NOTICE

This document is disseminated under the sponsorship of the U.S. Department 

of Transportation in the interest of information exchange. The U.S. Government 

assumes no liability for the use of the information contained in this document. This 

report does not constitute a standard, specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names may appear in this report only because they are considered 

essential to the objective of the document.

The publication of this document was sponsored by the U.S. Federal Highway 

Administration under contract DTFH61-06-00004, awarded to Cambridge 

Systematics, Inc. 

QUALITY ASSURANCE STATEMENT

The Federal Highway Administration (FHWA) provides high-quality information 

to serve Government, industry, and the public in a manner that promotes public 

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improvement. 
1. Report No.

FHWA-HOP-09-042

2. Government Accession No.

 

3. Recipient’s Catalog No.

 

4. Title and Subtitle

Localized Bottleneck Congestion Analysis: Focusing on what 

analysis tools are available, necessary, and productive in localized 

congestion remediation

5. Report Date

 March 2010

6. Performing Organization Code

 

7. Author(s)

Albinder Dbindsa, Cambridge Systematics, Neil Spiller, FHWA

8. Performing Organization Report 

No.

9. Performing Organization Name and Address

Cambridge Systematics, Inc.

555 12th Street, Suite 1600

Oakland, CA 94607

10. Work Unit No.

 

11. Contract or Grant No.

12. Sponsoring Agency Name and Address

US Department of Transportation

Federal Highway Administration/Office of Transportation 

Management

1200 New Jersey Avenue S.E.

Washington, D.C. 94607

13. Type of Report and Period 

Covered

14. Sponsoring Agency Code

HoTM

15. Supplementary Notes

Contact: Neil Spiller, FHWA, HoTM, 202-366-2188, neil.spiller@dot.gov

16. Abstract

In the past, much, or all, of recurring congestion was felt to be a systemic problem ("not enough lanes") 

but much of the root cause of recurring congestion is in fact subordinate locations within a facility; i.e., 

"bottlenecks" and chokepoints. Elsewhere on the same facility and during the same hours, the facility 

runs free. This document is meant to discuss when, where and how to study small, localized sections 

of a facility (e.g., on/off ramps, merges, lane drops, intersections, weaves, etc.) In cost-effective means. 

Some chokepoints are (or seem) obvious in their solution; add a turn lane, widen a stretch of highway, 

retime a signal, or separate a movement by ramp. However, the solution can often lead to hidden or 

supplementary problems; hidden bottlenecks, disruptions upstream, or undue influence on abutting 

accesses, etc Analyzing localized sections of highway is different from analyzing entire corridors or 

regions. Micro simulation analysis products vary in their target applications and purported results. 

This document will provide guidance that specifies the choice of analysis tools and inputs necessary to 

analyze localized problem areas. It also provides some guidance as to when analysis it warranted, and 

what data inputs are required.

17. Key Words

Bottleneck, chokepoint, micro simulation, 

localized

18. Distribution Statement.

No Restrictions

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No of Pages

30

22. 

Price

 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized

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Table of Contents

1.0 Introduction.................................................................................................... 1

2.0 Background and Discussion.......................................................................... 2

2.1 Common Causes of Bottlenecks.........................................................................................2

2.2 What Role does Analysis Play? ..........................................................................................3

2.3 Bottleneck Mitigation..........................................................................................................3

3.0 What to Analyze? ........................................................................................... 5

3.1 What Does One Mean By “Localized?” ............................................................................5

3.2 Is the Study Area Large Enough to be Meaningful?........................................................5

3.3 What Elements Need to be Analyzed?...............................................................................6

4.0 What Level of Analysis is Warranted?......................................................... 6

4.1 Project Guidelines ...............................................................................................................7

4.2 Small Corrections and Operational Changes...................................................................7

4.3 Large and Infrastructural Changes...................................................................................8

4.4	 Public Support and Justification........................................................................................8

5.0 Levels of Analysis........................................................................................... 9

5.1 Categories of Analysis Tools...............................................................................................9

5.2 A Word about Microsimulation Tools..............................................................................11

5.3 Case Study in Tool Selection: Sacramento SR 65/I‐80 

Interchange Analysis.........................................................................................................12

Appendix A. Tool Selection Worksheet.............................................................. 14
- ii -

List of Tables

A.1 Freeway Characteristics of Different Tool Types under Multiple Criteria..................15

A.2 Arterial Characteristics of Different Tool Types under Multiple Criteria...................17

A.3 Roundabout Characteristics of Different Tool Types under Multiple Criteria...........19

A.4 Signalized Intersection Characteristics of Different Tool Types under 

Multiple Criteria................................................................................................................21
- iii -

List of Figures

1. Relative Tool Complexity..................................................................................................12
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1.0 Introduction

A significant portion of the nation’s traffic congestion problems can be attributed to recurring 

congestion at specific locations on roadways – conditions that cause bottlenecks. There is now 

broad consensus that poorly functioning subordinate sections of a facility – rather than the entire 

facility being “undersized” – is more often than not the predominant problem. In layman’s terms, 

often only the “misfiring” parts of the facility need attention, rather than the much more costly 

solution of starting over by replacing it or widening it. 

A variety of mitigation techniques can reduce the frequency and impact of localized bottlenecks 

(further defined in Section 2.0). These techniques can range from the most intense (e.g., 

microsimulation product applications) to the least intense (e.g., sketch planning) with several 

iterations of tools in between; e.g., simple data summarizations, the Highway Capacity Manual 

analysis (HCM), empirical analysis, and deterministic tools. 

The crux of this document is to help agencies decide which analysis techniques to apply and 

how. But how should an agency choose which bottleneck analysis tool to apply? Which tools are 

correctly aligned with which challenges? How should an agency decide when to use a particular 

bottleneck analysis tool, and when is microsimulation warranted?

This bottleneck analysis tool selection guide was developed to help transportation agency managers 

decide which analysis is appropriate; and how to apply it cost-effectively. Thoroughly considering 

these decision-support tools can assure the most efficient use of agency resources to provide the 

highest level of service (LOS). This guide consists of the following sections:

•	 Section 1.0 – Introduction. How are bottlenecks caused? What should be the role of 

bottleneck analysis in decision-making? What bottleneck mitigation strategies are 

available?

•	 Section 2.0 – Background and Discussion. This section prompts the reader to define the 

study area through some main criteria.

•	 Section 3.0 – What to Analyze? The next step is to decide what level of analysis is 

appropriate. The availability of resources guides the formulation of alternatives.

•	 Section 4.0 – What Level of Analysis is Warranted? This section guides the selection of 

the most appropriate class of modeling tool.

•	 Section 5.0 – Levels of Analysis. What are the various options available to analyze 

congestion, and specifically, localized congestion?

•	 Appendix A – Worksheet. This reference tool guides transportation professionals to 

identify the appropriate category of modeling tool.

Applying this document and the worksheet will create additional analytical consistency and 

uniformity across state departments of transportation (DOT), Federal, regional, and local 

transportation agencies.
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2.0 Background and Discussion

The term “bottlenecks,” in the context of this guidance, is hereafter confined to the genre of 

“recurring” traffic bottlenecks, as opposed to “nonrecurring” ones. Recurring bottlenecks are 

predictable in cause, location, time of day, and approximate duration; e.g., the ones that we 

encounter in our everyday commutes. Nonrecurring bottlenecks are random (in the colloquial 

sense) as to location and severity. Examples include crashes, weather events, and even “planned” 

events, such as work zones and special events, all of which are irregular in occurrence and location. 

Let’s dispense with nonrecurring bottlenecks for a moment. Nonrecurring bottlenecks are more 

prone to empirical study; i.e., based on or characterized by observation and experiment instead 

of theory. Said bottlenecks trigger traffic control plans (TCP) that are either premeditated or 

reactionary to the event. Tweaking the plan can improve it either in real-time or “for the next 

time” it is needed. “Dynamic Lane Merges” (DLM) are increasingly being tested, empirically, to 

increase the safety and efficiency of nonrecurring bottlenecks. DLMs essentially are active traffic 

management plans that “kick in” when excessive queues are detected, say in work zones. Messages 

are enacted that display proactive information on how and when to merge. The messages shut 

off when the queues begin to dissipate. Recurring bottlenecks, however, have historically been 

studied by the academic community using non-empirical means, like microsimulation.

A “localized” recurring bottleneck may be considered to be a defined event (i.e., cause) in a defined 

location; e.g., a lane drop, a weave, an intersection, or an off- or on-ramp. For example, repeat 

congestion at one movement of an interchange over a couple of hours each day would be “local,” 

whereas a “mega” bottleneck or systemic congestion would be considered to be an undersized 

interchange, and is not the focus of this guidance. 

It hardly needs to be said that in the “mega” case, micro simulation is always warranted due to the 

complexity of the facility or facilities. However, it is recognized herewith that lesser problems 

typically require comparatively lesser study and solutions. The key is finding the cutoff point at 

which project execution meets project need, decision-justification, and budget, both in terms of 

project analysis and project implementation. For example, the insufficiency of a left turn phase at 

a ramp terminal (causing queuing back down the ramp to the mainline) would not warrant a fullblown study of the mainline, as much as it would warrant an adjustment of the signal timing. 

2.1 Common Causes of Bottlenecks

Recurring, localized bottlenecks occur any time the rate of approaching traffic is greater than the 

rate of departing traffic. The causal effect can usually be attributed to the existence of at least one 

of two factors:

•	 Decision points, such as on and off-ramps, merge areas, weave areas, lane drops, tollbooth 

areas, and traffic signals; or

•	 Physical constraints, such as curves, underpasses, narrow structures, or absence of 

shoulders.
- 3 -

Recurring bottlenecks usually disperse from the rear of the queue, as the volume crush dissipates 

and the confluence regains its ability to process the traffic more or less under free flow conditions. 

Nonrecurring bottlenecks, as a point of differentiation, can disperse from the front or rear, depending 

on whether the cause is incident-related (e.g., crash or work zone) or volume-related (e.g., special 

event crush load), respectively. 

One can even imagine a compounded situation, where a decision point (off-ramp) is preceded 

by a physical constraint (sharp curve). This type of bottleneck congestion is more complex to 

mitigate because both the decision point and physical constraint must be addressed to deal with 

the bottleneck. Further, it is difficult to predict the largest contributor if there are multiple causes.

Each bottleneck cause has its own mitigation strategies. To select the appropriate strategy, planners 

must understand the bottleneck’s causes before attempting to prescribe solutions, which will be 

discussed in Section 3.0.

2.2 What Role does Analysis Play?

As transportation agencies continue to seek innovative, cost-efficient solutions to reduce and 

eliminate bottlenecks, analysis of alternatives has become a necessary decision-support process.

By definition, the planner models the study area and measures the performance of several preselected 

criteria. If no improvements were made (“no-build” scenario), how would the corridor operate 

in the future? Conversely, what effects would be incurred if the alternatives were implemented? 

Alternatives analysis can be developed to compare operational forecasts under different scenarios.

Because analysis is useful for so many stages of the decision-making process, a variety of methods 

exist. It is important to note that the methods vary greatly; no one tool can model all scenarios or 

proposed improvements. Thus, selecting the appropriate tool based on the goals and objectives of 

the project is critical, and is the focus of this guidance.

2.3 Bottleneck Mitigation

In some cases, the most cost-effective ways to relieve bottlenecks are through the simplest geometric 

or operational improvements. Many of these solutions can be executed as safety projects, Federalaid non-exempt projects, or even maintenance activities. When applied properly, these strategies 

can produce very high benefit-cost ratios because of the smaller footprint solution, the lower-cost 

design solution, and the lower life-cycle cost, including planning, design, construction, operations, 

and maintenance. Some of the more common low-cost mitigation strategies include the following:

•	 Signal retiming. Many congested corridors can achieve bottleneck reductions by simply 

optimizing the timing of traffic signals or their timing offsets between intersections.

•	 Restriping. Remarking traffic lanes to add auxiliary lanes or acceleration/deceleration 

lanes can increase capacity or redirect volume more efficiently. Some of the common 

restriping techniques include preventing weaves or sharp turns that cause slowdowns; 

restriping lanes to provide more, although slightly narrower lanes; or converting short 
- 4 -

sections of shoulders into travel lanes.

•	 Signage and Signals. Signs and signals can be designed to purposely restrict specific 

movements to the benefit of others (i.e., STOP sign, YIELD sign at the minor approaches) or 

prohibit inefficient movements (i.e., restrict U‐Turns or left turns crossing heavy opposing 

traffic). On the flipside, these strategies also can be used to prioritize heavy movements to 

prevent bottlenecks from forming (i.e., right turn on red, and providing exclusive left-turn 

signals).

•	 Installing Loop Detectors. Installing loop detectors ahead of traffic lights can help reduce 

queuing by dynamically prioritizing the busiest approaches on-demand.

•	 Ramp or Driveway Removal (or Modification). Closing, relocating, metering, or 

combining ramps, especially low-volume ones, can unclog some traffic streams. In the 

case of ramp modifications, temporary closures can test a hypothesis. The ramp could 

always reopen if the cure is worse than the symptom. 

In other cases, more costly solutions might be necessary, including rebuilding or redesigning 

the area in the vicinity of the bottleneck location. These strategies also can produce significant 

benefit-cost ratios, but the cost will always be higher than the strategies listed previously. These 

strategies may have high life-cycle financial costs (planning, design, construction, operations, and 

maintenance) or social costs (such as forcing drivers to relearn lane directions or turns). Regardless 

of the nature of the cost, these improvements must be planned well before their implementation, 

because they are costly and difficult to undo. The following project examples include:

•	 Washington State DOT Integrated Operations/Construction Programs in Puget 

Sound Region and Seattle. A new exit ramp was constructed along I‐405/SR 67 to 

minimize weaving.

•	 Post Street Restriping Project. In San Francisco, California, Post Street between Kearny 

Street and Montgomery Street was a two-way street that was converted to a one-way street 

to increase its capacity during the p.m. peak-period.

There are many successful case studies of transportation agencies implementing low-cost, highcost, or a combination of solutions to relieve recurring, localized congestion. The Federal Highway 

Administration’s (FHWA) web site http://www.ops.fhwa.dot.gov/bn/index.htm has many other 

brief examples of localized bottleneck solutions.
- 5 -

3.0 What to Analyze?

This may seem an unnecessary question; i.e., the obvious answer would seem to be “the problem!” 

However, the purpose of this section is to remind agencies that secondary and tertiary impacts may 

result.

The transportation industry has dozens of infrastructural and operational strategies for mitigating 

congestion, including priced-tolling, high-occupancy lanes, telecommuting, public transit, and 

driver incentives like car-sharing and parking strategies, to name but just a few. At the highest 

levels, models and simulations offer the chance to test out congestion mitigation strategies without 

expensive construction or pilot projects. Using the appropriate simulation tools, planners can 

estimate the future conditions of a specific site with and without the mitigation strategies.

But a microsimulation study may be “overkill”; or not. For any analysis to be effective, it must 

consider the entire area affected by the bottleneck. For example, changing the signal timing at a 

frequently congested intersection may eliminate bottlenecks at the site, but if this improvement 

causes impacts to the neighboring intersections, this may not be a wise strategy. A large enough 

area must be considered to ensure that the analysis can account for all of the contributing and 

resulting factors. The questions below can help gauge the geographic/spatial extent of the analysis.

3.1 What Does One Mean By “Localized?”

For a bottleneck to be localized (per the definitions outlined in section 2.1) the factors causing that 

bottleneck ideally should not influence upon, or be influenced by, any other part of the transportation 

system; however, in a practical sense, the planner should consider any impact to the closest up- or 

downstream entity. If your bottleneck is deemed to be “the entire corridor” or something greater, 

then it is not intended to be covered by this guidance.

For a much more detailed discourse on this subject, please refer to FHWA’s web site on this subject 

at http://www.ops.fhwa.dot.gov/bn/index.htm and download or request the document “Recurring 

Traffic Bottlenecks: A Primer.” FHWA publication number FHWA-HOP-09-037.

3.2 Is the Study Area Large Enough to be Meaningful?

One must fully consider the size of the study area in determining the scope of analysis that is 

necessary. 

For example, consider an apparently isolated congested intersection. Suppose that modeling it 

as a roundabout, or simulating impacts of signal timing adjustments, or restriping, eliminates or 

greatly reduces the occurrence of bottlenecks. The planner knows this because he or she observes 

the simulated traffic and no longer sees bottlenecks congesting the study area. The planner then 

prepares to recommend a mitigation technique, but is unable to quantify the effects of these 

strategies. The travel time for a car through the roundabout, even under the worst conditions, is 

one minute or less. How can the planner make a meaningful case for one strategy over another 

if the results of each are only negligibly different? In this instance, localized Highway Capacity 

Manual (HCM) analysis may be more cost effective than a microsimulation product. 
- 6 -

However, suppose that in resolving the bottleneck at this intersection it threatens to cause new 

bottlenecks to form downstream. If the original analysis is localized, its impacts would only be 

observed at the simulated intersection. If the study area is expanded, the opportunity to improve 

the entire system would be plausible (discussed below), and the measurable differences between 

compared strategies is likely to be starker. This is where simulation analysis is likely to make a 

meaningful contribution.

3.3 What Elements Need to be Analyzed?

Understanding the breadth of the analysis is critical to assessing what level of analysis is justified.

• What are the impacted limits of the study?

• Are upstream and downstream facilities impacted?

• What alternatives can be considered?

• How many hours of congestion are present? What is the optimal outcome?

• How will the public be impacted?

– Will they accept the temporary inconvenience of work zone?

– Will they accept the changes in routine; i.e., new routes?

– Are businesses on board?

• What degree of precision do the decision-makers require?

4.0 What Level of Analysis is Warranted?

An agency with unlimited resources could possibly study – and unnecessarily overanalyze – some 

proposed physical and operational improvements. Studies require time and cost. There have 

been many projects wherein low-cost congestion solutions were based on qualified engineering 

studies (perhaps only sketch planning was necessary) and were executed in the course of time 

savings and/or cost savings, without execution of a complicated simulated analysis. Conversely, 

modeling invariably provides a more detailed analysis. Agencies should consider the pros and 

cons of substituting sketch planning-level studies against a potentially marginal benefit of higherlevel analysis. In short, the cost differential may be one factor but should not be the factor.

A clear understanding of both the study area and resources available for the project should fairly 

guide the decision of how much analysis is necessary. 

There are two sides to this coin.

• The level of analysis should roughly correlate to the size of the problem. However... 

• ... by their very nature, these are low-cost, “low hanging fruit” problems that nevertheless 

have potentially huge benefits in terms of reducing hours of delay. Even the simplest 

change may incur a significant operational change elsewhere on a facility. 
- 7 -

An agency should not scrimp on the resources necessary to make a knowledgeable decision; 

meaning that if the complexity of the project, or the level of public discourse is inordinately high, 

the agency should prepare for a thorough analysis, and possibly a very involved public presentation 

and discussion, especially in locations near private entrances and land owners.

Some mitigation strategies do indeed lend themselves towards “obvious” solutions or even real 

world experimentation. Closing a low-volume driveway or ramp can always be reversed if 

necessary. Tweaking signal timing may indeed be cheaper than building a model and adjusting the 

simulated signal timing. On the other hand, structural changes to a freeway facility are too costly 

to rebuild or reverse; experimentation in these cases is not a wise option.

Another important consideration that must be made is the availability of good data. Often, no or 

little data are available, making a significant portion of the analysis cost devoted to collecting data. 

An agency also must weigh the cost and necessity of data collection as part of the overall cost, 

against the fidelity of the analysis results. The agency must include this consideration; is there 

sufficient data available for the level of study that this project warrants?

4.1 Project Guidelines

Every congestion mitigation strategy comes at a cost. This cost includes the hard costs of analysis, 

design, materials, and labor; and the soft costs of user impact, public opinion, and life-cycle costs. 

Building an overpass is an expensive operation. But mitigation strategies also carry sensory and 

learning costs too. Drivers who frequently traverse a specific corridor will have an adjustment 

period as they relearn their familiar route with the new overpass. All of these considerations must 

be weighed when considering to model. Ultimately, the agency’s available resources, both in 

terms of preproject (e.g., analysis, public opinion, opportunity) and postproject (e.g., project cost, 

public acceptance, project life-cycle costs, interconnectivity, etc.) will decide how much project 

analysis is appropriate.

4.2 Small Corrections and Operational Changes

Many mitigation strategies have implementation and learning costs low enough to justify qualified 

engineering judgment in the real world. These include the following:

•	 Adjusting the timing of existing signals. The timings can be tweaked again, or reset to 

original settings.

•	 Placing new signs or signals. Signs can be tweaked either in message or relocation; 

removing or rebuilding signals is a significantly harder task (See “Note” below).

•	 Some aspects of lane restriping. Safety, above all else, should be addressed; but in 

essence, the striping could be tweaked. 

•	 The installation of loop detectors. The detectors will not inherently disrupt traffic; only 

the application of their data will. 
- 8 -

In cases where the bottlenecks are absolutely isolated from upstream and downstream influences, 

and the study area is small, it may be sufficient for the agency to commit only enough resources 

and decision necessary to implement the strategies listed above. 

Note: In the context of the message above, bear in mind that signal optimization software exists 

that can model intersection operation at less expense than a full simulation analysis.

4.3 Large and Infrastructural Changes

Most projects have implications that are too high to risk without considering even the least amount 

of analysis, if only to concur, justify, or present findings in a manner that warrants a responsible 

decision. Most agencies would agree that simulation is a necessary step in larger project execution, 

as in these examples:

1. The construction of new facilities, such as auxiliary or mainline lanes and overpasses; 

2. Complex movements, such as weaves, or the introduction of new movements; and

3. Any changes in required driver behavior, such as converting two-way roads to one-way 

roads, or other major redirections of flow, may be considered as “non-traditional” solutions 

requiring other levels of outreach and marketing with local officials and the motoring 

public.

As has been said before, these changes are expensive to implement, and would be prohibitively 

more expensive to undo or change. Agencies save money by spending resources up front to analyze 

simulations, and only implementing these major changes once.

4.4	 Public Support and Justification

Of course, not every case is so clear. Planners and approving boards and councils are stewards 

of the public trust and budget. They must consider which strategies are appropriate for the area, 

and estimate the potential cost and impact of each strategy. A good rule of thumb is that the level 

of analysis should correlate to the perceived level of total mitigation cost. A computer-aided 

rendering of a before-and-after proposal may be its own justification to use micro simulation to 

present a proposed project to the public.
- 9 -

5.0 Levels of Analysis

The planner must select the most appropriate type of analysis tool. This section introduces the 

variety of analysis tools, and discusses the circumstances when one might be preferable over 

another. This section also introduces a set of project characteristics to consider.

5.1 Categories of Analysis Tools

There are numerous types of tools to fit projects of different sizes, scopes, and objectives. 

Depending on the project, there might be more than one suitable tool, or the project might require 

more than one tool (from more than one category) simultaneously. These tools can typically be 

characterized as presented below.

Sketch Planning Tools

These tools produce order-of-magnitude estimates of travel demand, operations, and delay. They 

are sometimes used to prepare preliminary budget estimates or similar. They can be as simple as 

look-up tables or basic design criteria found in design or planning tenet manuals. They are limited 

in scope, analytical robustness, and presentation capabilities.

Empirical Observations

Collecting even the simplest field data or observing particular driver habits can go a long way 

towards assessing a particular problem. Maybe the observation of when or why drivers slow, 

yield, merge, or otherwise react to a bottleneck can help to propose a plan of action. Perhaps the 

observation that some motorists are bypassing a bottleneck via an adjacent collector-distributor 

road or local network can lead to a conclusion. Keep in mind that the more detailed the data 

collected or available, the greater the opportunity to employ a more thorough analysis later on. 

Equation Tools

Equation tools contain an analytical procedure that is static and closed-form. In such cases, the 

analyst will enter several inputs into the model, and the tool will produce singular outputs that 

provide information on the expected operational conditions on that facility; i.e., specific questions 

“in” will render specific answers “out,” if you will. Data outputs from such tools can include 

the facilities’ LOS (delay, speed, density). Such tools are simplistic; the outputs of the tool are 

typically not fed back into the model as new inputs, but rather, new equations are run. The same 

inputs will always yield the same outputs; random variations are not accounted for. Examples of 

equation tools include the Highway Capacity Software (HCS) and SIDRA (software for evaluating 

and designing roundabouts).

Equation tools are very appropriate for localized study areas like a single intersection or a highway 

section. Equation tools also are appropriate for a quick-and-dirty preliminary analysis that may 

lead to or warrant a future, more detailed analysis.
- 10 -

Deterministic Tools

Deterministic tools vary from equation tools in that deterministic tools can go beyond providing 

information of the traffic conditions present on the facility – they can help analyze operational and 

signal timing components. Deterministic tools also are closed-form and non-iterative; they do not 

necessarily use logical, advancing, or repeating investigation that iteratively builds upon a prior 

result, until a complete computer “run” is delivered. But in a deterministic system, every action 

or cause produces a predictable reaction or effect, and every reaction, in turn, becomes the cause 

of subsequent reactions. Given that the HCM has more than 30 chapters covering everything 

from driveways to highways, there are several qualifying sections that are “deterministic” in their 

application.

Traffic signal optimization tools also vary in complexity and are deterministic tools. Many of these 

tools have the ability to optimize signal phasing and timing plans for isolated signal intersections, 

arterial streets, or street networks. This may include capacity calculations, cycle length, or splits 

optimization, including left turns, as well as coordination/offset plans. Some deterministic tools 

also can optimize the ramp metering rates for freeway ramp control. The most advanced traffic 

optimization tools are capable of modeling actuated and semiactuated traffic signals, with or 

without signal coordination. Examples of such tools include Synchro and TRANSYT.

Deterministic tools are appropriate for a corridor, a series of intersections, or grid urban network.

Stochastic Tools

Stochastic modeling is the counterpart to deterministic modeling and introduces randomness. 

There is some indeterminacy in the future evolution of the analysis, as described by probability 

distributions. The product can generate either totally random outcomes, or, as is typically the benefit 

of the product, can predict more-probable ones. These tools can evaluate the 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 over 

time. Simulation models, by evaluating entire systems of facilities, can pinpoint the interference 

that occurs when congestion builds up at one location before it impacts other locations. Also, 

traffic simulators can model the variability in driver/vehicle characteristics.

Stochastic tools are most appropriate for analyzing complex systems; advanced operational 

strategies; mitigation techniques (i.e., adjustments of ramp metering parameters); or larger study 

areas (typically not more than 100 square miles).

There are three different subcategories of simulation models, as discussed below.

Macroscopic Models

Macroscopic models take place on a section-by-section basis rather than tracking individual vehicles, 

and therefore 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 other 

stochastic models. They do not, however, have the ability to analyze transportation improvements 
- 11 -

in as much detail as other stochastic models; and do not consider trip generation, trip distribution, 

and mode choice in their evaluation of changes in transportation systems.

Microscopic Models

Microscopic models simulate the movement of individual vehicles, based on theories of carfollowing and lane-changing. Microscopic models also do an increasingly good job of simulating 

the geometrics of the facility. Typically, vehicles enter a transportation network using a statistical 

distribution of arrivals (a stochastic process); and are tracked through the network over small 

time intervals (e.g., one second or fraction of a second). 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 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 can be completed.

Mesoscopic Models

Mesoscopic models utilize data that is more general than microscopic models, but represent 

traffic components in higher detail than macroscopic models. The mesoscopic models’ unit of 

traffic flow could be the individual vehicle or groups of vehicles. These models can handle large 

network grids of nodes and links, and can provide for diversionary routes and volume adjustments. 

Mesoscopic tools can assign vehicle types and driver behavior, or base their behavior on the roadway 

characteristics.1

 Their movement, however, is governed by the aggregate characteristics of the 

link or traffic group or cells.2 Mesoscopic model travel predictions take place at an aggregated 

level, and do not consider dynamic speed/volume relationships for individual system components 

(vehicles in most cases).

5.2 A Word about Microsimulation Tools

For all their prowess in number crunching, simulation tools have some caveats. First and foremost, 

microsimulation analysis is a specialty field and not a standard staff duty. These tools often require 

a plethora of data, considerable error checking, and the potential for manipulation by one or more 

of the basic data inputs. Calibration can be complex and time-consuming. Secondly, using them 

is not a “magic bullet” to be blindly accepted. The algorithms are often vendor-copyrighted and 

may not have universal acceptance by the professional community. There is no national consensus 

on the design of a simulation-tool approach. Simulation models assume “100 percent safe driving” 

and often assume the most direct route selection regardless of human behavior patterns or reaction. 

This is not an indictment of simulation tools – merely a caution towards the old adage “data in 

equals data out” and the fact that special training is required for each differing model that exists.

1 Jayakrishnan, R., H. S. Mahmassani, et al., 1994, An Evaluation Tool for Advanced Traffic Information and Management Systems in Urban Networks, Transportation Research C.

2 Ben-Akiva, M., 1996, Development of a Deployable Real-Time Dynamic Traffic Assignment System, Task D 

Interim Report: Analytical Developments for DTA System, ITS Program, Cambridge, Massachusetts, MIT ITS 

Program.
- 12 -

Microsimulation analysis might be entirely warranted when the complexity of the bottleneck has 

significant, and not merely incidental, impacts on weaving or upstream and downstream traffic. 

Other applications might be when the rate of a ramp meter discharge is impacting, or when route 

changes are impacted. 

Figure 1. Relative Tool Complexity

5.3 Case Study in Tool Selection: Sacramento SR 65/I‐80 

Interchange Analysis

Overview

Recurrent, localized bottlenecks occur at the SR 65/I‐80 interchange near Sacramento, California 

because of high traffic volumes and the inefficient geometry of the eastbound to northbound loop 

connector. Bottlenecks occur in the southbound SR 65 to westbound I‐80 in the a.m. and p.m. 

periods and in the eastbound I‐80 to northbound SR 65 in the p.m. The interchange is a Type F‐6 

freeway-to-freeway interchange. The bottlenecks and their effects are confined by Post Mile (PM) 

2.6 and 7.1 on I‐80 and PM 4.9 and 6.2 on SR 65.

Existing and forecast peak-period volume data is available for both mainlines and connectors.

The following four alternative solutions are being considered:

•	 Alternative 1 – Add a high-occupancy vehicle (HOV) flyover connector in both directions 

along the troublesome quadrant. Add an additional lane in each direction from the 

intersection for three overcrossings in either direction.
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•	 Alternative 2 – Replace the eastbound I‐80 to northbound SR 65 loop connector with a 

new flyover connector. HOV connectors would not be constructed. Add an additional lane 

in each direction from the intersection for three overcrossings in either direction.

•	 Alternative 3 – Add both flyover connectors proposed in Alternatives 1 and 2, as well as 

the additional lanes in each direction.

•	 Alternative 4 – The No-Build alternative.

Approach

Let us consider the case using the following seven criteria:

1. Area of Influence – The congestion is caused by geometric restrictions at a single 

interchange, but its effects are felt for as long as 4.5 miles upstream on I‐80.

2. Facility – The facility under consideration is a single freeway interchange.

3. Availability of Data – Existing and forecast peak-period volumes are available.

4. Mitigation Strategies – The mitigation strategies under consideration include HOV lanes, 

geometric improvements, and new facilities.

5. Scope – The project overview does not state a project scope, but if capital improvements 

are under consideration, it can be assumed that this project is scoped for the long term.

6. Performance Measures – To analyze this situation, one would want to include interchangewide vehicle throughput and person-mobility (number of persons served through this 

interchange) to compare the different design alternatives. The overall vehicle-miles of 

travel (VMT) and vehicle-hours of travel (VHT) can be calculated to gauge mobility and 

delay. Finally, speeds from the specific connector movements also may be useful. Due 

to the constrained study area limits, calculating travel time and delay would not be as 

meaningful or representative.

7. Cost Effectiveness – The project overview does not state restrictions on ease of use, so it 

can be assumed that this is not a prohibitive factor.

Analysis Results

The bottlenecks are localized and are caused by both driver behavior and design factors. The 

addition of the new flyover ramps (new structures) prompted the use of simulation, since other 

tool types may be more suitable to model changes in the current structure (i.e., lane widening, lane 

addition, grade reduction), instead of new links.

More specifically, microscopic simulation analysis would best fit this project’s analytical needs. 

In this case, the project analyst selected Paramics traffic microsimulation software to model the 

interchange.
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Modeling each alternative in Paramics, the analyst saw that Alternative 3 (both the HOV connector 

and eastbound to northbound flyover) would serve the most vehicles and people by year 2040. 

According to the microsimulation analysis, this alternative would serve about 83,000 to 84,000 

vehicles during the a.m. and p.m. peak-periods, respectively. This alternative also had the least 

number of miles traveled relative to the number of vehicles served; between 96,000 to 100,000 

vehicle-miles for about 83,000 to 84,000 vehicles per peak-period. The removal of the loop 

connector would shorten the distances traveled by the eastbound to northbound commuters, thus 

bringing some fuel consumption and air quality benefits.

Appendix A. Tool Selection Worksheet

Freeway

Depending on the needs of the project, modeling a freeway might require having field data on 

car-following and lane-changing behavior, but in most cases, default values from the tools should 

suffice. Some projects might require intense network coding, depending on the study area size 

and complexity. Table A.1 summarizes the characteristics of different tool types under multiple 

criteria.

Arterial

Depending on the needs of the project, modeling an arterial may need to include transit operations. 

Table A.2 summarizes the characteristics of different tool types under multiple criteria.

Roundabout

Depending on the needs of the project, modeling a roundabout might require modeling conflicting 

volumes. Some projects might include interaction intersections, isolated intersections, or both. 

Some projects demand comparing geometric configurations. Browse below (Table A.3) to find the 

category of tool that best fits your specific project.

Signalized Intersections

Depending on the needs of the project, modeling a signalized intersection might require 

pedestrian behavior. Some projects might transit signal priority, while others might not. 

Browse below (Table A.4) to find the category of tool that best fits your specific project.
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Table A.1 Freeway Characteristics of Different Tool Types under Multiple Criteria

Characteristic Equation Deterministic Macroscopic Microscopic MesoscopicLevel of detail Only for analyzing broad 

criteria based on theoretical capacity constraints; only geometric component is number of lanes and grade.

Only for analyzing broad criteria based on geometric capacity constraints; geometry used as physical capacity limits.

Regional travel demand patterns.

Vehicle Interactions, detailed geometry and operational elements (i.e., ramp meters, HOT lanes) may be modeled.

Limited Vehicle Interactions, detailed geometry and operational elements modeled.

Calibration effort None. Minor – Not many driver or 

roadway characteristics to change.

Medium – No calibration needed unless analyst must use innovative techniques to mimic some nonsimulatable strategies.

Significant – Localized bottlenecks can be represented in great detail, so traffic counts, travel times, and bottleneck extents need to be calibrated.

Significant – Localized bottlenecks can be represented as aggregate delay functions that represent slow down; average speed of vehicle groups/link performance must be calibrated.

Methodology Static equations. Capacity-based standard 

equations.

Speed/density relationships and localized volume/ratios are utilized.

Vehicle-to-vehicle interactions and interactions with geometry modeled.

Vehicle interactions usually modeled based on average speed density relationships of vehicle groups or links.

Recommended Application

Capacity determination. Preliminary feasibility 

studies.

Regional TDM analysis. Detailed and accurate 

representation of bottlenecks; reconstruction/staging, alternative analysis, diversion analysis.

Series of localized bottlenecks and possible diversion.

Dynamic traffic assignment

None. None. None. Available. Available.

Ease of use High. High. Medium. Low. Low.

Graphical representation

Basic diagrams. Moving vehicles with 

geometric constraints.

Link volumes only. Detailed geometry, vehicle 

movements, and dynamic performance measures.

Detailed geometry, limited vehicle details, and aggregated dynamic performance measures.

Input data requirements

Easy to find inputs. Counts, overlays (design files 

or aerial photos).

Counts, geographic information layers.

Counts, aerials/design files, travel times, and bottleneck details.

Counts, aerials/design files, travel times, and bottleneck details.

Weaving and merging

Theoretical capacity estimates.

Physical/geometric capacity constrained.

Volume/capacity representation only.

Represented through vehicle interactions and geometry modeling.

Represented through aggregated speed density relationships.
- 16 -

Characteristic Equation Deterministic Macroscopic Microscopic MesoscopicSight distance requirements

Theoretical estimates independent of freeway characteristic, demand, or design.

None. None. None. None.

Performance measures

LOS, capacity, estimated delay.

Volumes, speeds, LOS, capacity, estimated delay.

Volumes, LOS. Volumes, LOS, delay, and 

speeds.

Volumes, LOS, delay, and speeds.
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Table A.2 Arterial Characteristics of Different Tool Types under Multiple Criteria

Characteristic Equations Deterministic Macroscopic Microscopic Mesoscopic

Level of detail Only for analyzing broad 

LOS criteria based on theoretical capacity constraints. Only geometric component usable is number of lanes and grade.

Only for analyzing broad LOS criteria and delay estimates based on operational systems, such as traffic signals, stop signs, and lanes.

Low level of detail due to large coverage areas.

Detailed. Only for analyzing broad 

LOS criteria based on theoretical capacity constraints. Only geometric component usable is number of lanes and grade.

Calibration effort None. Volume-based calibration. Volume-based for large study 

areas, but cannot be used for specific location calibration.

Significant – Localized bottlenecks can be represented in great detail, so traffic counts, travel times, and bottleneck extents need to be calibrated.

Significant – Localized bottlenecks can be represented as aggregate delay functions that represent slow down; average speed of vehicle groups/link performance must be calibrated.

On-street parking As standard capacity 

reduction.

As standard capacity reduction.

As standard capacity reduction.

As standard capacity reduction.

As standard capacity reduction.

Vehicle interaction with pedestrians

None. None. As standard capacity 

reduction.

As standard capacity reduction.

As standard capacity reduction.

Road markings Number of lanes. Number of lanes with rough 

geometry.

No markings – Only capacity constrained by number of lanes.

Number of lanes. Number of lanes.

Transit None. Only operational 

characteristics such as signal preemption.

Some tools have the capabilities.

Some tools have the capabilities.

Some tools have the capabilities.

Lane restrictions As capacity constraints. Yes. Yes. Yes. Yes.

Traffic signal operations

Only standard delay. Detailed timing plans can be 

modeled.

Detailed timing plans can be modeled.

Detailed timing plans can be modeled.

Detailed timing plans can be modeled.

Traffic density Capacity-based only. Based on broad volume 

capacity relationships.

Can be shown over time. Can be shown over time. Can be shown over time.

Individual travel time

None. Low utility of individual 

travel times.

Tracked on a segment-bysegment basis. Tracked individually. Tracked individually.

Delay Capacity-based estimates. Based on operational 

elements like signals, speed limits, etc.

Based on operational elements like signals, speed limits, etc.

Based on operational elements like signals, speed limits, etc.

Based on operational elements like signals, speed limits, etc.
- 18 -

Characteristic Equations Deterministic Macroscopic Microscopic Mesoscopic

Graphical representation

Diagrammatic representation. Fairly detailed geometric 

representation.

Detailed. Detailed; animations 

available.

Detailed; animations available.

Input data requirements

Counts, configuration. Counts, signal timings, 

traffic, restrictions.

Counts, signal timings, traffic, restrictions.

Counts, signal timings, transit schedules, traffic, restrictions.

Counts, signal timings, transit schedules, traffic, restrictions.

Vehicle categories Represented as Passenger 

Car Equivalents (PCEs).

Represented as PCEs. Classification based on 

vehicle type, although vehicle dynamics cannot be modeled.

Classification based on vehicle type.

Classification based on vehicle type.

Intersection rightof-way None. None. Can be modeled. Can be modeled. Can be modeled.
- 19 -

Table A.3 Roundabout Characteristics of Different Tool Types under Multiple Criteria

Characteristics Equations Deterministic Macroscopic Microscopic Mesoscopic

Level of detail Only for analyzing broad 

LOS criteria based on theoretical capacity constraints. Not always sensitive to all geometric constraints.

Only for analyzing broad LOS criteria based on theoretical capacity constraints. Geometric components typically used include number of lanes, dimensions of the access points and the circulatory roadways, and grade.

No known tools with roundabout capabilities.

Vehicle operations and geometry modeled.

No known tools with roundabout capabilities.

Range Single location. Single location. N/A Multiple locations; in series 

or separate.

N/A

Methodology Gap acceptance models* Utilizes linear or exponential 

empirical regression models based on circulating and entry flows, geometric characteristics, and sometimes driver behavior.**

N/A Vehicle-to-vehicle 

interactions and interactions with geometry modeled.

N/A

Performance measures

Capacity, delay, and queuing estimation.

Capacity, delay, and queuing estimation.

N/A Travel time, VHT/VMT, and 

animations.

N/A

Conflicting volumes

Calculates conflicting flow rates or circulating flow rates as a function of turning movement volumes.

Calculates conflicting flow rates or circulating flow rates as a function of turning movement volumes.

N/A Calculates conflicting flow 

rates or circulating flow rates as a function of turning movement volumes.

N/A

Ease of use Easy. Easy. N/A Complex, labor-intensive. N/A

Lane characteristics

Single lane only. Number of lanes and lane 

widths are inputs.

N/A Details can be coded into 

network.

N/A

Number of approach legs

Not sensitive to geometric parameters.

Serve as inputs; more emphasis on entry flows than number of entry/access points.

N/A No limits; details can be 

coded into network.

N/A

Angle of approach legs

Not sensitive to geometric parameters

Sensitive to entry angle and radius – serve as inputs to calculation 

N/A No restrictions, details can be 

coded into network

N/A
- 20 -

Characteristics Equations Deterministic Macroscopic Microscopic Mesoscopic

Study area size Only analyzes performance 

of individual approaches to single roundabout; no multiple roundabout interactions

Certain software can analyze multiple roundabout interactions or roundabout interactions with other intersections of various control types. 

N/A Can analyze multiple 

roundabout interactions or roundabout interactions with other intersections of various control types. 

N/A

*”Roundabouts in the United States,” NCHRP Report 572, Transportation Research Board, National Academies, 2007.**Appendixes to NCHRP Report 572: Roundabouts in the United States, Transportation Research Board, National Academies, 2007.
- 21 -

Table A.4 Signalized Intersection Characteristics of Different Tool Types under Multiple Criteria

Characteristics Equations Deterministic Macroscopic Microscopic Mesoscopic

Level of detail Only for analyzing 

LOS based on capacity constraints. Only needs number of lanes and grade.

Only for analyzing LOS based on capacity constraints. Only needs number of lanes and grade.

Uses geometric and volume information as inputs; common outputs are LOS, delay, and queue length.

Needs volume, trip distribution, and geometrics as inputs; common outputs are travel time, VHT/VMT, and delay.

Needs volume, trip distribution, and geometrics as inputs; common outputs are travel time, VHT/VMT, and delay.

Signal type Pretimed. Pretimed and actuated. Pretimed, actuated, and 

coordinated.

Pretimed and actuated. Pretimed and actuated.

Signal optimization None. None. Available. None. None.

Intersection type Isolated intersection with 

four legs maximum. Basic operations only.

Isolated intersection, with four legs maximum. Basic operations only.

Can be in isolated, in series, or grid system.

Can be in isolated, in series, or grid system.

Can be in isolated, in series, or grid system.

Performance measures

LOS, capacity, and delay. LOS, capacity, lane-by-lane 

volumes, timing, queue lengths, delay, stops, average speed, and throughput.

LOS, capacity, lane-by-lane volumes, timing, queue lengths, delay, stops, average speed, and throughput.

Throughput, travel time, and delay.

Throughput, travel time, and delay.

Roadway conditions

Number of lanes, grade, timing.

Number of lanes, grade, and timing.

Number of lanes, grade, timing, pedestrian, transit, parking factors.

Number of lanes, grade, timing.

Number of lanes, grade, timing.

Multicycle modeling

None. None. Available. Available. Available.

Methodology LOS assessment for 

signalized intersections based on delay caused by the signal.*

LOS assessment for signalized intersections based on delay caused by the signal.

LOS assessment for signalized intersections based on delay caused by the signal.

Vehicle-by-vehicle simulation, and then aggregated together.

Vehicle-by-vehicle simulation, and then aggregated together.

Pedestrian behavior

Can estimate the LOS for pedestrians at signalized intersections.

Can estimate the LOS for pedestrians at signalized intersections.

Can estimate the LOS for pedestrians at signalized intersections.

None. None.

Transit signal priority

None. None. Basic settings available. Available with custom 

programming.

Available with custom programming.

Unusual geometry Not supported. Not supported. Allowed. Allowed. Allowed.

Graphical representation

Simple diagram of three- to four-way intersections.

Simple diagram of three- to four-way intersections.

Moderate details; no vehicles shown.

Detailed; vehicles may be shown.

Detailed; vehicles may be shown.
- 22 -

Characteristics Equations Deterministic Macroscopic Microscopic Mesoscopic

Intersection coding Easy. Easy. Moderate; requires some 

detailed field info.

Data and labor intensive. Data and labor intensive.

Lane restrictions Only capacity constraints. Only capacity constraints. Only capacity constraints. May restrict certain vehicle 

types.

May restrict certain vehicle types.

*Draft Working Paper NCHRP Project 3-85-12, Guidance for the Use of Alternative Traffic Analysis Tools for Highway Capacity Analysis, Chapter 16: Signalized Intersections, 

University of Florida Transportation Research Center, December 2007.
U.S. Department of Transportation

Federal Highway Administration

Office of Operations (HOP)

Mail Stop: E86-205

1200 New Jersey Avenue, SE

Washington, DC 20590

Web site: http://ops.fhwa.dot.gov

Publication #FHWA-HOP-09-042