1.0 Background and Objectives

Entering the 21st century, the Nation's transportation system has matured; it only expands its infrastructure by a fraction of a percentage each year. However, congestion continues to grow at an alarming rate, adversely impacting our quality of life and increasing the potential for crashes and long delays. These are expected to escalate, calling for transportation professionals to increase the productivity of existing transportation systems through the use of operational improvements. To assess the potential effectiveness of a particular strategy, it must be analyzed using traffic analysis tools or methodologies.

There are several traffic analysis methodologies and tools available for use; however, there is little or no guidance on which tool should be used. These tools all vary in their scope, capabilities, methodology, input requirements, and output. In addition, there is no one tool that can address all of the analytical needs of a particular agency.

The objective of Decision Support Methodology for Selecting Traffic Analysis Tools (Volume II) is to assist traffic engineers, planners, and traffic operations professionals in the selection of the correct type of traffic analysis tool for operational improvements. This document is intended to assist practitioners in selecting the category of tool for use (e.g., Highway Capacity Manual (HCM) versus traffic simulation); this document does not include an assessment of the capabilities of specific tools within an analytical tool category. Another objective of this document is to assist in creating analytical consistency and uniformity across State departments of transportation (DOTs) and Federal/regional/local transportation agencies.

Decision Support Methodology for Selecting Traffic Analysis Tools identifies the criteria that should be considered in the selection of an appropriate traffic analysis tool and helps identify the circumstances when a particular type of tool should be used. A methodology is also presented to guide users in the selection of the appropriate tool category. This document includes worksheets that transportation professionals can use to select the appropriate tool category and provides assistance in identifying the most appropriate tool within the selected category. An automated tool that implements this methodology can be found at the Federal Highway Administration (FHWA) Traffic Analysis Tools Web site at: http://ops.fhwa.dot.gov/Travel/Traffic_Analysis_Tools/traffic_analysis_toolbox.htm

This methodology was developed for FHWA. The FHWA Traffic Analysis Tools Team made extensive contributions to this document and to the automated tool. This document is organized into the following sections:

• Section 1.0: Background and Objectives: Describes the objectives of the document and highlights the need for and the role of traffic analysis tools, including the definitions of the analytical tool categories covered in this document. This section also presents a comparison of HCM with traffic simulation models.
• Section 2.0: Criteria for Selecting the Appropriate Type of Traffic Analysis Tool: Identifies the criteria that should be considered in the selection of an appropriate traffic analysis tool and helps identify the circumstances when a particular type of tool should be used. A methodology is presented to guide users in the selection of the appropriate tool category.
• Section 3.0: Methodology for Selecting a Traffic Analysis Tool: Provides guidance to users on how to use the criteria in section 2.0 to select the appropriate analytical tool category. This section includes worksheets that transportation professionals can use to select the appropriate tool category and assistance in identifying the most appropriate tool within the selected category.
• Section 4.0: Available Traffic Analysis Tools: Presents a list of available analytical tools.
• Section 5.0: Challenges and Limitations in the Use of Traffic Analysis Tools: Highlights some of the challenges and limitations of the analytical tools for consideration by users.
• Appendix A: Limitations of HCM: Lists the limitations of the HCM methodologies.
• Appendix B: Tool Category Selection Worksheet: Contains a worksheet that can be used to select an appropriate tool category for the task.
• Appendix C: Tool Selection Worksheet: Contains a worksheet that can assist users with the selection of a specific traffic analysis tool.
• Appendix D: Recommended Reading: Contains a list of documents that discuss or compare some of the specific traffic analysis tools.
• Appendix E: Traffic Analysis Tools by Category: Provides a list of analytical tools by category and their Web site links. This is only intended to be a starting point for users once they have selected an analytical tool category.
• Appendix F: References: Documents the literature reviewed and used in the development of this document.

1.1 Overview of the Transportation Analysis Process

The Intermodal Surface Transportation Efficiency Act (ISTEA), the Transportation Equity Act for the 21st Century (TEA-21), and Federal/State Clean Air legislation have reinforced the importance of traffic management and control of existing highway capacity. As transportation agencies deploy more sophisticated hardware and software system management technologies, there is an increased need to respond to recurring and nonrecurring congestion in a proactive fashion, and to predict and evaluate the outcome of various improvement plans without the inconvenience of a field experiment.

Out of these needs, traffic analysis tools emerge as one of the most efficient methods to evaluate transportation improvement projects. This document addresses quantifiable traffic operations analytical tools categories, but does not include real-time or predictive models. Traffic analysis tools may include software packages, methodologies, and procedures, and are defined as those typically used for the following tasks:

• Evaluating, simulating, or optimizing the operations of transportation facilities and systems.
• Modeling existing operations and predicting probable outcomes for proposed design alternatives.
• Evaluating various analytical contexts, including planning, design, and operations/construction projects.

Figure 1 presents an overview of the transportation analysis process, along with its various evaluation contexts and the types of traffic analysis tools that are typically used in each context. Typically, transportation analysis needs result from the policies and objectives of State/regional/local transportation plans and programs. A transportation improvement (project) goes through several phases, including planning, project development, design, implementation, and post-implementation operational assessment and modification. As shown in Figure 1, each of these phases requires different analytical methodologies and tools. A project's early planning stage usually involves the application of sketch-planning or travel demand modeling techniques. These methodologies help agencies screen the different transportation improvements, resulting in the selection of a few candidate transportation improvements. Later stages (such as project development or post-implementation modifications) usually involve the application of more rigorous and detailed techniques, such as traffic simulation and/or optimization. The role of traffic analysis tools is further explained in the following section.

Figure 1. Overview of the transportation analysis process.

1.2 Role of Traffic Analysis Tools

Traffic analysis tools are designed to assist transportation professionals in evaluating the strategies that best address the transportation needs of their jurisdiction. Specifically, traffic analysis tools can help practitioners:

• Improve the decision-making process. Traffic analysis tools help practitioners arrive at better planning/engineering decisions for complex transportation problems. They are used to estimate the impact of the deployment of traffic management and other strategies, and to help set priorities among competing projects. In addition, they can provide a consistent approach for comparing potential improvements or alternatives.
• Project potential future traffic. Traffic analysis tools can be used to project and analyze future traffic conditions. This is especially useful for planning long-term improvements and evaluating future impact.
• Evaluate and prioritize planning/operational alternatives. This typically involves comparing "no build" conditions with alternatives, which include various types of potential improvements. The impacts are reported as performance measures and are defined as the difference between the no-build and alternative scenarios. The results can be used to select the best alternative or prioritize improvements, increasing the odds of having a successful deployment.
• Improve design and evaluation time and costs. Traffic analysis tools are relatively less costly when compared to pilot studies, field experiments, or full implementation costs. Furthermore, analytical tools can be used to assess multiple deployment combinations or other complex scenarios in a relatively short time.
• Reduce disruptions to traffic. Traffic management and control strategies come in many forms and options, and analytical tools provide a way to cheaply estimate the effects prior to full deployment of the management strategy. They may be used to initially test new transportation management systems concepts without the inconvenience of a field experiment.
• Present/market strategies to the public/stakeholders. Some traffic analysis tools have excellent graphical and animation displays, which could be used as tools to show "what if" scenarios to the public and/or stakeholders.
• Operate and manage existing roadway capacity. Some tools provide optimization capabilities, recommending the best design or control scenarios to maximize the performance of a transportation facility.
• Monitor performance. Analytical tools can also be used to evaluate and monitor the performance of existing transportation facilities. In the future, it is hoped that monitoring systems can be directly linked to analytical tools for a more direct and real-time analytical process.

