Freeway Management and Operations Handbook

Chapter 4 – Performance Monitoring and Evaluation
Page 3 of 3

4.3 Self Assessment

The goal of performance measures is to describe the past, present, and future operation of the operation of the transportation network in quantifiable terms, be they direct outputs (e.g., travel times, tons of pollutant), costs, indices (e.g., accident risk and accessibility), or other surrogates that reflect broad system performance outcomes. Nevertheless, there will always be certain attributes of a freeway management program – such as how well the operations processes are organized and administered, and how well it interacts with other agencies and affected stakeholders – that may never be directly quantified in terms of a performance measure. Several self-assessment tools have been developed by FHWA for this purpose. To date (spring, 2003), the following self-assessment process have been developed:

Roadway Operations and System Management, by which state and local transportation agencies can assess the effectiveness of their roadway operations and system maintenance activities. (Some of the assessment criteria are summarized in the previous chapter in Table 2-1).
Work Zone, to provide a clear indicator of how well transportation agencies are doing in mitigating the impact of work zones on congestion and crashes. (Some of the assessment criteria are summarized in chapter 8)
Traffic Incident Management, to allow local stakeholders to assess how well they manage traffic incidents and identify areas for improvement. (Some of the assessment criteria are summarized in Chapter 10).

The self-assessment tools have been developed based upon what is known at the time of their development. FHWA plans to update and improve them as they go through the self-assessment process each year. The self-assessment tools are designed for internal use. They are intended to help an agency evaluate its operational effectiveness, both in terms of its internal processes and the degree to which it serves its customers. They will not necessarily provide a basis for comparison with other agencies, but instead serve as a guidance document to highlight areas in which improvements can be made. The self-assessment process should be repeated periodically to gage the degree to which agency performance is changing.

Self-Assessment is intended as a group exercise and as such, should be conducted with as many stakeholder representatives as possible, including representatives from other agencies as appropriate. Ideally, those participating in the self-assessment should represent every aspect of the particular subject or focus of the tool. Agency management should also be represented. Management's participation is essential if the results are to lead to implementation of needed changes. It is important that the participants reflect the organizational assignments of responsibility.

4.4 Analysis Techniques

Evaluation of a freeway management and operations program (and other transportation improvements) must occur throughout the life cycle of the program and the associated facility. This includes identifying segments with less-than-desired performance and other operational deficiencies, analyzing alternative solutions for correcting these problems, estimating the associated benefits and costs, and determining the actual improvement in performance and its cost effectiveness. Performance measures and self-assessments are just part (albeit a significant one) of this ongoing evaluation process. Other analytical tools and evaluation methods, as summarized in this section, may also be necessary and appropriate.

The FHWA document entitled "Decision Support Methodology for Selecting Traffic Analysis Tools" (Reference 17) has the stated objective to "assist traffic engineers and traffic operations professionals in the selection of the correct type of traffic analysis tool for operational improvements". These tools include sketch planning, travel demand models, analytical tools based on the Highway Capacity Manual, and simulation. (Several of these tools are discussed below). Reference 17 identifies the following criteria that a user should consider when selecting a type of analysis tool:

Identification of the analysis context for the task at hand – planning, design, or operations/construction.
Analyzing the appropriate geographic scope or study area for the analysis, including isolated intersection, single roadway, corridor, or a network.
Capability of modeling various facility types, such as freeways, high-occupancy vehicle (HOV) lanes, ramps, arterials, toll plaza, etc.
Ability to analyze various travel modes, such as single-occupancy vehicles (SOV), HOV, bus, train, truck, bicycle and pedestrian traffic.
Ability to analyze various traffic management strategies and applications such as ramp metering, signal coordination, incident management, etc.
Capability of estimating traveler responses to traffic management strategies including route diversion, departure time choice, mode shift, destination choice, and induced/ foregone demand.
Ability to directly produce and output performance measures such as safety measures (crashes, fatalities), efficiency (throughput, volumes, vehicle-miles of travel (VMT)), mobility (travel time, speed, vehicle-hours of travel (VHT)), productivity (cost savings) and environmental measures (emissions, fuel consumption, noise).
Tool/cost effectiveness for the task at hand, mainly from a management or operational perspective. Parameters influencing cost-effectiveness include tool capital cost, level of effort required, ease of use, hardware requirements, data requirements, animation, etc.

The document also helps identify under what circumstances a particular type of tool should be used, and contains guidance on how to use this information to select the appropriate type of tool. It is emphasized that Reference 17 is intended to assist practitioners in selecting the category of tool for use; it does not include an assessment of the capabilities of specific tools within an analysis tool category.

4.4.1 Highway Capacity Manual

The Highway Capacity Manual (Reference 18) provides analytical techniques for quantifying operational problems on freeways (e.g., capacity analysis and level of service for freeway segments, weaving areas, ramps and ramp junctions, and interchange ramp terminals). The HCM utilizes Level of service (LOS) as a quality measure to describe operational conditions within a traffic stream, generally in terms of such service measures as speed and travel time, freedom to maneuver, traffic interruptions, and comfort and convenience. The analytical methods in the HCM attempt to establish or predict the maximum flow rate for various facilities at each of the following levels of service:

LOS A describes free-flow operations. Free-flow speeds prevail. Vehicles are almost completely unimpeded in their ability to maneuver within the traffic stream. The effects of incidents or point breakdowns are easily absorbed at this level.
LOS B represents reasonably free flow, and free-flow speeds are maintained. The ability to maneuver within the traffic stream is only slightly restricted, and the general level of physical and psychological comfort provided to drivers is still high. The effects of minor incidents and point breakdowns are still easily absorbed.
LOS C provides for flow with speeds at or near the free flow speed of the freeway. Freedom to maneuver within the traffic stream is noticeably restricted, and lane changes require more care and vigilance on the part of the driver. Minor incidents may still be absorbed, but the local deterioration in service will be substantial. Queues may be expected to form behind any significant blockage.
LOS D is the level at which speeds begin to decline slightly with increasing flows and density begins to increase somewhat more quickly. Freedom to maneuver within the traffic stream is more noticeably limited, and the driver experiences reduced physical and psychological comfort levels. Even minor incidents can be expected to create queuing, because the traffic stream has little space to absorb disruptions.
At its highest density value, LOS E describes operation at capacity. Operations at this level are volatile, because there are virtually no usable gaps in the traffic stream. Vehicles are closely spaced, leaving little room to maneuver within the traffic stream. Any disruption of the traffic stream, such as vehicles entering from a ramp or a vehicle changing lanes, can establish a disruption wave that propagates throughout the upstream traffic flow. At capacity, the traffic stream has no ability to dissipate even the most minor disruption, and any incident can be expected to produce a serious breakdown with extensive queuing. Maneuverability within the traffic stream is extremely limited, and the level of physical and psychological comfort afforded the driver is poor.
LOS F describes breakdowns in vehicular flow; and with such stop-and-go conditions, it is difficult to predict a flow rate. These conditions generally exist within queues forming behind breakdown points. Breakdowns occur when the ratio of existing demand to actual capacity or of forecast demand to estimated capacity exceeds 1.00. The various reasons for these breakdowns (as identified in the HCM) include traffic incidents, which can cause a temporary reduction in the capacity of a short segment; and points of recurring congestion, such as merge or weaving segments and lane drops.

The HCM provides methodologies for determining the performance and LOS for undersaturated conditions based on a number of variables, including number of lanes, lane widths, pavement conditions, users familiarity with the facility, clearance between the edge of the travel lanes and the nearest obstructions (i.e. shoulder width), type of terrain / grade, percentage of heavy vehicles in the traffic stream, base free-flow speed, interchange spacing, and peak-hour factor. (Note: The analysis of LOS is based on peak rates of flow occurring within the peak hour. Most of the procedures in this manual are based on peak 15-min flow rates. The relationship between the peak 15-min flow rate and the full hourly volume is given by the peak-hour factor (PHF).)

HCM procedures are closed-form (i.e., they are not iterative). The practitioner inputs the data and parameters and, after a sequence of analytical steps, the HCM procedures produce a single answer. Moreover, HCM procedures are macroscopic (i.e., inputs and outputs deal with average performance during a 15-minute or a one-hour analysis period), deterministic (i.e., any given set of inputs will always yield the same answer), and static (i.e., they predict average operating conditions over a fixed time period and do not deal with transitions in operations from one state to another).

