construction.
Olard, F. et al. (2008). Low Energy Asphalt: New Half-Warm Mix Asphalt for Minimizing Impacts from Asphalt Plant Jo site. International ISAP Symposium on Asphalt Pavements and the Environment.
Rogers, Christopher D. F., Thomas, Andrew M., Jefferson, Ian, and Gaterell, Mark. Carbon Dioxide Emissions due to Highway Subgrade Improvements. (2009). Transportation Research Record: Journal of the Transportation Research Board, No. 2104, Transportation Research Board of the National Academies, Washington, D.C., pp. 80-87.
Sullivan, Daniel E. (2006). Materials in Use in U.S. interstate Highways. US Department of the Interior U.S. Geological survey. http://pubs.usgs.gov/fs/2006/3127/2006-3127.pdf.
Tayabji, S., T. Van Dam, and K. Smith. (2009). Advanced Concrete Pavement Technology (ACPT) Program: A Status Report on Available Products. Report No. FHWA-HIF-09-005. http://www.fhwa.dot.gov/pavement/concrete/pubs/if09005/if09005.pdf.
van Oss, H.G. (2006). Cement: U.S. Geological Survey Mineral Commodity Summaries, p. 44-45.
Venta, G. J. (1999). Potential for Reduction of CO2 Emissions in Canada Through Greater Use of Fly Ash in Concrete. Canada Centre for Mineral and Energy Technology/American Concrete Institute International Symposium on Concrete Technology and Sustainable Development, Vancouver, British Columbia, Canada.
Zapata, P., and Gambatese, J.A. (2005). Energy Consumption of Asphalt and Reinforced Concrete Pavement Materials and Construction. ASCE Journal of Infrastructure Systems, Vol. 11, No. 1, pp. 9-20.