1.3 Categories of Traffic Analysis Tools

The intent of this document is to provide guidance on the selection of the appropriate type of analytical tool, not the specific tool. To date, numerous traffic analysis methodologies and tools have been developed by public agencies, research organizations, and various consultants. Traffic analysis tools can be grouped into the following categories:

• Sketch-Planning Tools: Sketch-planning methodologies and tools produce general order-of-magnitude estimates of travel demand and traffic operations in response to transportation improvements. They allow for the evaluation of specific projects or alternatives without conducting an in-depth engineering analysis. Sketch-planning tools perform some or all of the functions of other analytical tools using simplified analytical techniques and highly aggregated data. For example, a highway engineer can estimate how much it will cost to add a lane to an existing roadway simply by using sketch-planning techniques and without doing a complete site evaluation. Similarly, traffic volume-to-capacity ratios are often used in congestion analyses. Such techniques are primarily used to prepare preliminary budgets and proposals, and are not considered a substitute for the detailed engineering analysis often needed later in the implementation process. Therefore, sketch-planning approaches are typically the simplest and least costly of the traffic analysis techniques. However, sketch-planning techniques are usually limited in scope, analytical robustness, and presentation capabilities.
• Travel Demand Models: Travel demand models have specific analytical capabilities, such as the prediction of travel demand and the consideration of destination choice, mode choice, time-of-day travel choice, and route choice, and the representation of traffic flow in the highway network. These are mathematical models that forecast future travel demand based on current conditions and future projections of household and employment characteristics. Travel demand models were originally developed to determine the benefits and impact of major highway improvements in metropolitan areas. However, they were not designed to evaluate travel management strategies, such as intelligent transportation systems (ITS)/operational strategies. Travel demand models only have limited capabilities to accurately estimate changes in operational characteristics (such as speed, delay, and queuing) resulting from implementation of ITS/operational strategies. These inadequacies generally occur because of the poor representation of the dynamic nature of traffic in travel demand models.
• Analytical/Deterministic Tools (HCM-Based): Most analytical/deterministic tools implement the procedures of the Highway Capacity Manual (HCM). The HCM procedures are closed-form, macroscopic, deterministic, and static analytical procedures that estimate capacity and performance measures to determine the level of service (e.g., density, speed, and delay). They are closed-form because they are not iterative. The practitioner inputs the data and the parameters and, after a sequence of analytical steps, the HCM procedures produce a single answer. Moreover, the HCM procedures are macroscopic (input and output deal with average performance during a 15-minute or a 1-hour analytical period), deterministic (any given set of inputs will always yield the same answer), and static (they predict average operating conditions over a fixed time period and do not deal with transitions in operations from one system state to another). As such, these tools quickly predict capacity, density, speed, delay, and queuing on a variety of transportation facilities and are validated with field data, laboratory test beds, or small-scale experiments. Analytical/deterministic tools are good for analyzing the performance of isolated or small-scale transportation facilities; however, they are limited in their ability to analyze network or system effects. The HCM procedures and their strengths and limitations are discussed in more detail in section 1.4.
• Traffic Signal Optimization Tools: Similar to the analytical/deterministic tools, traffic optimization tool methodologies are mostly based on the HCM procedures. However, traffic optimization tools are primarily designed to develop optimal signal phasings and timing plans for isolated signal intersections, arterial streets, or signal networks. This may include capacity calculations; cycle length; splits optimization, including left turns; and coordination/offset plans. Some optimization tools can also be used for optimizing ramp metering rates for freeway ramp control. The more advanced traffic optimization tools are capable of modeling actuated and semi-actuated traffic signals, with or without signal coordination.
• Macroscopic Simulation Models: Macroscopic simulation models are based on the deterministic relationships of the flow, speed, and density of the traffic stream. The simulation in a macroscopic model takes place on a section-by-section basis rather than by tracking individual vehicles. Macroscopic simulation models were originally developed to model traffic in distinct transportation subnetworks, 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 brief time increments. Macroscopic simulation models operate on the basis of aggregate speed/volume and demand/capacity relationships. The validation of macroscopic simulation models involves replication of observed congestion patterns. Freeway validation is based on both tachometer run information and speed contour diagrams constructed for the analytical periods, which are then aggregated to provide a "typical" congestion pattern. Surface-street validation is based on speed, queue, delay, and capacity information. Macroscopic models have considerably fewer 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.
• Mesoscopic Simulation Models: Mesoscopic models combine the properties of both microscopic (discussed below) and macroscopic simulation models. As in microscopic models, the unit of traffic flow for mesoscopic models is the individual vehicle. Similar to microscopic simulation models, mesoscopic tools assign vehicle types and driver behavior, as well as their relationships with roadway characteristics. Their movement, however, follows the approach of macroscopic models and is governed by the average speed on the travel link. Mesoscopic model travel prediction takes place on an aggregate level and does not consider dynamic speed/volume relationships. As such, mesoscopic models provide less fidelity than microsimulation tools, but are superior to the typical planning analysis techniques.
• Microscopic Simulation Models: Microscopic simulation models simulate the movement of individual vehicles based on car-following and lane-changing theories. Typically, vehicles enter a transportation network using a statistical distribution of arrivals (a stochastic process) and are tracked through the network over brief time intervals (e.g., 1 second or a fraction of a second). Typically, upon entry, each vehicle is assigned a destination, a vehicle type, and a driver type. In many microscopic simulation models, 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 are through the adjustment of driver sensitivity factors. Computer time and storage requirements for microscopic models are significant, usually limiting the network size and the number of simulation runs that can be completed.

1.4 Comparison of HCM and Simulation

The intent of this section is to provide an overview of the strengths and limitations of the HCM and traffic simulation tools and to provide additional guidance on assessing when traffic simulation may be more appropriate than the HCM-based methods or tools.

1.4.1 Overview of HCM

HCM is a compilation of peer-reviewed procedures for computing the capacity and operational performance of various transportation facilities. HCM was first produced in 1950 and has undergone many major revisions since then. It is currently published by the Transportation Research Board. The current edition of HCM was produced in 2000.

Highway Capacity Manual 2000 (HCM 2000) has more than 1,100 pages and 30 chapters. Parts I and II of the manual present introductory information on capacity and the quality of service analysis. Part III presents the actual analytical procedures. Part IV provides information on applying HCM to corridor and areawide planning analyses. Part V provides introductory materials on models that go beyond the HCM procedures described in part III.

Each chapter in part III focuses on a specific facility type and capacity analysis problem. For example, there are four chapters devoted to freeway facilities: freeway facilities, basic freeway segments, ramps and ramp junctions, and freeway weaving. There are three chapters devoted to the analysis of urban facilities: urban streets, signalized intersections, and unsignalized intersections. There are also chapters that cover procedures for the analysis of multilane highways, two-lane rural roads, transit, pedestrian facilities, and bicycle facilities.

The HCM procedures are closed-form, macroscopic, deterministic, and static analytical procedures that estimate capacity, and performance measures to determine the level of service (e.g., density, speed, and delay). They are closed-form because 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. In general, the HCM procedures have the following characteristics:

• Macroscopic: HCM's input and output deal with average performance during a 15-minute or 1-hour analytical period.
• Deterministic: Any given set of input will always yield the same answer.
• Static: The HCM procedures predict average operating conditions over a fixed time period and do not deal with transitions in operation from one system state to another (such as would be addressed in a dynamic analysis).

1.4.2 HCM Strengths and Limitations

For many applications, HCM is the most widely used and accepted traffic analysis technique in the United States. The HCM procedures are good for analyzing the performance of isolated facilities with relatively moderate congestion problems. These procedures are quick and reliable for predicting whether or not a facility will be operating above or below capacity, and they have been well tested through significant field-validation efforts. However, the HCM procedures are generally limited in their ability to evaluate system effects.

Most of the HCM methods and models assume that the operation of one intersection or road segment is not adversely affected by conditions on the adjacent roadway. Long queues at one location that interfere with another location would violate this assumption. The HCM procedures are of limited value in analyzing queues and the effects of the queues.

There are also several gaps in the HCM procedures. HCM is a constantly evolving and expanding set of analytical tools and, consequently, there are still many real-world situations for which HCM does not yet have a recommended analytical procedure. The following list identifies some of these gaps:

• Multilane or two-lane rural roads where traffic signals or stop signs significantly impact capacity and/or operations.
• Climbing lanes for trucks.
• Short through-lane is added or dropped at a signal.
• Two-way left-turn lanes.
• Roundabouts of more than a single lane.
• Tight diamond interchanges.