4.4.2 Simulation

Capacity and LOS analyses are useful tools for gauging the expected operating conditions along freeway segments, and for determining the "order-of-magnitude" changes that will result from major freeway improvements (e.g., widening, reconstructed interchanges, bottleneck improvements). However, improvements provided by freeway management strategies and systems are typically not reflected in such procedures. Moreover, information on performance measures (e.g., vehicle delays, fuel consumption, emissions) is not provided by capacity analysis techniques. It may therefore be worthwhile to utilize traffic simulation models, which can examine the manner the freeway network performs under various sets of simulated conditions.

As implied by the name, traffic simulation models examine the manner in which the roadway network performs under various sets of "simulated" conditions. They provide an excellent means of estimating changes in freeway performance metrics (e.g., average speeds, travel time, delays, emissions) resulting from freeway management strategies and improvements. Simulation models have been successfully used to evaluate the impacts of adding HOV lanes, auxiliary lanes, and truck climbing lanes; freeway widening and reconstruction; modifications to interchanges and weaving sections; ramp metering; incident management (e.g., the reduced time to respond and clear a capacity-reducing incident); and traveler information (by inputting an assumed level of diversion resulting from the information).

Traffic simulation models can be divided into the following two general classes:

Macroscopic simulation models – Macroscopic simulation models are based on deterministic relationships of flow, speed, and density of the traffic stream. The simulation in a macroscopic model takes place on a section-by-section basis rather than tracking individual vehicles. Macroscopic simulation models were originally developed to model traffic in distinct transportation networks, such as freeways, corridors (including freeways and parallel arterials), surface street grid networks, and rural highways. They consider platoons of vehicles and simulate traffic flow in small time increments. Macroscopic simulation models operate on the basis of aggregate speed/volume and demand/capacity relationships. Validation of macroscopic simulation models involves replication of observed congestion patterns. Macroscopic models have considerably less demanding computer requirements than microscopic models. They do not, however, have the ability to analyze transportation improvements in as much detail as microscopic models, and do not consider trip generation, trip distribution, and mode choice in their evaluation of changes in transportation systems (19). Examples include TRANSYT-7F and FREQ.
Microscopic simulation models – Microscopic simulation models simulate the movement of individual vehicles, based on theories of car-following and lane-changing. Typically, vehicles enter a transportation network using a statistical distribution of arrivals (a stochastic process), and are tracked through the network on a second-by-second basis. Upon entry, each vehicle is assigned a destination, a vehicle type, and a driver type. The traffic operational characteristics of each vehicle are influenced by vertical grade, horizontal curvature, and superelevation, based on relationships developed in prior research. The primary means of calibrating and validating microscopic simulation models is through the adjustment of driver sensitivity factors. Computer time and storage requirements for microscopic models are large, usually limiting the network size and the number of simulation runs that could be completed (19). Examples include CORSIM, INTEGRATION, PARAMICS, VISSIM, and Synchro/SimTraffic.

Simulation tools are effective in evaluating the dynamic evolution of traffic congestion problems on transportation systems. By dividing the analysis period into time slices, a simulation model can evaluate the buildup, dissipation, and duration of traffic congestion. Simulation models, by evaluating systems of facilities, can evaluate the interference that occurs when congestion builds up at one location and impacts the capacity of another location.

The individual models vary in their capabilities, limitations, and ease of use (a discussion of which is beyond the scope of this Handbook). Moreover, several models can also show results in real time on a computer monitor by a 2-dimension or 3-dimensional illustration. Simulation models are available from a variety of sources. Information about ordering several of the models mentioned herein is available at https://www.fhwa.dot.gov/environment/cmaqeat/descriptions_traffic_simulation_models.htm. A number of firms specialize in the application of simulation models. Some have their own proprietary simulation software that can be used to analyze special scenarios such as toll plaza operation (e.g., varying combinations of cash and electronic toll lanes) and border crossings.

Reference 19 (Guidelines for Applying Traffic Microsimulation Modeling Software) identifies the following tasks as being typically required to develop, calibrate, and apply a microsimulation model to a typical traffic analysis project:

Identification of project purpose, scope, and approach
Data Collection – Microsimulation models require significant input data, including geometry (lengths, lanes, curvature); controls, existing demands (volumes, OD table), calibration data (capacities, travel times, queues), and future demands
Coding – Each microsimulation model has a set of user-adjustable parameters that enable the practitioner to calibrate the model to specific local conditions. In the absence of good guidance on the appropriate procedures for determining these calibration parameters, it is possible for different practitioners to arrive at different or incorrect conclusions.
Error Checking – The coded transportation network and demand data are reviewed for errors. This step is necessary to weed out coding errors before proceeding with calibration.
Calibration – An initial calibration is performed to identify the values for the capacity adjustment parameters that cause the model to best reproduce observed traffic capacities in the field. If the microsimulation network includes parallel streets, then route choice will be important. In this case, a second calibration process is performed, but this time with the route choice parameters. Finally, the overall model estimates of system performance (travel times and queues) are compared to field measurements of travel times and queues. Fine-tuning adjustments are made to enable the model to better match the field measurements.
Alternatives Testing – In order to avoid biasing the results, it is important to ensure that the microsimulation model for each alternative contains all of the traffic congestion associated with it. The model should start the analysis period with no congestion on the network, and it should end the analysis period with no congestion present on the network. Insufficiently long analysis periods and insufficient geographic coverage result in "missed" congestion that is not properly tabulated by the microsimulation model. Microsimulation models typically produce two types of output, including animation displays and numerical output in text files. The animation display shows the movement of individual vehicles through the network over the simulation period. Text files report accumulated statistics on the performance of the network. It is crucial that the analyst reviews both numerical and animation outputs, and not just one or the other, in order to gain a complete picture of the results.
Documentation and presentation of the results

A significant amount of effort generally is required to learn to use traffic simulation models, including setting up the appropriate inputs and parameters. Simulation tools also require a plethora of input data – the data requirements being generally proportional to the extent of the network being modeled. The required data can include characteristics of each link (e.g., length, number of lanes, auxiliary / HOV lanes, ramps, grade, speed limits, lane widths, pavement condition), link traffic flow information (e.g., entering / exiting volumes, ramp volumes, travel times, prevent heavy vehicles and buses, lane changing characteristics) and other types of information such as detector locations, incident characteristics (e.g., effect of lane blockage on capacity), and ramp metering operations. Additionally, considerable error checking of the data is required, along with manipulation of a large amount of potential calibration parameters. Simulation models cannot be applied to a specific facility without calibration of those parameters to actual conditions in the field.

Simulation models generally require a non-trivial analysis effort. Moreover, any model-specific limitations should be taken into consideration when interpreting the outputs of simulation. Sensitivity analyses are important to developing an understanding of how reasonable the simulation estimates are, and how much confidence the analyst should place in them.

In a FHWA survey of 40 state DOT and local agencies, the following were the top answers to the question: "What are the major barriers to your use of traffic analysis tools?"

Lack of trained staff
Lack of time
Intensive data gathering requirements
Cost of software
Lack of confidence in results

These potential issues not withstanding, simulation should be strongly considered as a key element of any process to evaluate freeway performance, particularly during the alternatives analysis and design stages. As an example, a presentation to the TRB Freeway Operations Committee in January 2002 (Reference 20) identified 111 recent simulation experiences involving several models, including CORSIM, FREQ, INTEGRATION, PARAMICS, and VISSIM. Applications of these models included analyses / evaluations of ramp metering, HOV lanes, truck climbing lanes, auxiliary lanes, interchange modifications, design alternatives, widening, growth impacts, weaving sections, reconstruction planning, ITS strategies, and overall operations. In closing, the presentation identified the following keys to successful model applications:

Well designed work plan
Strong internal support
Model and technical training
Good input and output data
Model and technical support
High-quality calibration
Design of investigations
Documented results

4.4.2.1 Future Trends

FHWA has been a leader in the area of traffic simulation model development, including the development of the NETSIM and FRESIM models, and their integration into the CORSIM model. Today, FHWA continues to develop, maintain, and support the CORSIM model (now part of the Traffic Software Integrated System (TSIS) package) (Note: TSIS also includes a graphical input editor and an animation output processor), including bug fixes, training courses, and guidance documentation. When FHWA undertook this leadership role there were no commercial traffic simulation packages in the market – a situation that no longer exists. Accordingly, FHWA is now assuming more of a "market facilitator role". FHWA will not be a traffic simulation model developer, but will provide resources to stimulate the existing simulation market. Deployment will be facilitated through a combination of outreach, training, guidance, and technical support.

