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Transportation Systems Management and Operations Benefit-Cost Analysis CompendiumCHAPTER 6. FREEWAY SYSTEMS MANAGEMENT
Case Study 6.1 – Hypothetical Centrally Controlled Ramp Metering Deployment
Note: Chapters 2, 3, and 4 of this Compendium contain a discussion of the fundamentals of benefit-cost analyses (BCA) and an introduction to BCA modeling tools. These sections also contain additional BCA references. Project Technology or StrategyRamp metering involves the placement of a traffic signal on freeway on-ramps to meter the flow of traffic entering the mainline facility and smoothing the flow of traffic in the merge area. Ramp metering may be implemented with minimal cycle lengths designed to simply break up platoons of vehicles entering the facility to smooth the merge operations, or may be operated more aggressively with longer cycle lengths designed to hold traffic on the on-ramp to maintain lower volumes and higher speeds on the mainline facility. Ramp meters may be deployed at single isolated locations, or may be deployed region-wide to improve merge operations and reduce bottlenecks at on-ramp locations, thus improving corridor travel times and safety. Similar to arterial signal systems, the sophistication of the timing patterns may be determined according to preset, traffic actuated, or centrally-controlled patterns. Project Goals and ObjectivesA Midwestern traffic management agency deployed ramp metering on seven interchanges along a 5-mile corridor of a major Interstate. Ramp metering was selected as the most cost effective option, as increasing capacity or adding lanes would be expensive and difficult given limited right of way. The meters were installed at a cost of approximately $30,000 per on-ramp. The overall goal of the ramp metering program was to help decrease congestion by maximizing the flow of traffic and increasing merge safety on the freeway. Data RequirementsData was collected and analyzed prior to and after deployment of the ramp metering system to evaluate effectiveness. The data used for the analysis consisted of loop detector speed and volume data and accident and incident management data. The study focused on morning peak period (6am to 8am) and afternoon peak period (4pm to 6pm). For the 2010-2011 initial evaluation, data was compiled for a 24-month period (March 2008 to March 2010) prior to the implementation of the metering system and for a 12-month period (April 2010 to March 2011) following the activation. For the 2011-2012 Long Term Impacts Evaluation, the data used was archived data from morning and afternoon peak hours for the all no-holiday weekdays following the activation of the system in April, 2010 through September, 2012. The results of the evaluation indicated that the ramp meters were benefitting traffic flow on the Interstate and were meeting or exceeding the objectives for the system that were initially identified. Benefit Cost EvaluationA benefit cost evaluation could be used to determine whether to implement ramp metering technology. TOPS-BC provides input defaults for most variables that would be used in the evaluation of a new ramp meter system. If a planner was looking at a system similar to this ramp meter example, he or she could use the TOPS-BC defaults, or generate new data to make the example as realistic as possible by applying local data in place of the defaults. This also allows the user to test the impact of changes in selected input data. For example, the analysis can be carried out for cases that highlight local or recent information for the project using different technology costs, traffic levels, wait times, etc. Each of the items shown in Table 23 are included in the default input data set, but may be replaced with user supplied data as shown. If user supplied data is entered, it will override the default value and be used by TOPS-BC in all calculations that call for that input data. In addition to the characteristics that describe your project such as technology specific costs, roadway descriptions, number of installations, etc., you may also want to input values different from the TOPS-BC defaults for economic parameters related to the measures of benefits for the project. Examples may be the value of time or reliability. Others include the price of fuel, the cost of crashes or dollar value of other benefits you may have calculated such as vehicle emissions. Entering your own data allows you to make the analysis as specific as you can for your project. In addition, it provides a simple process for testing the sensitivity of the results to a particular variable or set of variables. Table 23 illustrates both user supplied data inputs and TOPS-BC supplied inputs. TOPS-BC calculates a default Freeway Link Capacity based on the HCM and the default or user inputs peak hours and lanes for this case. Here the default capacity is 26400 vehicles per hour. TOPS-BC uses 2200 vehicles per hour per lane times 4 hours times three lanes. If the user felt that the free flow capacity were different for this facility, say 2000 vphpl, the calculation can be redone as 2000 times 4 hours times 3 lanes or 24000. Entering 24000 in the User Supplied Data Input for Freeway Link capacity cell would override the default in all future TOPS-BC calculations. In this case we have some specific site characteristics including length, number of lanes, number of metered ramps, average speed and other characteristics. We also enter specific data about the performance of the facility we are analyzing. TOPS-BC has already done a literature review for the range of impacts of traffic centrally controlled ramp meters and provides a reasonable default value. However, in this case we have specific facility impacts and can input them into the system. We have chosen not to change the value of time, the value of reliability, energy prices or the value of crash avoidance for this example. In this run we are accepting the TOPS-BC default values found in the right column or on the Parameters page in the TOPS-BC model.
Source: FHWA TOPS-BC
In this example, we are running TOPS-BC and we would like to modify the inputs to reflect new data. We might do this because of the similarity of this particular deployment to the one we are considering. We know in previous deployments that the freeway travel speeds increased by 20 percent and the crash rate also decreased by 20 percent. However the TOPS-BC default for both these values was 12 percent. By using the navigation column we can go to the benefit inputs page and input the new percent for volume increases and crash reductions. These values will be used in all calculations calling for these inputs in TOPS-BC. The user can also test the inputs to see where additional benefits may be realized. This can be accomplished by modifying assumptions about the project costs, size or other dimension. One can also test the value assumptions. For example, an alternative set of crash costs by type (fatality, injury or property damage only (PDO)) that reflects local crash cost experience would improve the applicability of this tool for your project. The three primary benefits of ramp metering deployments are improvements in travel time, travel time reliability, and crashes. In addition, the smoother traffic flow results in improved vehicle fuel efficiency and reduced emissions for most pollutants. Each project plan is different and the realized benefits can be impacted by the plan. By varying the assumptions in the plan, BCA models allow you to see how plan assumptions will impact the expected benefits. Travel Time. Mainline and ramp delays increase travel time. Reducing delay and travel time is a benefit that accrues to the freeway user. Travel Time is usually calculated based on estimated link speeds in the corridor, both for the freeway and ramp links. Speeds may be estimated using the speed-flow relationship from the Highway Capacity Manual where a speed factor (to be applied to free flow speed) for varying degrees of congestion (as measured by volume/capacity ratio) can be found. Speed is estimated for the baseline (without improvement) scenario by determining the correct speed-flow factor to apply based on your inputs for capacity and volume and applying the factor to the free flow speed you provided. These analyses must be performed separately for the freeway and ramp links. For the improvement scenario, average capacities are adjusted based on default impact percentages. BCA models usually provide these defaults or the user can supply impact values if available. These default impact values are sensitive to the Level of Timing Sophistication. The adjusted capacity value is used to determine an adjusted volume/capacity ratio which can be used to look up the speed-flow factor from the HCM or as a default in the model. The estimated speeds for the baseline and with improvement scenarios are used to estimate link travel time based on your inputs for link length and average volumes. The difference between the two scenarios in hours of travel time is monetized as the travel time benefit. Travel Time Reliability. Travel time reliability can be based on the non-recurring delay estimation methodology developed for the Strategic Highway Research Program (SHRP 2 projects L03 and L05). The approach uses factors (applied to VMT) representing the expected amount of incident related delay based on the number of lanes on the facility, the length of the analysis period, the facility volume and the facility capacity. This analysis is only performed on the freeway links. The impact of the ramp metering strategy on incident related delay is two-fold – it is impacted by the change in facility capacity (discussed under the Travel Time impact above) and by a reduction in the number of crashes (discussed in the Crashes section below). The change in capacity results in a different volume/capacity ratio (between the without improvement and with improvement scenarios) being used with the incident related delay factors. Incident delay factor is multiplied with the VMT estimated for the facility. Further, the resulting estimated number of hours of incident related delay for the with improvement scenario are further reduced by the percentage decrease in the default crash rate. The incremental change in hours of non-recurring travel time delay between the baseline and with improvement scenario is assigned a dollar value. Tools like TOPS-BC or similar models will do all these calculations for you with data you provide about your project and its expected effects on performance. Reliability has been recognized as an important consideration to travelers. Improving reliability is a benefit to travelers. The SHRP 2 research project dedicated a significant portion of its resources to defining, understanding and measuring reliability. SHRP 2 has released several reports relating to the topic. Not all of this research has been added to the TOPS-BC model Version 1. TOPS-BC V1 now estimates only the benefits from reducing incident related delay. In the future, TOPS-BC will add new code to address the current reliability benefits and add these benefits to the full BCA. The latest model will be available on the FHWA, Planning for Operations web site (http://www.plan4operations.dot.gov/). Safety. Crashes represent the benefit in the reduction in crashes resulting from the smoothing of traffic conflicts in the merge area. A default crash rate factor is usually supplied by the BCA tool; however, if you have local data to support a different impact, you can usually input this project specific information in your model. For example, with TOPS-BC you can enter a factor in the "Reduction in Freeway Crash Rate (%)" cell. This impact factor will reduce the crash rates applied to all crash severities. Dollar values will be applied to the change in the number of crashes to estimate this benefit. The reduction in the number of crashes is also fed back into the calculation of incident related delay, producing a greater benefit level for travel time reliability. Other benefits are often associated with ramp metering strategies including the reduction in vehicle emissions and fuel use. These two benefits are inherently difficult to estimate within a spreadsheet based model (e.g., spreadsheet based models are generally incapable of estimating the vehicle acceleration and deceleration profiles to accurately assess these impacts). Other models such as IDAS offer a link between the BCA and the regional TDM. In TOPS-BC, you are free to modify the analysis framework to include these benefits, or simply to add the estimated value of these benefits to the "User Entered Benefit" cell if there is data to support their inclusion. Model Run ResultsAs shown in Table 24, TOPS-BC cost effectiveness analysis indicates that the first year cost for this ramp meter introduction will be $1.687 million with:
This results in a 20 year net present value of just over $2 million or levelized annual cost of $172,600. CostsIf the deployment was already complete, we could then use the actual cost experience in this case if it was felt that it was more accurate than the average cost shown by TOPS-BC. Costs shown in a single report may not be comparable to the default values as they may not include all deployment costs. TOPS-BC allows the user to add new cost components or to modify the cost categories. You are strongly encouraged to carefully review the default cost data and make modifications as necessary. You may change the predicted useful life, base unit cost of equipment, or continuing O&M cost for any piece of equipment. You may also delete or add pieces of equipment to better match your anticipated equipment mix for the strategy. BenefitsTOPS-BC estimates benefits from the ramp meter deployment from travel time savings, change in travel time reliability, reduced energy consumption and reduced crash events. Together they result in levelized annual benefits of about $8 million. In this case, TOPS-BC estimates that the project benefits far exceed the costs. This results from the gain in operating efficiency for the system under study. Prior to introducing the ramp meters, insufficient freeway capacity during the morning and evening peak traffic periods led to congestion and lost time for road users. With the introduction of the ramp meters, the roadway operated at its design capacity and offered a higher level of certainty for the peak period trips. TOPS-BC also estimated a substantial reduction in energy costs due to congestion relief. The number of crashes was also reduced, which provided the added benefit of crash cost reduction.
Source: FHWA TOPS-BC
Key ObservationsThis case identifies the introduction of a series of ramp meters at 15 on-ramps on an Interstate that is highly congested during the morning and evening peak periods. The peak congested periods last about two hours each on weekday. Prior to and after the deployment, the State DOT collected data on system performance to be able to compare the changes brought about by the deployment. Those performance changes revealed impacts on both freeway and ramp performance. These realized changes are what a pre-project deployment analysis needs in order to estimate the expected project benefits and costs. Once the project is deployed, performance indicators and their changes are known and can be used as an estimate of what might be expected if a similar project is deployed. Case Study 6.2 – Florida DOT Road Ranger Program
Note: Chapters 2, 3, and 4 of this Compendium contain a discussion of the fundamentals of benefit-cost analyses (BCA) and an introduction to BCA modeling tools. These sections also contain additional BCA references. Project Technology or StrategyA Freeway Service Patrol (FSP) program comprises the necessary funding, personnel, training, equipment, operations, maintenance, and business practices that enable agencies to reduce traffic incident duration and thereby reduce traffic congestion on freeways and arterials in their jurisdiction. An effective FSP program requires highly trained personnel who use specially equipped vehicles and tools to systematically patrol congested highways searching for and responding to traffic incidents. A FSP provides incident response services, clearance resources, and free motorist assistance services. FSP functions include performing minor repairs, assisting motorists, removing debris, providing fuel, providing first aid, pushing vehicles out of travel lanes, and assisting emergency services at vehicle crash scenes. Project Goals and ObjectivesIn 1999, the Florida Department of Transportation (FDOT) funded a traffic incident management program called Road Ranger. This freeway service patrol program consists of roving vehicles that provide primary incident response and assistance to disabled vehicles on Interstate corridors and construction zones. The objectives of the program include:
To meet these objectives, Road Rangers provide direct assistance to motorists by quickly responding, assisting, and clearing primary incidents from the travel lanes in close coordination with the state highway patrol and other law enforcement and emergency response agencies. Road Rangers also assist disabled motorists with basic services including furnishing fuel, assisting with tire changes, and helping with other types of minor vehicle repairs. From 2000 to 2010, the number of Road Ranger assists climbed from 112,000 to 351,941 per year.(4) The FSPE model:
In order to have a successful transportation systems management and operations (TSMO) deployment, you must first demonstrate that the benefits of the project exceed the costs. Your assumptions will be evaluated by decision makers so you should plan on providing sensitivity testing of your key input assumptions. BCA models like TOPS-BC allow you to quickly and easily vary input assumptions and compare results. This process lets you demonstrate a range of potential outcomes that can help you gain support from the public and the planning community. In 2012, FDOT commissioned the Center for Urban Transportation Research (CUTR) at the University of South Florida to conduct an independent evaluation of the Road Ranger program and develop a benefit-cost analysis. The study, "Review and Update of Road Ranger Cost Benefit Analysis," presents a district- and state-level evaluation of the program's costs and benefits and provides recommendations for improvements. This case study presents the methodology, tools and data used to analyze the benefits and costs of the Road Ranger program and discusses how they relate to TOPS-BC. Data The study utilized a customized version of the Freeway Service Patrol Evaluation (FSPE) model. The FSPE model was developed by the Institute of Transportation Studies at the University of California, Berkeley for the California Department of Transportation (Caltrans). The model uses Microsoft Excel and it is available at no cost to the public subject to the approval of Caltrans.(5)To apply the FSPE model to evaluate the Florida Road Ranger program, the model was calibrated to suit Florida traffic, roadway conditions, and information availability. Required data inputs included:
In Florida's case, the State DOT uses an advanced traffic management system software system to collect and access these and other traffic related data elements. Benefit Cost EvaluationTo calculate the benefits and costs of the Road Ranger program, the CUTR researchers:
Researchers developed the two types of benefit categories – individual benefits and general public benefits. Individual benefits included: increased safety at the incident scene, reduced incident duration and reduced cost of towing or assistance for the motorist being helped. General public benefits included increased safety at the scene, reduced traffic delays, reduced emissions and reduced fuel consumption. The FSPE methodology uses nine types of incidents to estimate benefits. These include: accident (right shoulder, in lane, left shoulder), breakdown (right shoulder, in lane, left shoulder), and debris (right shoulder, in lane, left shoulder). The model distributes the incident types over a specified road segment during the service period proportional to the vehicle miles traveled (VMT) in that segment during different periods of the day. The model uses study area traffic profiles and average annual daily traffic (AADT) volumes on the study segments to calculate VMT during different times of the day and assigns incidents accordingly. It calculates the statewide benefits for one average day and then multiplies it by the number of days of service to yield the total benefit. After collecting traffic volume and incident response data, the team selected Version 12.1 of the FSPE model. The model uses Microsoft Excel workbooks for all the inputs and outputs. The inputs are used by FSPE to estimate hourly traffic flow due to FSP service. The model uses a queuing model for calculating the delay. The FSPE delay model uses VBA code implemented as an add‐in module to accommodate the more detailed queuing model. (Visual Basic for Applications or VBA is a sophisticated MS Excel tool for Excel power users. See for example: Getting Started with VBA in Excel 2010 at http://msdn.microsoft.com/en-us/library/office/ee814737(v=office.14).aspx.) The model estimates delay saving benefits based on geometric and traffic characteristics, and the frequency and type of FSP-assisted incidents. To apply the FSPE model to evaluate the Florida Road Ranger program, the model was calibrated to suit Florida traffic, roadway conditions, and information availability. Model Run ResultsThe main benefit categories estimated by the FSPE model are delay, fuel, and emissions savings for carbon monoxide (CO), volatile organic compounds (VOC), and nitrogen oxide (NOx). Note that emissions savings were not monetized in the BCA. The total annual emissions savings were estimated at 7,818 Kg for CO and 90,371 Kg for VOC. For NOx, the emissions increased to 59,829 Kg., and CO and VOC are reduced in most cases with increased speeds. NOx emissions increase at high speeds, therefore the emissions for nitrogen oxide increased as overall highway speed increased. CostsThe total cost used in the FSPE model was the contract value to operate and maintain the Road Ranger program. This amount was over $20 million.
Source: FHWA TOPS-BC
Key ObservationsConducting BCA of TSMO projects can seem very challenging at first. However, many previous studies and tools are available to assist you in the process. Some items of particular interest in this case include:
Case Study 6.3 – Metropolitan Area Transportation Operations Coordination Program
Note: Chapters 2, 3, and 4 of this Compendium contain a discussion of the fundamentals of benefit-cost analyses (BCA) and an introduction to BCA modeling tools. These sections also contain additional BCA references. Problem Technology or StrategyThe National Capital Region (NCR) features a multi-jurisdictional and multi-modal transportation system. The system includes highways, multiple transit services, rail, carpool lanes, bicycle trails, and walking trails, with over 300 centerline miles of Interstate, tollways and HOV/HOT lanes. To aid in the quick and reliable exchange of transportation system information among operating agencies in the region, partnering jurisdictions organized the Metropolitan Area Transportation Operations Coordination (MATOC) program in 2008. Project Goals and ObjectivesThe goal of MATOC is to facilitate real-time situational awareness of transportation operations during significant incidents in the National Capital Region. MATOC monitors, collects, analyzes, and coordinates the sharing of information among the stakeholders regarding incidents of regional significance and actions taken by the agencies involved. In 2010 the Metropolitan Washington Council of Governments (MWCOG) published an evaluation of the MATOC program, which included a benefit-cost analysis. The BCA uses a customized traffic model, incident data, and engineering judgment to estimate loss of roadway capacity, vehicular queuing, travel delay, and costs (i.e., emissions, fuel consumption, value of time) associated with a select number of regionally significant traffic incidents for the purpose of quantifying benefits attributable to MATOC. This case study will summarize the approached used to identify, quantify and analyze the benefits and costs of this traffic incident management program. This procedure may be reproduced and customized to fit your organization's needs. This study also serves as an example of how the value of time is addressed in a transportation systems management and operations (TSMO) benefits analysis. DataThe MWCOG analysis compares the actual mobility costs for an incident in which MATOC is involved in the response to the costs of the same incident assuming only a local agency response (i.e. "Without MATOC" scenario). The analysis relied on empirical data collected by the MATOC from participating agencies. Data elements included traffic volume, incident detection time, response time, time on scene, and time to return to normal traffic operations. Benefit Cost EvaluationThe objectives of the BCA study were to assess the benefits that are unique to the coordinated management of incidents affecting regional travel in the NCR; determine how regional coordination of major traffic incidents that span jurisdictional boundaries enhances existing local incident management and mobility savings (e.g., time, fuel, emissions); and determine the benefit-to-cost ratio of the MATOC Program. To complete this task, the study used the following approach:
The study used a series of custom Microsoft Excel spreadsheets and Synchro/SimTraffic, a microscopic simulation model, to model the traffic incidents under each scenario. Model Run ResultsCostsThe study utilized the MATOC annual operating budget as the source for program cost data. Cost categories included:
The total annual cost of the program is $1.2 million. BenefitsDollar estimates for the following benefits were developed based on a University of Maryland and Maryland State Highway Administration benefit cost study:
In transportation economics, the value of time is considered the opportunity cost of the time that a commuter spends on his/her journey. It is typically expressed as the dollar amount a commuter would be willing to pay in order to save time or the amount they would accept as compensation for lost time. The MWCOG study used the following cost conversion factors, developed by the University of Maryland and Maryland State Highway Administration, to quantify the value of time in the "with" and "without MATOC" scenarios used in the BCA study:
According to the MWCOG study, an average of 224 police-reported crashes occur each day in the National Capital Region. A portion of these nonrecurring incidents are regionally significant and require MATOC involvement. The study assumed that MATOC is involved in about 20 minor incidents (such as vehicle fires) and one major incident (such as a bus crash) per month on freeways, arterials or transit. When modeling the minor incident both with and without MATOC involvement, it was found that MATOC contributed to a total savings of $30,260 in terms of emissions, fuel consumption, and the value of time, as shown in Table 26. When modeling the major incident, it was found that MATOC contributed to a total savings of $382,830 in terms of emissions, fuel consumption, and the value of time, as shown in Table 27. For both of these estimates, the assessment is conservative, as it does not include potential savings for reduced or eliminated secondary queues, secondary incidents, or the potential delay reduction due to rubbernecking in the opposite direction.
Source: Metropolitan Washington Council of Governments
Source: Metropolitan Washington Council of Governments
The evaluation estimated that the benefits of one year of MATOC operation amounted to the following:
As shown in Table 28, the BCA results show that MATOC yielded positive benefits associated with reduced traffic delay, reduced emissions and reduced fuel consumption. The total annual benefit was an estimated $11.9 million per year (7.3 million + $4.6 million). The total annual cost of the program was $1.2 million. The resulting benefit-to-cost ratio is 10:1 ($11.9 million in benefits / $1.2 million in costs).
Key ObservationsConducting benefit cost analyses of TSMO projects can seem very challenging at first. However, many previous analysis and tools are available to assist you in the process. Some items of particular interest in this case include: Many MPO and SDOT planning and operations offices utilize a variety of traffic models to describe how the transportation system operation changes with the introduction of new technologies or strategies. These data are often used in BCA and when they are not available, assumed values can provide the information needed to conduct the preliminary BCA. In this case, MWCOG made assumptions about the crash frequency and severity based on available information. They further assume that the MATOC would not be involved in all crashes, so they created a reasonable baseline, local management, and compared the cost of the crash management impacts to what could be expected in the subset of crashes where central management would be appropriate. Some additional observations from the MWCOG BCA include:
Case Study 6.4 – Regional Traffic Management Center, Ft. Lauderdale, Florida
Note: Chapters 2, 3, and 4 of this Compendium contain a discussion of the fundamentals of benefit-cost analyses (BCA) and an introduction to BCA modeling tools. These sections also contain additional BCA references. Problem Technology or StrategyFlorida Department of Transportation (FDOT) District 4 operates The Fort Lauderdale System Management for Advanced Roadway Technologies (SMART) SunGuide Regional Traffic Management Center (RTMC). The center manages intelligent transportation systems (ITS) for the Florida Interstate Highway System (FIHS) in Broward County. The program area includes the I-95, I-75, and I-595 corridors in Broward County. The RTMC operates 7 days per week, 24 hours per day. The program is the product of a FDOT effort that began in the mid-1990s, designed to deploy ITS technologies to manage the region's surface transportation system from a common facility. The system became fully operational in 2004. Project Goals and ObjectivesThe goals of SMART SunGuide RTMC are to:
To meet these objectives, the program applies ITS technologies to make the transportation system more efficient and facilitates interagency communication and coordination to respond to traffic incidents. The RTMC's ITS technologies include:
In 2006, FDOT commissioned a study by the Lehman Center for Transportation at Florida International University to evaluate the RTMC programs from a benefit cost perspective. This case study will summarize the approached used to identify, quantify and analyze the benefits and costs of this traffic management center. This procedure may be reproduced and customized to fit your organization's needs. This study also serves as an example of how one BCA methodology can be used to evaluate multiple strategies. DataThe FDOT SMART database was used to gather inputs for the BCA study. This database provides detailed incident statistics by location, frequency, duration and type of blockage and the number of DMS message activations. Other FDOT databases provide AADT and hourly volume statistics and roadway geometry information (number of lanes, section length, etc.). Calculating Benefits
Benefit Cost EvaluationThe objectives of FDOT BCA were to evaluate the cost and benefits attributed to the RTMC operations. The study used a series of custom Excel spreadsheets that calculated delays; queue lengths and total number of vehicles queued using a combination of information from the SMART database and the highway capacity manual, which provides data for capacity under incident and no-incident conditions. A Florida-specific IDAS model was used to calculate emissions, fuel consumption, and safety impacts. Model Run ResultsCostsCost data for the RMTC program were derived from the FDOT annual operating budget. In 2006, the total annual cost of the program was $8,239,397. The considered costs include capital, operation, and maintenance costs. This figure also included was the value of service contracts for freeway service patrol operators and related incident response management activities. BenefitsDollar estimates for the following benefits were developed:
Of particular note is the evaluation's method to quantify the reduction in travel time. The study used an Excel spreadsheet model that compiles the number and type of freeway incidents for the region in a given year and calculates the durations of each incident where the RTMC was involved. These values were compared to estimates of detection, verification and response times from the available literature. The difference between incident duration was considered the total travel time reduction benefit. The time savings, expressed in hours, was then multiplied by value of time conversion factors ($13.35 per hour per passenger for automobiles and $71.05 per hour for trucks) to convert the time savings to dollar values. The analysis estimated the impact of two safety-related benefits: 1) reduction in secondary incidents and 2) reduction in fatalities due to faster response. These safety benefits were calculated by estimating the annual frequencies of fatal, injury and property damage only (PDO) crashes with no automated traffic management system (ATMS) in place. These were calculated using Florida urban freeway incident rates in the IDAS program, which were modified to reflect Florida specific traffic conditions. The benefits were estimated by multiplying this annual frequency of crashes by reduction factors estimated on previous ATMS studies. The study used a fatality reduction factor of 10 percent to account for faster response to injuries. This figure was based on IDAS default rates which contains estimates for reduction in incident notification and response times that results in faster provided care to injured travelers; result in a 10-15 percent decrease in urban Interstate fatalities. (Additional information on IDAS rates can be found in the Section 2.6 - Benefits – of the IDAS User Guide). The study used a 2.8 percent crash reduction factor to estimate of the impact of traffic management strategies on the number of fatal, injury, and PDO crashes. This factor was selected after a review of previous studies indicating that incident management resulted in 2.8 percent reduction in crashes in San Antonio, Texas. However, other studies have indicated higher reductions in crash rates (15-40 percent reductions) due to the implementation of incident management strategies. The lowest reduction factor was selected to ensure a conservative benefits estimate. The evaluation also provides a methodology for additional safety benefits, which are expressed as reduced crash related injuries and fatalities. Using a method similar to the time savings benefit estimation above, the study used the following conversion factors to convert avoided crash incidents into dollar values: $3,200,000 per fatal crash, $74,730 per injury crash, and $2,000 per PDO crash. As shown in Table 29, the BCA results show that in 2006 RTMC program yielded significant benefits. The resulting benefit-to-cost ratio is $10.44:1.
Source: Florida DOT
Key ObservationsConducting benefit cost analyses of TSMO projects can seem very challenging at first. However, many previous analysis and tools are available to assist you in the process. For example the sketch-planning methodology developed and implemented in this case can be reproduced in Excel and be used in combination with your existing traffic simulation models. This will allow you to use your own traffic and incident data. If you plan to use TOPS-BC as alternative, there are default values that should be review to see if they seem appropriate for your region or project. TOPS-BC covers all of the key benefit categories including: reduction in travel time, reduction in secondary incidents, reduction in fatalities due to faster response, reduction in fuel consumption, and reduction in emissions. The user can rely on TOPS-BC defaults or employ local information. Monetary benefits to drivers due to free services provided by the freeway service patrol is another important benefit of this program This case study also showed that you can use different conversion factors to quantify the value of time and safety benefits. Florida DOT's study used ratios developed by a local university. You may need to select a ratio that fits your jurisdiction's characteristics. Case Study 6.5 – Coordinated Highways Action Response Team, Maryland
Note: Chapters 2, 3, and 4 of this Compendium contain a discussion of the fundamentals of benefit-cost analyses (BCA) and an introduction to BCA modeling tools. These sections also contain additional BCA references. Problem Technology or StrategyCoordinated Highways Action Response Team (CHART) is a joint initiative of the Maryland Department of Transportation, Maryland Transportation Authority and the Maryland State Police, in cooperation with other federal, state and local agencies. The program began in the mid-1980's in an effort to improve travel to and from Maryland's eastern shore. It has evolved into a multi-jurisdictional and multi-disciplinary program. Today, this advanced traffic management system is enhanced by a command and control center called the Statewide Operations Center (SOC). The SOC is the "hub" of the CHART system, functioning 24 hours-a-day, seven days a week with four satellite Traffic Operations Centers (TOCs) located across the state to handle peak-period traffic. Project Goals and ObjectivesCHART's mission is to improve "real-time" operations of Maryland's highway system through teamwork and technology. To meet this objective, CHART oversees the following activities:
The Maryland State Highway Administration tasked the University of Maryland to conduct an annual performance evaluation and benefits analysis of the program. This case study will summarize the approached used to identify, quantify and analyze the benefits of this traffic incident management program. Specifically, this case study will highlight the study's approach to quantifying the benefits of reduced delay to highway users. This procedure may be reproduced and customized to fit your organization's needs. DataSince 1997, University of Maryland researchers have used actual performance data collected from the CHART program. This data included incident management records from the statewide operation centers as well as accident report data from the Maryland State Police. In 2012, CHART recorded over 63,500 emergency response cases. Data elements for each case include:
This study conducted a statistical analysis of incident durations to provide insight into the characteristics of incident durations under various conditions. The distributions of average incident duration were identified by a range of categories including: nature, county, weekdays and weekends, peak and off-peak hours, CHART involvement, and roads. Researchers also collected and compared average duration of incidents and response times from incidents managed by other agencies. Benefit Cost EvaluationThe objectives of the benefits analysis were is to evaluate the effectiveness of CHART's incident detection, response, and traffic management operations on Interstate freeways and major arterials. An estimate of CHART benefits is also provided quantify the benefits the state obtains from its ongoing programs. The most recent study was published in July 2013. To complete this task, researchers used the following methodology:
Model Run ResultsCostsThe focus of the evaluation was to analyze and quantify the benefits of the program. No specific comparison of the cost was completed. BenefitsDirect benefits associated with CHART include:
Of note is the researchers' approach to estimating the value of time benefits resulting from reduced delays. By calculating the difference between actual incident durations resulting from CHART involvement to average incident duration times collected from similar state agencies where CHART was not involved, the study estimates the total time saved by type of vehicle attributable to the CHART program. Incident duration is defined as the time from the lane-blocking incident to the time the lanes are re-opened. Using the unit rates obtained from the U.S Census Bureau (2012) and the Energy Information Administration (2012), researchers then convert delays to monetary value. Each delay is multiplied by the value of time factors - $20.21 per hour for driver and $45.40 per hr. for truck. The study also used a similar approach to quantify the reduction in fuel consumption and emissions attributed to CHART involvement. The reductions in delay were multiplied by the following conversion factors:
Key ObservationsConducting benefit cost analyses of transportation systems management and operations (TSMO) projects can seem very challenging at first. However, many previous analyses and tools are available to assist you in the process. Some items of particular interest in this case include:
Case Study 6.6 – Georgia NaviGator Traffic Incident Management System
Note: Chapters 2, 3, and 4 of this Compendium contain a discussion of the fundamentals of benefit-cost analyses (BCA) and an introduction to BCA modeling tools. These sections also contain additional BCA references. Problem Technology or StrategyTraffic incident management is the process of coordinating the resources of a number of different partner agencies and private sector companies to detect, respond to, and clear traffic incidents as quickly as possible to reduce the impacts of incidents on safety and congestion, while protecting the safety of on-scene responders and the traveling public. Project Goals and ObjectivesThe Georgia NaviGAtor system is a highly integrated traffic incident management system that uses a variety of technologies and processes to monitor the operation of the freeway and arterial system, respond to a variety of incidents, and disseminate traveler information. The goal of NaviGAtor is to reduce traffic congestion caused by traffic incidents as well as secondary crashes that result from incident-related congestion, and to improve overall mobility for the public. In 2006, Georgia DOT published a study that established a methodology to assess a wide range benefits associated with the Georgia NaviGAtor system and described the resulting benefits and cost analysis. This case study highlights key methods utilized in the BCA analysis to calculate three of these benefits. These include: 1) reduction in travel delay, 2) savings due to delay reduction, and 3) savings due to secondary crash reduction. DataCosts. Cost data used in the BCA were obtained from the NaviGator program's annual operating budget for 2003 through 2004. This amounted to $42.5 million. Benefits. As shown in Table 31, the BCA analysis selected six areas of program benefits, with associated measures of benefits.
Benefit Cost EvaluationThis case study highlights key methods utilized in the BCA analysis to calculate three benefits types: 1) reduction in travel delay, 2) savings due to delay reduction, and 3) savings due to secondary crash reduction. Reduction in Travel Delay. The traffic incident management system reduces travel delay by reducing: incident detection times, emergency response times and durations. The delay savings were calculated as the result of the reduction time it takes to respond to and clear an incident using the NaviGator system when compared with a response time of a similar incident responded to without the Navigator System (also called the "baseline" scenario). Using the NaviGator system logs and surveys of emergency response organizations, the "Navigator Managed" and" Baseline" data sets were developed. Average incident detection times, emergency response times and incident durations where NaviGator managed the response were subtracted from the baseline. For example, the average reduction in incident-duration because of NaviGAtor is calculated as: Average reduction in incident-duration = Baseline incident duration – NaviGAtor managed incident duration = 66.6 minutes - 20.7 minutes = 45.9 minutes Savings Due to Travel Delay Reduction. After calculating the total delay savings (vehicle-hours), the cost savings associated with delay reduction was calculated. These savings result from the decrease in time that motorists spend in traffic attributed to NaviGAtor, as converted to a dollar figure estimate for the motorists' value of time. The dollar amount used to estimate the value of motorists' time was based on data from the Bureau of Labor Statistics. The study assumed that the average vehicle occupancy on Atlanta freeways for persons driving from home to work is 1.16 persons per vehicle. The savings due to delay reduction calculation uses this occupancy value to capture the driver and passenger's time. The percent cars and trucks are also determined, based on the segment where the incident occurs, to give a more accurate estimate of the value of time. The average truck's value of time is different from the average value of time for an individual in a car, and different corridors in the Atlanta region have wide variations in percent trucks. The percentage of trucks on highway segments that NaviGAtor manages was determined by using data from GDOT count stations. The equation used to determine the individual incident savings attributed to NaviGator is as follows: IDS(Cost) = IDS(Veh-Hr) * [(Cars(%) * Occ* Car(Cost)) + (Trucks(%) * Truck(Cost))] Figure 27. Equation. Individual Incident Savings. From this calculation, the cost savings for all incidents worked by NaviGAtor are summed to give the total cost savings: Total IDS(Cost) = Σx/1 IDS(Cost) Figure 28. Equation. Cost Savings for All Incidents Where: IDS(Cost) = Incident Delay Savings in Terms of Dollars Saved Savings Due to Secondary Crash Reduction. Secondary crashes are the result of the change in traffic patterns because of the effects of an upstream incident and can be defined by the occurrence of a crash within a predefined distance and time threshold from a primary crash. The reduction in secondary crashes due to NaviGAtor is a result of the reduced incident duration time from the incident management program. The BCA analysis used the equation below to calculate the number of secondary crashes that would occur on average, based on the assumption that 15 percent of all crashes are secondary crashes. The calculation is as follows: Number of secondary crashes in the baseline condition = X * 15.00% Figure 29. Equation. Number of Secondary Crashes in the Baseline Condition. Where: X = Total number of crashes in the baseline condition = 4512 The above number is the number of crashes with in the presence of the NaviGAtor system and is an estimate to the number of crashes in the baseline condition. The baseline condition is expected to have a higher number of incidents; therefore, this number is a conservative estimate. Number of secondary crashes in the baseline condition = 4512 * 15.00% = 676 crashes The estimated decrease in secondary crashes is computed as: Decrease in secondary crashes because of NaviGAtor = Number of secondary crashes in baseline condition * [(T1 - T2)/T1] Figure 30. Equation. Estimated Decrease In Secondary Crashes. Where: T1 = Average incident duration (baseline condition) = 66.6 minutes Therefore: Decrease in secondary crashes because of NaviGAtor = 676 crashes* [(66.6 minutes - 20.7 minutes)/ 66.6 minutes] = 466 crashes The cost savings from the reduction in secondary crashes is: Cost Savings = Decrease in secondary crashes because of NaviGAtor * Acc$ Figure 31. Equation. Cost Savings from the Reduction in Secondary Crashes. Where: Acc$ = Average cost of a two-vehicle property damage only crash = $3,458 per crash Therefore: Cost Savings = 466 crashes * $3,458 /crash = $1,611,054 The average cost associated with each crash is based on data provided by the National Highway Traffic Safety Administration. The rate used is for a low-impact crash (property damage only) involving two vehicles. While crashes that result from a vehicle queue can be severe and result in injuries, a low-impact crash assumption was chosen to give a more conservative estimate for the cost savings benefit. Model Run ResultsThe study determined that annual benefit-cost ratio of the NaviGAtor system in 2003/2004 was 4.4:1 ($186.8M/$42.5M). Table 32 summarizes the BCA results.
Source: Georgia DOT
Key ObservationsThis case identifies three potential methodologies that can be replicated to estimate benefits associated with traffic incident management system deployment. Specifically, this case outlined specific mathematical equations that can be used to quantify the reductions in travel incident delay, savings due to delay reduction, and savings due to secondary crash reduction. In this case, the agency used data from responders and the incident management system's database to compare and contrast program results with a baseline condition where no program existed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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United States Department of Transportation - Federal Highway Administration |
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