Appendix A summarizes the limitations of HCM based on information listed in HCM 2000.

1.4.3 Simulation Strengths and Limitations

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

Simulation tools, however, require a plethora of input data, considerable error checking of the data, and 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. Calibration can be a complex and time-consuming process.

The algorithms of simulation models are mostly developed independently and are not subject to peer review and acceptance in the professional community. There is no national consensus on the appropriateness of a simulation approach.

Simulation models, for all their complexity, also have limitations. Commercially available simulation models are not designed to model the following:

• Two-way left-turn lanes.
• Impact of driveway access: Major driveways can be modeled as unsignalized T-intersections. However, models cannot address the impact of numerous minor driveways along a street segment (link). They can only be approximately modeled as a midblock sink or node.
• Impact of on-street parking, commercial vehicle loading, and double parking (although such conditions may be approximately modeled as short-term incidents).
• Interferences that can occur among bicycles, pedestrians, and vehicles sharing the same roadway.

Simulation models also assume "100 percent safe driving," so they will not be effective in predicting how changes in design might influence the probability of collisions. In addition, simulation models do not take into consideration how changes in the roadside environment (outside of the traveled way) affect driver behavior within the traveled way (e.g., obstruction of visibility, roadside distractions such as a stalled vehicle, etc.).

1.4.4 Traffic Performance Measures: Differences Between HCM and Simulation

The HCM methodologies and tool procedures take a static approach to predicting traffic performance; simulation models take a dynamic approach. HCM estimates the average density, speed, or delay over the peak 15 minutes of an hour, while simulation models predict density, speed, and delay for each time slice within the analytical period (which can be longer than an hour).

Not only are there differences in approach, there are differences in the definitions of the performance measures produced by simulation models and the HCM tools. Some of the most notable differences include:

• Simulation models report density for actual vehicles, while HCM reports density in terms of equivalent passenger cars (trucks and other heavy vehicles are counted more than once in the computation of density).
• Simulation models report vehicle flow in terms of actual vehicles, while HCM reports capacity for freeways and highways in terms of passenger-car equivalents.
• Simulation models report delay only on the street segment where the vehicles are slowed down, while HCM reports all delays caused by a given bottleneck (regardless of the actual physical location of the vehicles).
• Simulation models report queues only on the street segment where the vehicles are actually queued, while HCM reports all queued vehicles resulting from a given bottleneck (regardless of the actual physical location of the vehicles).
• Simulation models do not necessarily report control delay at signalized intersections. The reported values include midblock delays for the vehicles traveling along the link, or only stopped delay at the traffic signal.

1.4.5 Strategy for Overcoming the Limitations of HCM

Once a transportation professional has decided that the HCM procedures do not meet the needs of the analysis, the next step is to determine whether microscopic, mesoscopic, or macroscopic simulation is required. There are several simulation programs available for evaluating a variety of transportation improvements, facilities, modes, traveler responses, and performance measures. These analytical tools vary in their data requirements, capabilities, methodology, and output. In addition, the performance measures for the simulation models and the HCM procedures may differ in definition and/or methodology (e.g., the number of stops may be estimated at speeds of less than 8 kilometers per hour (km/h) (5 miles per hour (mi/h)) in one tool, but at 0 km/h for another).

If it is not necessary to microscopically trace individual vehicle movement, then the analyst can take advantage of the simpler data entry and control optimization features available in many macroscopic simulation models. However, macroscopic models often have to make certain assumptions of regularity in order to be able to apply macroscopic vehicle behavior relationships. If these assumptions are not valid for the situation being studied, then the analyst must resort to mesoscopic or microscopic simulation.

Simulation models require a considerable amount of detailed data for input, calibration, and validation. In general, microscopic simulation models have more demanding data requirements than mesoscopic and macroscopic models. Simulation models are also more complicated and require a considerable amount of effort to gain an understanding of the assumptions, parameters, and methodologies involved in the analysis. The lack of understanding of these tools often makes credibility and past performance (use/popularity) a major factor in the selection of a particular simulation tool.

More information on this issue may be found in Guidelines for Applying Traffic Microsimulation Modeling Software (Volume III).