Development activities are focused on developing new tools and improving the analytical foundation of existing tools. The NGSIM program (Next Generation SIMulation) is part of this activity. The goal of the NGSIM Program is to ensure the needs of the model users are met through improving the capability of commercial models. The products of the NGSIM program will include:

Validation data sets – the sets of real-world traffic data with its corresponding data descriptions that may be used to validate the core algorithms.
Core algorithms – the set of algorithms necessary to describe the fundamental behavioral models associated with the driver-vehicle-highway systems (e.g., lane change logic, gap acceptance logic, and response to traffic control devices)
Documentation of the core algorithms and the validation data sets.

Another trend in simulation is the development of real time models that can estimate and predict traffic conditions, thereby allowing freeway management systems to operate in more of a proactive mode. As an example, FHWA is supporting the Center for Transportation Studies at the University of Virginia in the development and evaluation of two prototype traffic estimation and prediction systems. One of these, DynaMIT, is a real-time simulation model that estimates and predicts traffic conditions, generates traveler information, and provides route guidance. The performance of DynaMIT is being evaluated using real world data from the Hampton Roads Smart Traffic Center.

4.4.3 Before and After Studies

Whereas simulation models provide estimates of changes in performance measures (quite a useful tool when evaluating alternatives prior to selecting the specific freeway improvement for design and deployment); after the selected strategies have been implemented, the actual changes in performance can be measured. The most common method of evaluating this actual effectiveness is a Before-and-After study. With Before-and-After studies, the performance of the freeway network is evaluated prior to implementation of the freeway management strategies and / or system. The same performance measures are then taken again after the strategies / system have been implemented. The effectiveness of the system is then determined by comparing the performance of the freeway during the "before" and "after" conditions.

Potential limitations of a Before-and-After analysis include the following:

The effects of individual improvements are difficult to distinguish when more than one improvement is made at a time.
It may take some time for drivers to adjust their travel behavior after the strategy / system has been implemented. Therefore, depending upon when the "after" data are collected, the true effect of the changes may not be measured.
There is often a long time lag between the "before" condition and the "after" condition, which causes this approach to be susceptible to errors caused by time-related factors (such as changes in travel patterns, population growths, economic fluctuations, etc.).
Some performance measures (like the number of crashes, or demand) can fluctuate considerably over time. There is a tendency for these performance measures to return to more typical values after an extraordinary value has been observed. This tendency is called regression to the mean. It is possible that either the "before" condition or the "after" condition could fall at one of these extreme values, thereby, hiding the true performance of the system.

4.4.4 Alternatives Analysis

In very general terms, an alternatives analysis involves estimating the benefits and costs for each alternative, comparing these alternative-specific benefits to its costs, comparing this "cost-efficiency" for all alternatives, and then selecting the one that offers the greatest potential.

4.4.4.1 Benefits

Freeway management strategies (i.e., operational improvements, low-cost geometric improvements, ITS) can produce a number of benefits, often significant in their magnitude. An overview of some of these benefits is included in Chapter 1, with additional information provided in subsequent topic-specific chapters. Several benefits can be quantified as performance measures (e.g., reduction in travel time, reduced delay, reduced emissions, reduced fuel consumption, reduced incidents) and associated indices; whereas others cannot (e.g., improvement in driver perception of the transportation agencies in the region). Furthermore, while some of the quantifiable benefits can be readily converted to a monetary value (e.g., fuel consumption, person delay), other benefits, such as emission reductions, do not easily lend themselves to monetary conversions (at least not without some significant assumptions).

Another consideration when estimating benefits (and in developing alternatives) is to fully recognize the synergies that can develop from implementing certain combinations of freeway management elements and / or ITS components. For example, if deployed independently, ramp widening, ramp metering, and retiming of signals at nearby intersections would likely improve operations; but combined, the benefits could be significant. Similarly, implementation of closed-circuit television may not only assist in the verification and response-determination of an incident, but also prove useful in verifying whether a traffic message is properly displayed on a nearby changeable message sign. At the same time, it is important to realistically assess how certain elements or components will actually perform, given the presence of other improvements and subsystems. In some cases, the interrelationships are such that the benefits of stand-alone elements may not be additive, as in the case of automated incident detection algorithms (and the associated surveillance infrastructure) combined with a toll free telephone number established for cellular telephone users to call in and report incidents – quickly detecting the same incident twice (once by each subsystem) does not double the benefits.

The freeway practitioner must also recognize the fact that whereas freeway management costs are "real dollars" obligated by a government agency and ultimately funded by taxpayers; the benefits, while very real in terms of improved operations and safety, may not always translate well into dollar equivalents – that is, the monetary value of the benefits does not represent actual funds that accrue back to an agency or that are recognized by individual travelers. Moreover, these benefits may not be as highly valued in the political decision arena as more traditional highway improvements involving significant amounts of concrete and asphalt. As discussed in Chapter 2, the freeway practitioner must endeavor to promote a more widespread appreciation of the relatively high cost-effectiveness of freeway management and operations.

Information on benefits can be obtained from a variety of sources, including:

Simulation (as discussed in section 4.4.2)
ITS Deployment Analysis System (IDAS, discussed in section 4.4.4.6)
Other similar improvements and systems (e.g., www.benefitcost.its.dot.gov for ITS-related benefits) with the caveat that great care must be taken when using representative benefits of similar systems and programs. The user must consider potential differences in the features and functionality of the programs, location and topography, the existing traffic conditions before implementation, the existence and stability of working relationships between agencies, the specific combination of elements and subsystems incorporated into the overall freeway management program – all of which contribute to its overall success and impact of a freeway management and operations program.

4.4.4.2 Costs

Costs associated with freeway management improvements may be classified as follows:

Capital costs include all costs associated with the implementation of the freeway management strategies and systems, including planning, design, right-of-way, equipment, construction, maintenance & protection of traffic during construction, software development and licensing, system integration, and testing.
Continuing costs are those associated with ongoing operations of the freeway management program, including equipment and infrastructure maintenance costs, equipment replacement, staffing costs to operate the system (operations personnel, clerical personnel, public information personnel, etc.), utilities costs, software updates, and leasing costs (communications, control center space, etc.).

Continuing costs are just as important as, if not more important than, capital costs. Adequate funding for operations and maintenance, including funding to replace system components when their useful lives have expired, is essential for successful freeway management.

It is crucial that the life-cycle costs of the program must be determined in terms of its complete implementation and operating schedule, recognizing that a freeway management and operations program will likely entail many separate steps, with elements that are deployed and become operational at various points in time. In developing life-cycle costs, the time stream of capital and operating / maintenance costs must be determined and net present worth techniques applied (e.g., discount the annual recurring costs and sum with capital costs to derive the net present value).

Information on costs can be obtained from a variety of sources, including:

ITS Deployment Analysis System (IDAS, discussed in section 4.4.4.6)
Experience of other programs and systems (e.g., www.benefitcost.its.dot.gov for ITS-related costs, and selected DOT web sites), with the caveat that great care must be taken when using representative costs of similar systems and programs. The user must consider potential differences in methods of construction and integration, timing (i.e., inflation), location and topography, and what all is included in a particular item (e.g., does the DMS cost include the support structure).

4.4.4.3 Benefit-Cost Analysis

The Benefit-Cost (B/C) analysis technique is perhaps the most widely accepted methodology for evaluating transportation improvement alternatives. The B/C ratio is simply the equivalent benefit of an alternative divided by the equivalent cost of that alternative:

B/C = (benefits of alternative i) / (costs of alternative i)

Benefit-cost comparisons are possible when the benefits of an improvement can be assigned a monetary value. If the benefits of an alternative exceed its costs, the improvement is economically justifiable. Furthermore, the ratio of each alternative provides a convenient basis for comparison, providing a measure of the dollars of expected benefit of an alternative for each dollar spent on that alternative.

If system alternatives being analyzed build upon each other in terms of the costs, quantities, complexities, etc. of components that meet the system goals and objectives, it may be more appropriate to consider an incremental benefit-cost analysis. For this approach, the benefits and costs considered for each alternative are not the totals, but rather the additional benefits achieved and costs incurred over the next expensive (and presumably effective) alternative. This analysis considers, in effect, whether an investment necessary to achieve the next incremental step in the system can be justified in terms of the incremental benefits that would be achieved.

The benefit-cost (or incremental benefit cost) analysis methodology provides an objective means of comparing the quantifiable and monetarily-based benefits of an alternative to the costs of that alternative. However, as already discussed, some freeway management benefits are not easily quantified, and not all quantifiable benefits are easily converted to a monetary value. Because of this, alternative analyses are often needed to help assess which alternatives systems or subsystems meet their objectives in the most economical manner. One such analysis approach is utility cost.

4.4.4.4 Net Present Value

Computation of an alternative's net present worth involves a conversion of all costs and benefits of an alternative that are incurred at the alternative's initiation and throughout its useful life (life-cycle) to an equivalent current value. The current value of the equivalent costs is subtracted from the current value of the equivalent benefits of the alternative. If the benefits exceed the costs, the alternative can be justified economically. Furthermore, comparisons among alternatives are straightforward; the alternative that provides the greatest additional benefits over costs (sometimes referred to as "excess benefits") is said to have the greatest net present worth.

4.4.4.5 Utility-Cost Analysis

Although a benefit-cost (or incremental benefit-cost) analysis is a direct method of determining whether a freeway management alternative is economically viable, such an analysis can be performed only if the benefits to be accrued can be estimated in monetary terms. For many goals and objectives of freeway management, this is not possible. In these cases, a utility-cost analysis approach is commonly utilized. The term cost-effectiveness is sometimes used interchangeably with the term utility-cost analysis.

In a utility-cost analysis, utility measures of performance goals or objectives are created to estimate system benefits. Typically, a project team or expert panel subjectively rates (from 0 to 10 or on a similar scale) how well an alternative is expected to achieve each of the objective or performance criteria. Weighting factors (summing to unity) are also estimated for each of the objective or performance criteria, and multiplied by the rating given to that objective/criterion. These "utilities" of each of the objective/criteria are then summed to determine the total system utility. Dividing the system utility by total system cost represents the utility-cost factor for a particular system. The basic steps in a utility-cost analysis are as follows:

Define goals and subgoals (done as part of the decision process).
Weigh each goal.
Weigh each subgoal.
Rate the utility of each alternative in satisfying each goal/subgoal.
Multiply the rating by the weight for each goal / subgoal, and sum over all goals for each alternative (i.e., calculate the utility)
Compute utility-cost ratio.

4.4.4.6 ITS Deployment Analysis System (IDAS)

The ITS Deployment Analysis System (IDAS) is software developed by the Federal Highway Administration that can be used in planning for Intelligent Transportation System (ITS) deployments. It is a modeling tool at the sketch planning level that enables the user to conduct systematic assessments and quantitative evaluations of the relative benefits and costs of more than 60 types of ITS investments (at the time of this writing), in combination or in isolation. IDAS has a number of useful features. For example, IDAS:

Works with the output of existing transportation planning models;
Compares and screens ITS deployment alternatives;
Estimates the impacts and traveler responses to ITS;
Develops inventories of ITS equipment needed for proposed deployments and identifies cost sharing opportunities;
Estimates life-cycle costs including capital and O&M costs for the public and private sectors;
Provides documentation for transition into design and implementation.

The model utilizes network and trip data from the regional transportation model. Strategies are applied either for links in the transportation network or at the traffic analysis zone level. Strategies that affect the time or cost of travel affect mode choice, temporal choice, and induced/foregone demand through a "pivot-point" model, which is based on coefficients from the regional travel model. Other strategy impacts are based on findings from various empirical studies. Changes in trips by mode, time of day, and origin/destination subsequently affect vehicle speeds and volumes.

Required data include transportation network and trip tables by mode and/or purpose, which can be obtained from the regional travel model, and deployment of ITS strategies by type and location on the transportation network. The outputs include changes in vehicle-trips, VMT, emissions; travel time savings and improvements in travel time reliability; energy consumption, noise impacts, safety impacts, and monetary values of these changes; and lists of ITS equipment and costs.

IDAS requires some time investment to learn and some user skills – in particular, it is helpful to have familiarity with travel model data in setting up the model. Data entry and alternatives analysis are conducted in a user-friendly Windows environment. Run time is non-trivial (anywhere from a few minutes to a few hours, depending on the number of zones and other factors.)

4.5 Closing, and a Look Forward

Performance measures are important and valuable indices for evaluating the transportation system operating conditions, identifying problems (e.g., congestion and delays, poor operating speeds, crashes, large fluctuations in travel times / average speeds), and their locations and severity. As discussed in Chapter 2, having identified the problems, the next step for the freeway practitioner is to develop alternative improvements and strategies for alleviating (or at least reducing the impact of) these problems, and then analyzing and evaluating these alternatives (using one or more of the tools and techniques described in Section 4.4) to determine the optimum alternative or combination of alternatives.

The remainder of this Freeway Management and Operations Handbook (i.e., Chapters 5 – 17) describes numerous alternatives for improving the operation and safety of a freeway facility. What is "best" for a particular location is dependent on a number of factors and considerations specific to that location, including the roadway geometrics, signs and markings, weather, lighting, driver population and their behavior, the mix of vehicles in the traffic flow, locations and characteristics of major traffic generators, the institutional environment and the associated goals and policies of decision makers, the extent of any ITS deployment, the features and functionality of any existing management systems, just to name a few. It is the responsibility of the practitioner to consider all such variables in the analysis.

Moreover, as discussed in Chapter 2, when identifying potential operational improvements and enhancements, the practitioner must consider a wide range of possibilities and perspectives – including the perspective of enhanced transportation services (i.e., the "supply" of transportation), the perspective of those who use these services (i.e., better managing the "demand" for the transportation system), the perspective of influencing where this demand occurs (i.e., the land use dimension), or any combination of the above. Practitioners should also carefully consider how individual actions relate to one another and how, when combined into an overall program, they relate to area, regional and statewide objectives.

4.6 References

1. "Implementing Performance Measurement in Transportation Agencies"; Hal Kassoff, Parsons Brinckerhoff Quade & Douglas; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26.

2. "Performance Measures of Operational effectiveness for Highway Segments and Systems – A Synthesis of Highway Practice"; NCHRP Synthesis 311; Transportation Research Board; Washington D.C.; 2003.

3. "Performance Measurement & Integrated Transportation Management Systems – A Traffic Operations Perspective"; Wolf; from the 4th conference on Integrated Transportation Management Systems; Newark, NJ; July 2001.

4. "Measuring That Which Cannot Be Measured—At Least According to Conventional Wisdom"; Michael Meyer, School of Civil and Environmental Engineering, Georgia Institute of Technology; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26.

5. "Use of Performance Measures in Transportation Decision Making"; Steven Pickrell and Lance Neumann, Cambridge Systematics, Inc; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26.

6. Executive Summary; "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26.

7. "Transportation Data and Performance Measurement"; Doug Dalton, Joseph Nestler, John Nordbo, Bob St. Clair, Ernest Wittwer, and Mark Wolfgram, Wisconsin Department of Transportation; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26.

8. NHI Training Course on CMS

9. "FHWA's Mobility Monitoring Program: Transforming Gigabytes of Archived Operations Data Into Mobility and Reliability Performance Measures"; Shawn Turner, Tim Lomax, Rich Margiotta and Vince Pearce

10. "Monitoring Urban Roadways in 2000: Using Archived Operations Data for Reliability and Mobility Measurement"; Tim Lomax, Shawn Turner, Texas Transportation Institute, and Richard Margiotta, Cambridge Systematics, Inc.; FHWA, December 2001.

11. "Guidelines For Transportation Management Systems Maintenance Concepts and Plans (Draft)", PB Farradyne, July, 2002

12. "Cross-Cutting Studies and State-of-the-Practice Reviews: Archive and Use of ITS-Generated Data"; Center for Transportation Analysis, Oak Ridge National Laboratory; FHWA; April 30, 2002

13. "Guidelines for Developing ITS Data Archiving Systems"; Report 2127-3; Texas Transportation Institute; September 2001

14. Hallenbeck, Mark; "Data Collection, Archiving and Performance Measures: Why Should Freeway Operations Care?"; Newsletter of the ITS Cooperative Deployment Network; March 2003.

15. "Manual on Transportation Engineering Studies"; ITE; Washington, D.C.

16. Hallenbeck, Marc; "Operations Planning – The Case for Archived Data User Services"; Presentation to Freeway Operations Committee of TRB; January 2003

17. "Decision Support Methodology for Selecting Traffic Analysis Tools"; FHWA; January 2003.

18. Highway Capacity Manual, Transportation Research Board, National Research Council, Washington D.C: 2000

19. "Guidelines for Applying Traffic Microsimulation Modeling Software – Draft"; Dowling Associates, Inc. in association with Cambridge Systematics, Inc; FHWA; December 2002

20. May, Dolf; University of California; "Recent California Freeway Simulation Model Experiences"; Presentation to Freeway Operations Committee of TRB; January 2003

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Freeway Management and Operations Handbook
Table of Contents Chapter 4 – Performance Monitoring and Evaluation Page 3 of 3 4.3 Self Assessment The goal of performance measures is to describe the past, present, and future operation of the operation of the transportation network in quantifiable terms, be they direct outputs (e.g., travel times, tons of pollutant), costs, indices (e.g., accident risk and accessibility), or other surrogates that reflect broad system performance outcomes. Nevertheless, there will always be certain attributes of a freeway management program – such as how well the operations processes are organized and administered, and how well it interacts with other agencies and affected stakeholders – that may never be directly quantified in terms of a performance measure. Several self-assessment tools have been developed by FHWA for this purpose. To date (spring, 2003), the following self-assessment process have been developed: Roadway Operations and System Management, by which state and local transportation agencies can assess the effectiveness of their roadway operations and system maintenance activities. (Some of the assessment criteria are summarized in the previous chapter in Table 2-1). Work Zone, to provide a clear indicator of how well transportation agencies are doing in mitigating the impact of work zones on congestion and crashes. (Some of the assessment criteria are summarized in chapter 8) Traffic Incident Management, to allow local stakeholders to assess how well they manage traffic incidents and identify areas for improvement. (Some of the assessment criteria are summarized in Chapter 10). The self-assessment tools have been developed based upon what is known at the time of their development. FHWA plans to update and improve them as they go through the self-assessment process each year. The self-assessment tools are designed for internal use. They are intended to help an agency evaluate its operational effectiveness, both in terms of its internal processes and the degree to which it serves its customers. They will not necessarily provide a basis for comparison with other agencies, but instead serve as a guidance document to highlight areas in which improvements can be made. The self-assessment process should be repeated periodically to gage the degree to which agency performance is changing. Self-Assessment is intended as a group exercise and as such, should be conducted with as many stakeholder representatives as possible, including representatives from other agencies as appropriate. Ideally, those participating in the self-assessment should represent every aspect of the particular subject or focus of the tool. Agency management should also be represented. Management's participation is essential if the results are to lead to implementation of needed changes. It is important that the participants reflect the organizational assignments of responsibility. 4.4 Analysis Techniques Evaluation of a freeway management and operations program (and other transportation improvements) must occur throughout the life cycle of the program and the associated facility. This includes identifying segments with less-than-desired performance and other operational deficiencies, analyzing alternative solutions for correcting these problems, estimating the associated benefits and costs, and determining the actual improvement in performance and its cost effectiveness. Performance measures and self-assessments are just part (albeit a significant one) of this ongoing evaluation process. Other analytical tools and evaluation methods, as summarized in this section, may also be necessary and appropriate. The FHWA document entitled "Decision Support Methodology for Selecting Traffic Analysis Tools" (Reference 17) has the stated objective to "assist traffic engineers and traffic operations professionals in the selection of the correct type of traffic analysis tool for operational improvements". These tools include sketch planning, travel demand models, analytical tools based on the Highway Capacity Manual, and simulation. (Several of these tools are discussed below). Reference 17 identifies the following criteria that a user should consider when selecting a type of analysis tool: Identification of the analysis context for the task at hand – planning, design, or operations/construction. Analyzing the appropriate geographic scope or study area for the analysis, including isolated intersection, single roadway, corridor, or a network. Capability of modeling various facility types, such as freeways, high-occupancy vehicle (HOV) lanes, ramps, arterials, toll plaza, etc. Ability to analyze various travel modes, such as single-occupancy vehicles (SOV), HOV, bus, train, truck, bicycle and pedestrian traffic. Ability to analyze various traffic management strategies and applications such as ramp metering, signal coordination, incident management, etc. Capability of estimating traveler responses to traffic management strategies including route diversion, departure time choice, mode shift, destination choice, and induced/ foregone demand. Ability to directly produce and output performance measures such as safety measures (crashes, fatalities), efficiency (throughput, volumes, vehicle-miles of travel (VMT)), mobility (travel time, speed, vehicle-hours of travel (VHT)), productivity (cost savings) and environmental measures (emissions, fuel consumption, noise). Tool/cost effectiveness for the task at hand, mainly from a management or operational perspective. Parameters influencing cost-effectiveness include tool capital cost, level of effort required, ease of use, hardware requirements, data requirements, animation, etc. The document also helps identify under what circumstances a particular type of tool should be used, and contains guidance on how to use this information to select the appropriate type of tool. It is emphasized that Reference 17 is intended to assist practitioners in selecting the category of tool for use; it does not include an assessment of the capabilities of specific tools within an analysis tool category. 4.4.1 Highway Capacity Manual The Highway Capacity Manual (Reference 18) provides analytical techniques for quantifying operational problems on freeways (e.g., capacity analysis and level of service for freeway segments, weaving areas, ramps and ramp junctions, and interchange ramp terminals). The HCM utilizes Level of service (LOS) as a quality measure to describe operational conditions within a traffic stream, generally in terms of such service measures as speed and travel time, freedom to maneuver, traffic interruptions, and comfort and convenience. The analytical methods in the HCM attempt to establish or predict the maximum flow rate for various facilities at each of the following levels of service: LOS A describes free-flow operations. Free-flow speeds prevail. Vehicles are almost completely unimpeded in their ability to maneuver within the traffic stream. The effects of incidents or point breakdowns are easily absorbed at this level. LOS B represents reasonably free flow, and free-flow speeds are maintained. The ability to maneuver within the traffic stream is only slightly restricted, and the general level of physical and psychological comfort provided to drivers is still high. The effects of minor incidents and point breakdowns are still easily absorbed. LOS C provides for flow with speeds at or near the free flow speed of the freeway. Freedom to maneuver within the traffic stream is noticeably restricted, and lane changes require more care and vigilance on the part of the driver. Minor incidents may still be absorbed, but the local deterioration in service will be substantial. Queues may be expected to form behind any significant blockage. LOS D is the level at which speeds begin to decline slightly with increasing flows and density begins to increase somewhat more quickly. Freedom to maneuver within the traffic stream is more noticeably limited, and the driver experiences reduced physical and psychological comfort levels. Even minor incidents can be expected to create queuing, because the traffic stream has little space to absorb disruptions. At its highest density value, LOS E describes operation at capacity. Operations at this level are volatile, because there are virtually no usable gaps in the traffic stream. Vehicles are closely spaced, leaving little room to maneuver within the traffic stream. Any disruption of the traffic stream, such as vehicles entering from a ramp or a vehicle changing lanes, can establish a disruption wave that propagates throughout the upstream traffic flow. At capacity, the traffic stream has no ability to dissipate even the most minor disruption, and any incident can be expected to produce a serious breakdown with extensive queuing. Maneuverability within the traffic stream is extremely limited, and the level of physical and psychological comfort afforded the driver is poor. LOS F describes breakdowns in vehicular flow; and with such stop-and-go conditions, it is difficult to predict a flow rate. These conditions generally exist within queues forming behind breakdown points. Breakdowns occur when the ratio of existing demand to actual capacity or of forecast demand to estimated capacity exceeds 1.00. The various reasons for these breakdowns (as identified in the HCM) include traffic incidents, which can cause a temporary reduction in the capacity of a short segment; and points of recurring congestion, such as merge or weaving segments and lane drops. The HCM provides methodologies for determining the performance and LOS for undersaturated conditions based on a number of variables, including number of lanes, lane widths, pavement conditions, users familiarity with the facility, clearance between the edge of the travel lanes and the nearest obstructions (i.e. shoulder width), type of terrain / grade, percentage of heavy vehicles in the traffic stream, base free-flow speed, interchange spacing, and peak-hour factor. (Note: The analysis of LOS is based on peak rates of flow occurring within the peak hour. Most of the procedures in this manual are based on peak 15-min flow rates. The relationship between the peak 15-min flow rate and the full hourly volume is given by the peak-hour factor (PHF).) HCM procedures are closed-form (i.e., they are not iterative). The practitioner inputs the data and parameters and, after a sequence of analytical steps, the HCM procedures produce a single answer. Moreover, HCM procedures are macroscopic (i.e., inputs and outputs deal with average performance during a 15-minute or a one-hour analysis period), deterministic (i.e., any given set of inputs will always yield the same answer), and static (i.e., they predict average operating conditions over a fixed time period and do not deal with transitions in operations from one state to another). 4.4.2 Simulation Capacity and LOS analyses are useful tools for gauging the expected operating conditions along freeway segments, and for determining the "order-of-magnitude" changes that will result from major freeway improvements (e.g., widening, reconstructed interchanges, bottleneck improvements). However, improvements provided by freeway management strategies and systems are typically not reflected in such procedures. Moreover, information on performance measures (e.g., vehicle delays, fuel consumption, emissions) is not provided by capacity analysis techniques. It may therefore be worthwhile to utilize traffic simulation models, which can examine the manner the freeway network performs under various sets of simulated conditions. As implied by the name, traffic simulation models examine the manner in which the roadway network performs under various sets of "simulated" conditions. They provide an excellent means of estimating changes in freeway performance metrics (e.g., average speeds, travel time, delays, emissions) resulting from freeway management strategies and improvements. Simulation models have been successfully used to evaluate the impacts of adding HOV lanes, auxiliary lanes, and truck climbing lanes; freeway widening and reconstruction; modifications to interchanges and weaving sections; ramp metering; incident management (e.g., the reduced time to respond and clear a capacity-reducing incident); and traveler information (by inputting an assumed level of diversion resulting from the information). Traffic simulation models can be divided into the following two general classes: Macroscopic simulation models – Macroscopic simulation models are based on deterministic relationships of flow, speed, and density of the traffic stream. The simulation in a macroscopic model takes place on a section-by-section basis rather than tracking individual vehicles. Macroscopic simulation models were originally developed to model traffic in distinct transportation networks, such as freeways, corridors (including freeways and parallel arterials), surface street grid networks, and rural highways. They consider platoons of vehicles and simulate traffic flow in small time increments. Macroscopic simulation models operate on the basis of aggregate speed/volume and demand/capacity relationships. Validation of macroscopic simulation models involves replication of observed congestion patterns. Macroscopic models have considerably less demanding computer requirements than microscopic models. They do not, however, have the ability to analyze transportation improvements in as much detail as microscopic models, and do not consider trip generation, trip distribution, and mode choice in their evaluation of changes in transportation systems (19). Examples include TRANSYT-7F and FREQ. Microscopic simulation models – Microscopic simulation models simulate the movement of individual vehicles, based on theories of car-following and lane-changing. Typically, vehicles enter a transportation network using a statistical distribution of arrivals (a stochastic process), and are tracked through the network on a second-by-second basis. Upon entry, each vehicle is assigned a destination, a vehicle type, and a driver type. The traffic operational characteristics of each vehicle are influenced by vertical grade, horizontal curvature, and superelevation, based on relationships developed in prior research. The primary means of calibrating and validating microscopic simulation models is through the adjustment of driver sensitivity factors. Computer time and storage requirements for microscopic models are large, usually limiting the network size and the number of simulation runs that could be completed (19). Examples include CORSIM, INTEGRATION, PARAMICS, VISSIM, and Synchro/SimTraffic. Simulation tools are effective in evaluating the dynamic evolution of traffic congestion problems on transportation systems. By dividing the analysis period into time slices, a simulation model can evaluate the buildup, dissipation, and duration of traffic congestion. Simulation models, by evaluating systems of facilities, can evaluate the interference that occurs when congestion builds up at one location and impacts the capacity of another location. The individual models vary in their capabilities, limitations, and ease of use (a discussion of which is beyond the scope of this Handbook). Moreover, several models can also show results in real time on a computer monitor by a 2-dimension or 3-dimensional illustration. Simulation models are available from a variety of sources. Information about ordering several of the models mentioned herein is available at https://www.fhwa.dot.gov/environment/cmaqeat/descriptions_traffic_simulation_models.htm. A number of firms specialize in the application of simulation models. Some have their own proprietary simulation software that can be used to analyze special scenarios such as toll plaza operation (e.g., varying combinations of cash and electronic toll lanes) and border crossings. Reference 19 (Guidelines for Applying Traffic Microsimulation Modeling Software) identifies the following tasks as being typically required to develop, calibrate, and apply a microsimulation model to a typical traffic analysis project: Identification of project purpose, scope, and approach Data Collection – Microsimulation models require significant input data, including geometry (lengths, lanes, curvature); controls, existing demands (volumes, OD table), calibration data (capacities, travel times, queues), and future demands Coding – Each microsimulation model has a set of user-adjustable parameters that enable the practitioner to calibrate the model to specific local conditions. In the absence of good guidance on the appropriate procedures for determining these calibration parameters, it is possible for different practitioners to arrive at different or incorrect conclusions. Error Checking – The coded transportation network and demand data are reviewed for errors. This step is necessary to weed out coding errors before proceeding with calibration. Calibration – An initial calibration is performed to identify the values for the capacity adjustment parameters that cause the model to best reproduce observed traffic capacities in the field. If the microsimulation network includes parallel streets, then route choice will be important. In this case, a second calibration process is performed, but this time with the route choice parameters. Finally, the overall model estimates of system performance (travel times and queues) are compared to field measurements of travel times and queues. Fine-tuning adjustments are made to enable the model to better match the field measurements. Alternatives Testing – In order to avoid biasing the results, it is important to ensure that the microsimulation model for each alternative contains all of the traffic congestion associated with it. The model should start the analysis period with no congestion on the network, and it should end the analysis period with no congestion present on the network. Insufficiently long analysis periods and insufficient geographic coverage result in "missed" congestion that is not properly tabulated by the microsimulation model. Microsimulation models typically produce two types of output, including animation displays and numerical output in text files. The animation display shows the movement of individual vehicles through the network over the simulation period. Text files report accumulated statistics on the performance of the network. It is crucial that the analyst reviews both numerical and animation outputs, and not just one or the other, in order to gain a complete picture of the results. Documentation and presentation of the results A significant amount of effort generally is required to learn to use traffic simulation models, including setting up the appropriate inputs and parameters. Simulation tools also require a plethora of input data – the data requirements being generally proportional to the extent of the network being modeled. The required data can include characteristics of each link (e.g., length, number of lanes, auxiliary / HOV lanes, ramps, grade, speed limits, lane widths, pavement condition), link traffic flow information (e.g., entering / exiting volumes, ramp volumes, travel times, prevent heavy vehicles and buses, lane changing characteristics) and other types of information such as detector locations, incident characteristics (e.g., effect of lane blockage on capacity), and ramp metering operations. Additionally, considerable error checking of the data is required, along with manipulation of a large amount of potential calibration parameters. Simulation models cannot be applied to a specific facility without calibration of those parameters to actual conditions in the field. Simulation models generally require a non-trivial analysis effort. Moreover, any model-specific limitations should be taken into consideration when interpreting the outputs of simulation. Sensitivity analyses are important to developing an understanding of how reasonable the simulation estimates are, and how much confidence the analyst should place in them. In a FHWA survey of 40 state DOT and local agencies, the following were the top answers to the question: "What are the major barriers to your use of traffic analysis tools?" Lack of trained staff Lack of time Intensive data gathering requirements Cost of software Lack of confidence in results These potential issues not withstanding, simulation should be strongly considered as a key element of any process to evaluate freeway performance, particularly during the alternatives analysis and design stages. As an example, a presentation to the TRB Freeway Operations Committee in January 2002 (Reference 20) identified 111 recent simulation experiences involving several models, including CORSIM, FREQ, INTEGRATION, PARAMICS, and VISSIM. Applications of these models included analyses / evaluations of ramp metering, HOV lanes, truck climbing lanes, auxiliary lanes, interchange modifications, design alternatives, widening, growth impacts, weaving sections, reconstruction planning, ITS strategies, and overall operations. In closing, the presentation identified the following keys to successful model applications: Well designed work plan Strong internal support Model and technical training Good input and output data Model and technical support High-quality calibration Design of investigations Documented results 4.4.2.1 Future Trends FHWA has been a leader in the area of traffic simulation model development, including the development of the NETSIM and FRESIM models, and their integration into the CORSIM model. Today, FHWA continues to develop, maintain, and support the CORSIM model (now part of the Traffic Software Integrated System (TSIS) package) (Note: TSIS also includes a graphical input editor and an animation output processor), including bug fixes, training courses, and guidance documentation. When FHWA undertook this leadership role there were no commercial traffic simulation packages in the market – a situation that no longer exists. Accordingly, FHWA is now assuming more of a "market facilitator role". FHWA will not be a traffic simulation model developer, but will provide resources to stimulate the existing simulation market. Deployment will be facilitated through a combination of outreach, training, guidance, and technical support. Development activities are focused on developing new tools and improving the analytical foundation of existing tools. The NGSIM program (Next Generation SIMulation) is part of this activity. The goal of the NGSIM Program is to ensure the needs of the model users are met through improving the capability of commercial models. The products of the NGSIM program will include: Validation data sets – the sets of real-world traffic data with its corresponding data descriptions that may be used to validate the core algorithms. Core algorithms – the set of algorithms necessary to describe the fundamental behavioral models associated with the driver-vehicle-highway systems (e.g., lane change logic, gap acceptance logic, and response to traffic control devices) Documentation of the core algorithms and the validation data sets. Another trend in simulation is the development of real time models that can estimate and predict traffic conditions, thereby allowing freeway management systems to operate in more of a proactive mode. As an example, FHWA is supporting the Center for Transportation Studies at the University of Virginia in the development and evaluation of two prototype traffic estimation and prediction systems. One of these, DynaMIT, is a real-time simulation model that estimates and predicts traffic conditions, generates traveler information, and provides route guidance. The performance of DynaMIT is being evaluated using real world data from the Hampton Roads Smart Traffic Center. 4.4.3 Before and After Studies Whereas simulation models provide estimates of changes in performance measures (quite a useful tool when evaluating alternatives prior to selecting the specific freeway improvement for design and deployment); after the selected strategies have been implemented, the actual changes in performance can be measured. The most common method of evaluating this actual effectiveness is a Before-and-After study. With Before-and-After studies, the performance of the freeway network is evaluated prior to implementation of the freeway management strategies and / or system. The same performance measures are then taken again after the strategies / system have been implemented. The effectiveness of the system is then determined by comparing the performance of the freeway during the "before" and "after" conditions. Potential limitations of a Before-and-After analysis include the following: The effects of individual improvements are difficult to distinguish when more than one improvement is made at a time. It may take some time for drivers to adjust their travel behavior after the strategy / system has been implemented. Therefore, depending upon when the "after" data are collected, the true effect of the changes may not be measured. There is often a long time lag between the "before" condition and the "after" condition, which causes this approach to be susceptible to errors caused by time-related factors (such as changes in travel patterns, population growths, economic fluctuations, etc.). Some performance measures (like the number of crashes, or demand) can fluctuate considerably over time. There is a tendency for these performance measures to return to more typical values after an extraordinary value has been observed. This tendency is called regression to the mean. It is possible that either the "before" condition or the "after" condition could fall at one of these extreme values, thereby, hiding the true performance of the system. 4.4.4 Alternatives Analysis In very general terms, an alternatives analysis involves estimating the benefits and costs for each alternative, comparing these alternative-specific benefits to its costs, comparing this "cost-efficiency" for all alternatives, and then selecting the one that offers the greatest potential. 4.4.4.1 Benefits Freeway management strategies (i.e., operational improvements, low-cost geometric improvements, ITS) can produce a number of benefits, often significant in their magnitude. An overview of some of these benefits is included in Chapter 1, with additional information provided in subsequent topic-specific chapters. Several benefits can be quantified as performance measures (e.g., reduction in travel time, reduced delay, reduced emissions, reduced fuel consumption, reduced incidents) and associated indices; whereas others cannot (e.g., improvement in driver perception of the transportation agencies in the region). Furthermore, while some of the quantifiable benefits can be readily converted to a monetary value (e.g., fuel consumption, person delay), other benefits, such as emission reductions, do not easily lend themselves to monetary conversions (at least not without some significant assumptions). Another consideration when estimating benefits (and in developing alternatives) is to fully recognize the synergies that can develop from implementing certain combinations of freeway management elements and / or ITS components. For example, if deployed independently, ramp widening, ramp metering, and retiming of signals at nearby intersections would likely improve operations; but combined, the benefits could be significant. Similarly, implementation of closed-circuit television may not only assist in the verification and response-determination of an incident, but also prove useful in verifying whether a traffic message is properly displayed on a nearby changeable message sign. At the same time, it is important to realistically assess how certain elements or components will actually perform, given the presence of other improvements and subsystems. In some cases, the interrelationships are such that the benefits of stand-alone elements may not be additive, as in the case of automated incident detection algorithms (and the associated surveillance infrastructure) combined with a toll free telephone number established for cellular telephone users to call in and report incidents – quickly detecting the same incident twice (once by each subsystem) does not double the benefits. The freeway practitioner must also recognize the fact that whereas freeway management costs are "real dollars" obligated by a government agency and ultimately funded by taxpayers; the benefits, while very real in terms of improved operations and safety, may not always translate well into dollar equivalents – that is, the monetary value of the benefits does not represent actual funds that accrue back to an agency or that are recognized by individual travelers. Moreover, these benefits may not be as highly valued in the political decision arena as more traditional highway improvements involving significant amounts of concrete and asphalt. As discussed in Chapter 2, the freeway practitioner must endeavor to promote a more widespread appreciation of the relatively high cost-effectiveness of freeway management and operations. Information on benefits can be obtained from a variety of sources, including: Simulation (as discussed in section 4.4.2) ITS Deployment Analysis System (IDAS, discussed in section 4.4.4.6) Other similar improvements and systems (e.g., www.benefitcost.its.dot.gov for ITS-related benefits) with the caveat that great care must be taken when using representative benefits of similar systems and programs. The user must consider potential differences in the features and functionality of the programs, location and topography, the existing traffic conditions before implementation, the existence and stability of working relationships between agencies, the specific combination of elements and subsystems incorporated into the overall freeway management program – all of which contribute to its overall success and impact of a freeway management and operations program. 4.4.4.2 Costs Costs associated with freeway management improvements may be classified as follows: Capital costs include all costs associated with the implementation of the freeway management strategies and systems, including planning, design, right-of-way, equipment, construction, maintenance & protection of traffic during construction, software development and licensing, system integration, and testing. Continuing costs are those associated with ongoing operations of the freeway management program, including equipment and infrastructure maintenance costs, equipment replacement, staffing costs to operate the system (operations personnel, clerical personnel, public information personnel, etc.), utilities costs, software updates, and leasing costs (communications, control center space, etc.). Continuing costs are just as important as, if not more important than, capital costs. Adequate funding for operations and maintenance, including funding to replace system components when their useful lives have expired, is essential for successful freeway management. It is crucial that the life-cycle costs of the program must be determined in terms of its complete implementation and operating schedule, recognizing that a freeway management and operations program will likely entail many separate steps, with elements that are deployed and become operational at various points in time. In developing life-cycle costs, the time stream of capital and operating / maintenance costs must be determined and net present worth techniques applied (e.g., discount the annual recurring costs and sum with capital costs to derive the net present value). Information on costs can be obtained from a variety of sources, including: ITS Deployment Analysis System (IDAS, discussed in section 4.4.4.6) Experience of other programs and systems (e.g., www.benefitcost.its.dot.gov for ITS-related costs, and selected DOT web sites), with the caveat that great care must be taken when using representative costs of similar systems and programs. The user must consider potential differences in methods of construction and integration, timing (i.e., inflation), location and topography, and what all is included in a particular item (e.g., does the DMS cost include the support structure). 4.4.4.3 Benefit-Cost Analysis The Benefit-Cost (B/C) analysis technique is perhaps the most widely accepted methodology for evaluating transportation improvement alternatives. The B/C ratio is simply the equivalent benefit of an alternative divided by the equivalent cost of that alternative: B/C = (benefits of alternative i) / (costs of alternative i) Benefit-cost comparisons are possible when the benefits of an improvement can be assigned a monetary value. If the benefits of an alternative exceed its costs, the improvement is economically justifiable. Furthermore, the ratio of each alternative provides a convenient basis for comparison, providing a measure of the dollars of expected benefit of an alternative for each dollar spent on that alternative. If system alternatives being analyzed build upon each other in terms of the costs, quantities, complexities, etc. of components that meet the system goals and objectives, it may be more appropriate to consider an incremental benefit-cost analysis. For this approach, the benefits and costs considered for each alternative are not the totals, but rather the additional benefits achieved and costs incurred over the next expensive (and presumably effective) alternative. This analysis considers, in effect, whether an investment necessary to achieve the next incremental step in the system can be justified in terms of the incremental benefits that would be achieved. The benefit-cost (or incremental benefit cost) analysis methodology provides an objective means of comparing the quantifiable and monetarily-based benefits of an alternative to the costs of that alternative. However, as already discussed, some freeway management benefits are not easily quantified, and not all quantifiable benefits are easily converted to a monetary value. Because of this, alternative analyses are often needed to help assess which alternatives systems or subsystems meet their objectives in the most economical manner. One such analysis approach is utility cost. 4.4.4.4 Net Present Value Computation of an alternative's net present worth involves a conversion of all costs and benefits of an alternative that are incurred at the alternative's initiation and throughout its useful life (life-cycle) to an equivalent current value. The current value of the equivalent costs is subtracted from the current value of the equivalent benefits of the alternative. If the benefits exceed the costs, the alternative can be justified economically. Furthermore, comparisons among alternatives are straightforward; the alternative that provides the greatest additional benefits over costs (sometimes referred to as "excess benefits") is said to have the greatest net present worth. 4.4.4.5 Utility-Cost Analysis Although a benefit-cost (or incremental benefit-cost) analysis is a direct method of determining whether a freeway management alternative is economically viable, such an analysis can be performed only if the benefits to be accrued can be estimated in monetary terms. For many goals and objectives of freeway management, this is not possible. In these cases, a utility-cost analysis approach is commonly utilized. The term cost-effectiveness is sometimes used interchangeably with the term utility-cost analysis. In a utility-cost analysis, utility measures of performance goals or objectives are created to estimate system benefits. Typically, a project team or expert panel subjectively rates (from 0 to 10 or on a similar scale) how well an alternative is expected to achieve each of the objective or performance criteria. Weighting factors (summing to unity) are also estimated for each of the objective or performance criteria, and multiplied by the rating given to that objective/criterion. These "utilities" of each of the objective/criteria are then summed to determine the total system utility. Dividing the system utility by total system cost represents the utility-cost factor for a particular system. The basic steps in a utility-cost analysis are as follows: Define goals and subgoals (done as part of the decision process). Weigh each goal. Weigh each subgoal. Rate the utility of each alternative in satisfying each goal/subgoal. Multiply the rating by the weight for each goal / subgoal, and sum over all goals for each alternative (i.e., calculate the utility) Compute utility-cost ratio. 4.4.4.6 ITS Deployment Analysis System (IDAS) The ITS Deployment Analysis System (IDAS) is software developed by the Federal Highway Administration that can be used in planning for Intelligent Transportation System (ITS) deployments. It is a modeling tool at the sketch planning level that enables the user to conduct systematic assessments and quantitative evaluations of the relative benefits and costs of more than 60 types of ITS investments (at the time of this writing), in combination or in isolation. IDAS has a number of useful features. For example, IDAS: Works with the output of existing transportation planning models; Compares and screens ITS deployment alternatives; Estimates the impacts and traveler responses to ITS; Develops inventories of ITS equipment needed for proposed deployments and identifies cost sharing opportunities; Estimates life-cycle costs including capital and O&M costs for the public and private sectors; Provides documentation for transition into design and implementation. The model utilizes network and trip data from the regional transportation model. Strategies are applied either for links in the transportation network or at the traffic analysis zone level. Strategies that affect the time or cost of travel affect mode choice, temporal choice, and induced/foregone demand through a "pivot-point" model, which is based on coefficients from the regional travel model. Other strategy impacts are based on findings from various empirical studies. Changes in trips by mode, time of day, and origin/destination subsequently affect vehicle speeds and volumes. Required data include transportation network and trip tables by mode and/or purpose, which can be obtained from the regional travel model, and deployment of ITS strategies by type and location on the transportation network. The outputs include changes in vehicle-trips, VMT, emissions; travel time savings and improvements in travel time reliability; energy consumption, noise impacts, safety impacts, and monetary values of these changes; and lists of ITS equipment and costs. IDAS requires some time investment to learn and some user skills – in particular, it is helpful to have familiarity with travel model data in setting up the model. Data entry and alternatives analysis are conducted in a user-friendly Windows environment. Run time is non-trivial (anywhere from a few minutes to a few hours, depending on the number of zones and other factors.) 4.5 Closing, and a Look Forward Performance measures are important and valuable indices for evaluating the transportation system operating conditions, identifying problems (e.g., congestion and delays, poor operating speeds, crashes, large fluctuations in travel times / average speeds), and their locations and severity. As discussed in Chapter 2, having identified the problems, the next step for the freeway practitioner is to develop alternative improvements and strategies for alleviating (or at least reducing the impact of) these problems, and then analyzing and evaluating these alternatives (using one or more of the tools and techniques described in Section 4.4) to determine the optimum alternative or combination of alternatives. The remainder of this Freeway Management and Operations Handbook (i.e., Chapters 5 – 17) describes numerous alternatives for improving the operation and safety of a freeway facility. What is "best" for a particular location is dependent on a number of factors and considerations specific to that location, including the roadway geometrics, signs and markings, weather, lighting, driver population and their behavior, the mix of vehicles in the traffic flow, locations and characteristics of major traffic generators, the institutional environment and the associated goals and policies of decision makers, the extent of any ITS deployment, the features and functionality of any existing management systems, just to name a few. It is the responsibility of the practitioner to consider all such variables in the analysis. Moreover, as discussed in Chapter 2, when identifying potential operational improvements and enhancements, the practitioner must consider a wide range of possibilities and perspectives – including the perspective of enhanced transportation services (i.e., the "supply" of transportation), the perspective of those who use these services (i.e., better managing the "demand" for the transportation system), the perspective of influencing where this demand occurs (i.e., the land use dimension), or any combination of the above. Practitioners should also carefully consider how individual actions relate to one another and how, when combined into an overall program, they relate to area, regional and statewide objectives. 4.6 References 1. "Implementing Performance Measurement in Transportation Agencies"; Hal Kassoff, Parsons Brinckerhoff Quade & Douglas; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26. 2. "Performance Measures of Operational effectiveness for Highway Segments and Systems – A Synthesis of Highway Practice"; NCHRP Synthesis 311; Transportation Research Board; Washington D.C.; 2003. 3. "Performance Measurement & Integrated Transportation Management Systems – A Traffic Operations Perspective"; Wolf; from the 4th conference on Integrated Transportation Management Systems; Newark, NJ; July 2001. 4. "Measuring That Which Cannot Be Measured—At Least According to Conventional Wisdom"; Michael Meyer, School of Civil and Environmental Engineering, Georgia Institute of Technology; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26. 5. "Use of Performance Measures in Transportation Decision Making"; Steven Pickrell and Lance Neumann, Cambridge Systematics, Inc; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26. 6. Executive Summary; "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26. 7. "Transportation Data and Performance Measurement"; Doug Dalton, Joseph Nestler, John Nordbo, Bob St. Clair, Ernest Wittwer, and Mark Wolfgram, Wisconsin Department of Transportation; from the "Conference on Performance Measures to Improve Transportation Systems and Agency Operations"; Irvine, California; October 2000; TRB Conference Proceedings 26. 8. NHI Training Course on CMS 9. "FHWA's Mobility Monitoring Program: Transforming Gigabytes of Archived Operations Data Into Mobility and Reliability Performance Measures"; Shawn Turner, Tim Lomax, Rich Margiotta and Vince Pearce 10. "Monitoring Urban Roadways in 2000: Using Archived Operations Data for Reliability and Mobility Measurement"; Tim Lomax, Shawn Turner, Texas Transportation Institute, and Richard Margiotta, Cambridge Systematics, Inc.; FHWA, December 2001. 11. "Guidelines For Transportation Management Systems Maintenance Concepts and Plans (Draft)", PB Farradyne, July, 2002 12. "Cross-Cutting Studies and State-of-the-Practice Reviews: Archive and Use of ITS-Generated Data"; Center for Transportation Analysis, Oak Ridge National Laboratory; FHWA; April 30, 2002 13. "Guidelines for Developing ITS Data Archiving Systems"; Report 2127-3; Texas Transportation Institute; September 2001 14. Hallenbeck, Mark; "Data Collection, Archiving and Performance Measures: Why Should Freeway Operations Care?"; Newsletter of the ITS Cooperative Deployment Network; March 2003. 15. "Manual on Transportation Engineering Studies"; ITE; Washington, D.C. 16. Hallenbeck, Marc; "Operations Planning – The Case for Archived Data User Services"; Presentation to Freeway Operations Committee of TRB; January 2003 17. "Decision Support Methodology for Selecting Traffic Analysis Tools"; FHWA; January 2003. 18. Highway Capacity Manual, Transportation Research Board, National Research Council, Washington D.C: 2000 19. "Guidelines for Applying Traffic Microsimulation Modeling Software – Draft"; Dowling Associates, Inc. in association with Cambridge Systematics, Inc; FHWA; December 2002 20. May, Dolf; University of California; "Recent California Freeway Simulation Model Experiences"; Presentation to Freeway Operations Committee of TRB; January 2003 Previous \| Next
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