Overview

The Federal Highway Administration 

(FHWA) Offi ce 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 refl ect the demand 

and traffi c fl ow 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 third in a series of informational 

briefs on ATDM analysis:

• ATDM Analysis Brief #1 introduced 

this project.

Active Transportation 

and Demand Management

ATDM Analysis Brief #3: Example Application (HOV to HOT) of ATDM Capacity and Operations Analysis 

• ATDM Analysis #2 described the 

technical analysis method.

• ATDM Analysis Brief #3 (the subject 

of this brief) 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 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

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

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 fl uctuate throughout the year and ATDM strategies will generally have 

their greatest benefi ts 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 fi rst 

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 compu tational 

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 (FREEVALATDM) 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.

Exhibit 1: Flow Chart of ATDM Analysis Process
ATDM Analysis Brief #3: Example Application (HOV to HOT) of ATDM Capacity and Operations Analysis 

Example Application – 

Conversion of HOV Lane to 

HOT Lane

The selected study freeway is 7.6 miles 

long with three basic through lanes. It 

currently experiences relatively little 

recurrent congestion, but it is operating 

very close to the margin. Therefore, 

when work zones, weather, and/

or incidents are present, signifi cant 

congestion can occur. 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 wishes to determine the 

value of investing in a strategy that would 

convert the HOV lane to HOT operation 

with dynamic congestion pricing. This 

would allow spare capacity in the HOV 

lane during weekday PM peak periods 

when weather, work zone, and incident 

events occur, as well as when demand 

is unusually high such as during special 

events. The analysis presented below 

allows the agency to evaluate the 

productivity, speed, delay, and reliability 

effects of changes to the operations of 

the managed lane. 

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).

Source: Cambridge Systematics, Inc.

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 fi le” for 

the reliability analysis and generation of 

scenarios.) Data is then assembled on 

the day-to-day variability of demand, 

the historical frequencies of adverse 

weather, the frequencies of incidents 

and crashes, and the frequencies of 

work zones by type. These are used 

to refl ect how the facility currently 

performs under varying conditions 

present 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 possible 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 fi les required by 

FREEVAL-ATDM to evaluate the unique 

demand, capacity, and free-fl ow 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.

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

Examination of the “before” results 

determined that congestion regularly 

occurs at medium to high demand 

levels (with or without incidents) and 

suggests that there might be spare 

capacity in the HOV that could be used 

during periods of high congestion or 

incidents. The maximum HOV demand 

is 1,350 vph compared to a target 

capacity of 1,600 vph for a HOT lane. 

Therefore conversion of the HOV lane 

to a HOT lane with dynamic congestion 

responsive tolling seems an appropriate 

ATDM investment. 

The slight difference between the 

current HOV lane and the target 1,600 

vph operating volume for a HOT lane 

has agency management concerned 

that there may be only marginal benefi ts 

of the HOT lane conversion, hence 

the motivation for conducting this 

ATDM analysis.

6. Convert ATDM Strategy into 

Operations Tool Inputs for 

“After” Condition

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 fi ll 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 suffi cient to reach 

those levels.

Exhibit 2: 
ATDM Analysis Brief #3: Example Application (HOV to HOT) of ATDM Capacity and Operations Analysis 

Before 

Conversion

After 

Conversion

Difference 

(After-Before)

Percent 

Difference

Vehicle-Miles Traveled Demand (VMT-Demand) 25,847,488 25,847,488 0 0.0%

Vehicle-Miles Traveled Served (VMT-Served) 25,847,198 25,847,488 290 0.0%

Vehicle-Hours Traveled (VHT) 603,529 561,258 -42,271 -7.5%

Vehicle-Hours Delay (VHD) 234,285 192,009 -42,276 -22.0%

Average Speed (mph) 42.83 46.05 3.22 7.0%

Average Delay (secs/mi) 32.63 26.74 -5.89 -22.0%

Planning Time Index (95th% TTI) 3.92 3.36 -0.56 -16.7%

TTI = Travel time index, ratio of actual travel time to free-fl ow travel time.

7. Apply Operations Tool for 

“After” Condition

As in Step 3, 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. In 

this example, the HOV lane to HOT 

lane conversion is present for all 30 

scenarios. However, the methodology 

allows the user to choose the scenarios 

to which individual ATDM strategies 

are applied.

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. The conversion of 

the HOV lane to a HOT lane increases 

the lane’s capacity, and that leads to 

improved performance. Note that in 

the “before” case not all of the demand 

is “served” by the facility – this is due 

to queuing that exists at the end of 

the analysis period. After HOT lane 

implementation, all potential demand 

is served.

Conclusion

Converting the HOV lane to HOT lane operation with dynamic congestion pricing is estimated to reduce vehicle-hours of delay by 

22 percent (across all lanes), increase average speeds on the facility by 7 percent, and improve reliability of the facility (as measured 

using the planning time index) by 17 percent. The productivity of the facility is improved so that now it is able to process all of the PM 

peak period VMT demand (VMT served now equals VMT demanded with the HOT lane).

There is still signifi cant congestion on the facility (as evidenced by the high planning time index of 3.36), which suggests that 

additional ATDM investments should be considered. To further improve facility operations, additional ATDM strategies applied to this 

hypothetical scenario are examined in ATDM Analysis Brief #4. 

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-13-036

June 2013

ATDM Project FHWA ATDM Program

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.

Exhibit 3: