Overview

The Federal Highway Administration 

(FHWA) Office of Operations sponsored 

a research project to develop new and 

updated methodologies, data sets, and 

content for the Highway Capacity Manual 

(HCM) that will better reflect the demand 

and traffic flow behavior that results from 

the application of Active Transportation 

and Demand Management (ATDM) 

concepts and strategies. This project 

was completed in June 2013.

This is the fourth in a series of informational 

briefs on ATDM analysis:

• ATDM Analysis Brief #1 introduced 

this project.

Active Transportation 

and Demand Management

• ATDM Analysis Brief #2 described 

the technical analysis method.

• ATDM Analysis Brief #3 provides 

an example application of the 

analysis method for converting a 

high occupancy vehicle (HOV) lane to 

a high occupancy toll (HOT) lane with 

dynamic congestion pricing.

• ATDM Analysis Brief #4 (the 

subject of this brief) illustrates the 

application of the method to travel 

demand management (TDM) for 

incident management.

These analysis briefs as well other 

ATDM program briefs are available at: 

http://ops.fhwa.dot.gov/publications/

publications.htm#atdm.

Source: Minnesota Department of 

Transportation

Applying the HCM to ATDM Analysis

The ATDM Analysis framework uses modest extensions of 

conventional Highway Capacity Manual methods combined 

with SHRP 2-L08 reliability analysis methods to develop 

estimates of the effects of ATDM investments on facility 

demand, capacity, travel time, and travel time reliability. 

These conditions will fluctuate throughout the year and 

ATDM strategies will generally have their greatest benefits 

under non-typical conditions. The analysis is performed 

twice on 30 possible demand, weather, incident, and work 

zone scenarios for the facility. (The reason for selecting 30 

scenarios is that it keeps the amount of effort to develop 

input data manageable while capturing the major sources 

of variability in performance.) In this way, the performance 

of the facility over time is replicated by accounting for the 

factors that cause travel times to vary from day-to-day. This 

variability is captured in performance measures related 

to reliability. The first round of analysis evaluates “before 

ATDM” conditions. The second round evaluates “after 

ATDM” conditions. ATDM Analysis Brief #2 provides more 

details on the methodology.

A pair of spreadsheet based computational engines, 

implementing Visual Basic routines, have been developed 

to research and demonstrate the analysis method. The 

core engine is an extended version of the HCM 2010 

FREEVAL software (FREEVAL-ATDM) for evaluating peak 

period freeway facility operations. A second engine, the 

ATDM Analyzer, generates the scenarios and the ATDM 

investments to be tested in FREEVAL-ATDM.

Source: Cambridge Systematics, Inc.

ATDM Analysis Brief #4: Example Application (Ramp Metering 

and Demand Management) of ATDM Capacity and Operations Analysis

Exhibit 1: Flow Chart of ATDM Analysis Process

1. Preparation

(Data, Study Area)

2. Generate

Scenarios

3. Apply Ops

Tool to Scenarios

4. Compute

MOEs

Assess “Before”

Performance

5. Design ATDM

Strategy

6. Convert ATDM

Into Ops Inputs

7. Apply Ops

Tool to Scenarios

8. Compute

MOEs

Assess “After”

Performance
The selected study freeway is 7.6 miles long with three through lanes. It currently experiences relatively little recurrent congestion, 

but it is operating very close to the margin. Work zones, weather, and incidents can have significant effects on congestion. The 

left most lane is currently dedicated to HOV 2+ during weekday PM peak periods. The HOV lane is currently slightly underutilized, 

carrying at most 1,350 vehicles per hour.

The agency recently completed an analysis of an investment in converting the HOV lane to HOT operation with dynamic congestion 

pricing during the weekday PM peak period and found significant performance benefits (see the exercise in ATDM Analysis Brief #3), 

but observed that there was still significant recurring and incident related congestion on the facility. So the purpose of this analysis is 

to determine the incremental performance benefits (e.g. Speed, delay, productivity, reliability) of adding dynamic ramp metering and 

employer based travel demand management (TDM) to the agency’s program of ATDM investments for the facility. 

The steps to conduct the analysis are presented below. (Note: for more detailed discussion of each of the steps, please see ATDM 

Analysis Brief #2).

1. Preparation

Data is assembled for the selected study 

facility and time period for a traditional 

HCM freeway facility analysis. (This 

HCM data becomes the “seed file” for 

the reliability analysis and generation of 

scenarios.) Data is then assembled on 

the day-to-day variability of demand, the 

historic frequencies of adverse weather, 

the frequencies of incidents and crashes, 

and the frequencies of work zones by 

type. These are used to reflect how the 

facility currently performs under varying 

conditions presented throughout a year. 

2. Generate Scenarios for “Before” 

Condition

As noted in the description of the 

methodology in ATDM Analysis Brief #2, 

the method allows for the following 

ranges of conditions: 7 possible levels 

of demand; 16 weather subscenarios; 13 

incident subscenarios; and 7 work zone 

subscenarios. These conditions can 

vary independently resulting in 10,192 

combinations (or scenarios) for analysis. 

The probability of any given scenario 

is estimated by multiplying together the 

probabilities of the individual subscenarios 

and demand levels. From these 10,192 

scenarios, 30 scenarios are selected 

for detailed analysis of the effectiveness 

of the proposed ATDM strategies. The 

ATDM Analyzer generates the scenarios 

and creates the input files required by 

ATDM Analysis Brief #4: Example Application (Ramp Metering 

and Demand Management) of ATDM Capacity and Operations Analysis

Source: Cambridge Systematics, Inc.

Example Application – ATDM Program with HOT Lane, Metering, and TDM

FREEVAL-ATDM to evaluate the unique 

demand, capacity, and free-flow speed 

characteristics of each scenario. 

3. Apply Operations Tool to 

Scenarios for “Before” Condition

A conventional HCM analysis (with 

SHRP 2-L08 extensions to predict 

capacities and speeds for adverse 

weather, incident, and work zone 

conditions) is applied to the 30 scenarios. 

This analysis is fully automated within 

the FREEVAL-ATDM software. The 

methodology uses capacity and speed 

adjustment factors for weather, work 

zones, and incidents from the HCM 

in order to model these disruptions. 

In this example, since an incremental 

analysis is desired, the HOT lane that 

was previously determined to be costeffective is included in the “before” case.

With dynamic congestion pricing, the 

assumption is that the toll for the HOT 

lane will be dynamically set as low or as 

high as necessary to fill the HOT lane to 

its target operating capacity of 1,600 vph. 

Allowing for some latency in the tolling/

demand cycle, it will be assumed that a 

target maximum volume of 1,500 vph will 

be achieved. Thus volumes in the HOT 

lane will be limited in the HCM analysis to 

1,500 vph, where demand on the facility 

is sufficient to reach those levels.

4. Compute MOEs for “Before” 

Condition

FREEVAL-ATDM generates the MOEs 

for each scenario as well as combined 

summary results for the 30 scenarios. 

(See exhibit following Step 8.)

5. Design ATDM Strategy

While the HOT lane was found in the prior 

analysis to relieve recurring congestion 

for the low and medium demand levels, 

there was still predicted to be significant 

congestion on the facility during an 

upcoming major work zone as well as 

during adverse weather and when major 

incidents occur. Consequently, two 

additional strategies will be added to the 

ATDM program for evaluation: dynamic 

ramp metering and an employer-based 

TDM program. The dynamic ramp 

metering would be sensitive to expected 

and unexpected variations in demand and 

capacity conditions on the freeway. The 

employer-based TDM program would be 

targeted to the times when the work zone 

is expected to cause major disruptions, 

a major incident has occurred, and 

during forecasted major weather events. 

During these times, employers will allow 

a certain percentage of their employees 

to telecommute and will implement 

staggered work hours. In this example, 

agency management expressed concerns 

that implementing ramp metering and 

TDM could be counterproductive to the 

Exhibit 2: 
investment in the HOT lane strategy by 

diverting demand from the freeway facility. 

This ATDM analysis is being performed 

to determine if the effects of these three 

operations strategies working together. 

The modeling of ramp metering is handled 

by several steps: 

• The mainline capacity is increased, 

which is at the discretion of the 

user. We are suggesting 3 percent, 

unless the user has better data.

• The effect on ramp volumes 

delivered to the freeway is handled 

by setting the capacity of the ramp 

equal to the ramp metering rates.

• Ramp metering rates are specified 

by the user, but can also be 

automatically generated using a 

local optimal method roughly based 

on the ALINEA algorithm. (ALINEA 

is the basis for ramp metering for 

this example.) The local optimal 

method generates ramp specific 

metering rates for each 15 minute 

analysis period.

6. Convert ATDM Strategy into 

Operations Tool Inputs for 

“After” Condition

Locally optimal dynamic ramp metering 

is emulated in the HCM analysis by 

comparing the predicted total demand 

(ramp plus mainline) for the on-ramp 

merge section to the target maximum 

desirable flow rate for the freeway. (For 

this example the target is set at 2,100 

vehicles per hour per lane for the general 

purpose lanes.) The difference between 

the target merge section volume and the 

upstream freeway mainline input volume 

is the ramp metering rate, subject to 

these constraints: 

• the maximum ramp metering rate is 

set at 900 vph/lane; 

• the minimum ramp metering rate is 

set as 240 vph/lane; and 

• if, during the course of the analysis, 

the number of vehicles stored on 

the ramp hits 40, then the meter 

rate is set to the maximum rate until 

the queue drops below 40 vehicles. 

(This storage threshold is set by 

the user.) 

The analysis is automatically repeated 

for each ramp for each 15-minute 

analysis period within each scenario. 

The computed ramp rates become the 

ramp capacities input into the HCM 

analysis tool.

The capacities of the ramp merge 

sections are increased by 3 percent 

to account for the capacity increasing 

effects of ramp metering.

Examination of the ramp volume data 

suggested that single lane metered 

on-ramps would be inadequate to 

accommodate the expected ramp 

demands under medium demand 

conditions. Consequently it was judged 

that the ramps would have to be 

expanded to two metered lanes each, 

for metering to work on this facility.

Various TDM strategies are considered 

for reducing recurring demand. A 

program of strategies that increase 

as demand increases is adopted. For 

example, a special program to contact 

cooperative major employers in the area 

is put in place for activation when PM 

peak period demand levels are expected 

to be greater than normal. Based upon 

an independent assessment of the 

likely effects on facility demand, it is 

estimated that this program will reduce 

freeway demands by 1 percent for low 

demand levels, 2 percent for medium 

demand levels, and by 4 percent for 

high demand levels.

A TDM plan for dealing with long-term 

work zones, major incident, or severe 

weather events can also be developed 

in order to model the effects of reduced 

demand. Major employer participation 

and information dissemination is ramped 

up when a long-term work zone is in 

effect, a major incident has occurred, or 

severe adverse weather is predicted.

7. Apply Operations Tool for 

“After” Condition

A conventional HCM analysis (with 

SHRP 2-L08 extensions to predict 

capacities and speeds for adverse 

weather, incident, and work zone 

conditions) is applied to the 30 scenarios. 

This analysis is fully automated within 

the FREEVAL-ATDM software using 

capacity and speed adjustment factors 

from the HCM.

8. Compute MOEs for “After” 

Condition

FREEVAL-ATDM generates the MOEs 

for each scenario as well as combined 

summary results for the 30 scenarios (see 

exhibit below). Results of adding dynamic 

metering and employer-based TDM to a 

freeway with a HOT lane are shown below. 

Note that VMT after implementation 

is lower because of the TDM strategy. 

Implementing ramp metering and TDM 

improves both total delay and average 

speed, and also improves travel time 

reliability (as shown in the reduction of the 

Planning Time Index).

ATDM Analysis Brief #4: Example Application (Ramp Metering 

and Demand Management) of ATDM Capacity and Operations Analysis

Measure of Effectiveness

Before 

(with only HOT)

After (with HOT, 

metering, and TDM)

Difference

(After-Before)

Percent 

Difference

Vehicle-Miles Traveled Demand (VMT-Demand) 25,847,488 25,390,134 -457,354 -1.8%

Vehicle-Miles Traveled Served (VMT-Served) 25,847,488 25,390,134 -457,354 -1.8%

Vehicle-Hours Traveled (VHT) 561,258 482,868 -78,390 -14.0%

Vehicle-Hours Delay (VHD) 192,009 120,152 -71,857 -37.4%

Average Speed (mph) 46.05 52.58 6.53 14.2%

Average Delay (secs/mi) 26.74 17.04 -9.70 -36.3%

Planning Time Index (95th% TTI) 3.36 2.54 -0.82 -24.4%

TTI = travel time index, ratio of actual travel time to free-flow travel time.

Exhibit 3: 
The U.S. Department of Transportation (U.S. DOT) is advancing the 

development of guidance, planning, case studies, and research in the 

application and design of active transportation and demand management 

approaches. In addition, the ATDM program will provide lessons learned, 

standards, and best practices on key underlying ATDM planning, evaluation, 

analysis techniques and design elements that serve as a foundation for 

ATDM implementation. 

For more information on this project or the FHWA ATDM program efforts, 

please contact:

Jim Hunt Bob Sheehan

Jim.Hunt@dot.gov Robert.Sheehan@dot.gov

James Colyar Greg Jones

James.Colyar@dot.gov GregM.Jones@dot.gov

FHWA ATDM web site: http://www.ops.fhwa.dot.gov/atdm/index.htm

FHWA-HOP-13-037

June 2013

FHWA ATDM Program

ATDM Analysis Brief #4: Example Application (Ramp Metering 

and Demand Management) of ATDM Capacity and Operations Analysis

ATDM Project 

Informational Briefs

This informational brief is one of 

the ATDM briefs in the Analysis 

category of the FHWA ATDM Brief 

Series. ATDM briefs are or will be 

available in the categories of: 

• ATDM Program: Yellow

• ATDM Planning: Purple

• ATDM Analysis: Orange

• ATDM Design: Green

• ATDM Operations: Red

In addition to this overview brief of the 

ATDM/HCM Analysis method, FHWA 

is releasing additional project briefs to 

provide high-level descriptions of the 

various components of the ATDM/HCM 

methodology and its application.

Conclusion

Adding dynamic metering and employer-based TDM to the previously selected ATDM strategy of converting the HOV lane to HOT 

lane operation with dynamic congestion pricing is estimated to reduce vehicle-hours of delay by an additional 37 percent (across all 

lanes), increase average speeds on the facility by an additional 14 percent, and improve reliability of the facility (as measured using 

the planning time index) by an additional 24 percent. It should be noted that some of the reduced delay across all lanes is due to the 

lower demand caused by the TDM strategy.

Rather than detracting from the effectiveness of the HOT lane conversion, dynamic metering and employer based TDM further 

support the agency’s objectives for improving the performance of the freeway facility.

The table below shows the relative contributions to facility performance of the various components of the total ATDM investment 

program for the freeway facility.

Measure of Effectiveness

Before 

with HOV

Convert 

HOV to HOT

Add In

Dynamic Meter

Full ATDM Program 

(HOT + Meter +TDM)

Average Speed 42.8 46.1 48.6 52.6

Average Delay 32.6 26.7 22.6 17.0

Planning Time Index 3.92 3.36 2.99 2.54

It is likely that the order in which the strategies are applied influence the change from one strategy to another. If the incremental 

benefits of adding a strategy to another are desired, then the analyst should conduct multiple runs of the procedure, varying the 

sequence in which the strategies are applied.

Exhibit 4: