Why is there a need for an ATDM Chapter in the HCM?

The HCM provides a comprehensive 

set of methodologies for predicting the 

capacity and performance of a given 

highway facility design. However, except 

for signal timing, these methodologies 

are not sensitive to advanced traffi c 

operations and demand management 

techniques. Moreover, real world daily 

variations in weather, incident conditions, 

and travel demand are not typically 

considered in HCM computations. As 

a result, transportation engineers and 

planners cannot use resource-effi cient 

HCM methods to assess the benefi ts 

of advanced management techniques 

that reduce the adverse impacts of 

these factors on highway capacity and 

performance. The 2010 edition of the 

HCM contains Chapter 35 dedicated 

to Active Traffi c Management (ATM) 

strategies. However, this chapter is 

mostly introductory in nature, defi ning 

ATM, describing typical ATM strategies, 

and citing some research on ATM 

strategies. This FHWA project is 

intended to develop procedures for 

predicting how ATDM strategies can 

affect facility demand, and improve travel 

time reliability and facility performance. 

The research will provide content to 

support a new permanent chapter 

on ATDM integrated into subsequent 

editions of the HCM.

Project Purpose

The Federal Highway Administration 

(FHWA) Offi ce of Operations has 

sponsored a research project to develop 

new and/or 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 result from the application of Active 

Transportation and Demand Management 

(ATDM) concepts and strategies. 

Although the new methodology is currently 

limited in its ability to model dynamic 

operations, the method allows for the 

analysis of operational strategies under 

varying demand and capacity conditions, 

which serves as an important foundation for 

analyzing ATDM as well as other operational 

strategies. FHWA will continue to support 

research to further refi ne this methodology.

Active Transportation 

and Demand Management

ATDM Analysis Brief #2: Methodology for Capacity and Operations Analysis of ATDM

Active Demand Management Active Traffi c Management Active Parking Management

Dynamic Ridesharing Dynamic Lane Use Control Dynamically Priced Parking

On-Demand Transit Dynamic Speed Limits Dynamic Parking Reservation

Dynamic Pricing Queue Warning Dynamic Way-Finding

Predictive Traveler Information Adaptive Ramp Metering Dynamic Parking Capacity

What is ATDM? 

ATDM is the dynamic management, 

control, and infl uence of travel demand, 

traffi c demand, and traffi c fl ow on 

transportation facilities. Through the 

use of available tools and assets, traffi c 

fl ow is managed and traveler behavior 

is infl uenced in real time to achieve 

operational objectives, such as preventing 

or delaying breakdown conditions, 

improving safety, reducing emissions, or 

maximizing system effi ciency.

Under an ATDM approach, the transportation system is continuously monitored. 

Using real-time performance data and predictive methods, dynamic actions are 

evaluated and deployed in real time to achieve or maintain system performance.

Active management of transportation and demand can include multiple approaches 

spanning demand management, traffi c management, parking management, 

and effi cient utilization of other transportation modes and assets. Some example 

approaches include:

Source: Minnesota Department of 

Transportation

Source: FHWA, Ramp Management and 

Control, A Primer

Source: Minnesota Department 

of Transportation

Source: Cambridge Systematics, Inc.
The ATDM analysis methodology developed in this research should be viewed as an initial, foundational set of procedures 

primarily focused on traffi c management applications. The methodology provides practitioners with an approach to represent 

the varied demand and capacity conditions that facilities may be expected to operate under and suggests a realistic set of 

transportation management actions to respond to those conditions. Although the methods are intended to support ATDM analysis 

and provide content for Chapter 35 of the HCM, several aspects of the methodology, such as considering various combinations 

of demand levels and the characteristics of incidents, weather, and workzone when evaluating alternatives can be applied to 

analyzing most any improvement covered by the HCM. The ATDM Analysis methodology is designed to provide estimates of 

the effects of ATDM strategies on a facility in terms of throughput, travel time, speed, delay, and travel time reliability. The analysis 

considers a “before” case prior to the implementation of ATDM strategies and an “after” case with the ATDM strategies implemented.

The before conditions are used to calibrate and error check 

the selected traffi c operations models to be used to estimate 

maximum person throughput, mean travel time, and travel 

time reliability. Depending on the availability of “before” 

information on traffi c counts, incidents, weather, mean travel 

time, and reliability, the calibration can be relatively simple or 

quite sophisticated.

The after conditions represent conditions immediately 

after the ATDM strategy is implemented. The methodology 

predicts how facility throughput, mean travel times, and travel 

time reliability will change after implementation of the ATDM 

strategy. The methodology does not take into consideration 

any possible long-term induced travel from the improvement 

in facility performance. 

The ATDM Analysis methodology, as currently structured, is 

designed to be applied to a freeway facility. Future research may 

extend the method to signalized roadways. Its current capabilities 

are built around concepts in Chapter 10 of the HCM (“Freeway 

Facilities”). In order to implement these concepts, the FREEVAL 

software tool (which was designed to implement the HCM Chapter 

10 procedure) was modifi ed to accommodate the inputs required 

to analyze ATDM strategies. This computational engine was 

developed so that the methodology could be tested for research 

and development purposes.

A fl owchart of the analysis within the ATDM Analysis 

methodology is given in Exhibit 1. The major steps of the 

analysis are summarized below.

1. Preparation

This fi rst task consists of setting the scope and purpose of the 

analysis, defi ning the target study area, and collecting data. 

Generally the same amount and type of data as are required 

for a typical highway operations analysis. 

The primary difference is that additional historical data is 

needed on demand variability, weather frequencies, and 

incident frequencies. Default values may be used where 

actual historical data are not available to establish frequencies 

and demand variability. 

The study area and time period must be valid for nonrecurring 

congestion conditions, and therefore will usually be larger than 

normally required by a conventional analysis evaluating only 

recurring congestion conditions. 

An additional consideration is the selection of the period (the reliability reporting period) over which the reliability of the facility 

operations are to be compared with and without ATDM improvements. This reliability reporting period will usually be on the order 

of one year’s worth of weekday peak periods (approximately 250 days; see Exhibit 2).

2. Generate Scenarios 

Using historical data on demand, incidents, and weather, a set of demand and capacity scenarios are generated against which 

to evaluate current conditions and to test the new ATDM strategy. This is the “before” condition. Typically, 30 scenarios are 

ATDM Analysis Brief #2: Methodology for Capacity and Operations Analysis of ATDM

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

Source: Cambridge Systematics, Inc.

Exhibit 1: Flow Chart of ATDM Analysis Process

Exhibit 2: Study Section, Study Period,

and Reliability Space

Source: FHWA
ATDM Analysis Brief #2: Methodology for Capacity and Operations Analysis of ATDM

generated, representing 5 different levels of possible demand and 6 different capacity levels as affected by different combinations 

of capacity reducing events, such as, bad weather and incidents. The probabilities of each scenario are computed based on the 

historical frequencies of the selected combination of demand, weather, and incident conditions for each scenario. 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. (See Exhibit 3 for a typical example.)

A conventional operations analysis input fi le is used as the “seed” fi le from which the various scenarios are created by factoring 

the demands and capacities. The seed fi le capacities and free-fl ow speeds are reduced for poor weather and incidents using 

capacity and speed adjustments recommended in the 2010 HCM where available.

3. Apply Selected Operations Analysis Tool to Scenarios 

The appropriate operations analysis tool is used to evaluate facility operations for each scenario. Since FREEVAL was used as 

the analysis tool in this research, the analysis period is broken into 15-minute time slices, in accordance with HCM concepts. 

Each scenario consists of a 

combination of demand, incident, and 

weather conditions. Essentially, the 

HCM analysis method (FREEVAL 

for this research) is run for each 

combination of demand, incident, and 

weather conditions. This differs from 

a typical HCM analysis where a single 

analysis would be made by using 

fi xed factors assuming the average or 

“typical” condition.

4. Compute Measures of 

Effectiveness (MOEs) 

The results output by the operations 

analysis tool are combined to yield the 

desired throughput, delay, and travel 

time reliability measures of effectiveness 

(i.e., performance measures) for the 

“before” condition. A combination of 

basic metrics is output along with other 

measures of effectiveness, relevant to 

system effi ciency, travelers’ perspectives, 

and reliability (see Exhibit 4).

5. Design ATDM Strategy 

Based on an assessment of the “before” 

conditions and an identifi cation of 

the relative contribution of weather, 

demand, incidents, and work zones 

to the undesirable performance of the 

when the trigger conditions are met by 

one of the 30 scenarios.

The analyst specifi es the ATDM 

response according to the response 

types, for example:

• Managed lane responses – these 

include opening and closing shoulder 

lanes, and adjusting the occupancy 

requirements for HOV lanes.

• Variable speed limit responses – 

these responses include different 

speed limits by section of freeway 

each limit applying equally to 

all lanes.

Exhibit 3: Example Demand/Capacity Scenarios with Probabilities

Demand Level

Capacity Level

5th

Percentile

20th

Percentile

50th

Percentile

80th

Percentile

95th

Percentile Row Total

Fair Weather, No Incidents 6.18% 15.45% 18.54% 15.45% 6.18% 61.80%

Fair Weather, Single Lane Closed 1.07% 2.68% 3.21% 2.68% 1.07% 10.71%

Fair Weather, 2 Lanes Closed 0.04% 0.09% 0.11% 0.09% 0.04% 0.36%

Poor Weather, No Incidents 2.30% 5.75% 6.90% 5.75% 2.30% 23.00%

Poor Weather, Single Lane Closed 0.40% 1.00% 1.20% 1.00% 0.40% 3.99%

Poor Weather, 2 Lanes Closed 0.01% 0.04% 0.04% 0.04% 0.01% 0.14%

Column Total 10.00% 25.00% 30.00% 25.00% 10.00% 100.00%

Entries are illustrative.

facility or system, the analyst selects 

and designs the ATDM strategy that he 

or she wishes to test. If ATDM is already 

in place for the before condition, then 

the analyst identifi es the changes in the 

existing ATDM strategy to be tested.

ATDM strategy response plans are 

identifi ed to respond to any of the 

possible weather conditions, incident 

conditions, and peak demand conditions. 

The analyst identifi es the threshold 

conditions which will trigger each ATDM 

response and describes the specifi c 

ATDM response. The software then 

implements the selected ATDM plan 

Exhibit 4: ATDM Basic Performance Measures and Measures

of Effectiveness

Basic Performance Measures Useful for Computing MOEs

Vehicle-Miles Traveled Demand (VMT-Demand)

Vehicle-Miles Traveled Served (VMT-Served)

Vehicle-Hours Traveled (VHT)

Vehicle-Hours Delay (VHD)

Measures of Effectiveness (MOEs)

System Effi ciency: Average System Speed (MPH)

Traveler Perspective: Vehicle-Hours Delay/Vehicle-Mile Traveled (VHD/VMT)

Reliability: The Planning Time Index (PTI)
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-13-035

June 2013

FHWA ATDM Program

ATDM Analysis Brief #2: Methodology for Capacity and Operations Analysis of ATDM

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.

• Ramp metering responses – these include time-of-day, 

locally dynamic, and facility optimal dynamic metering plans.

• Pricing responses – These include static, time-of-day, and 

dynamic pricing responses.

• Traveler information responses include different levels 

of travel information responses in terms of their market 

penetration and likely effect on demand.

• Employer-based travel demand management (TDM) 

responses include different levels of employer/employee 

participation in TDM responses. 

The effect of ATDM strategies is determined by modifying key 

inputs to the methodology: capacity, demand, free-fl ow speed, 

and incident duration. The user must decide which of these 

inputs is affected by a particular strategy and what the size 

of the impact is, and would code the impacts as adjustment 

factors for each of these inputs. Guidance is provided for how 

users should determine the size of these adjustments, but 

considerable judgment on the part of the user is required. 

For example, consider the addition of a high-occupancy toll 

(HOT lane). At this point in time, the 2010 HCM does not 

yet incorporate HOT lane analysis capabilities. The recently 

completed NCHRP 3-96 research project should address 

this capability, when it is incorporated into the next update 

of the 2010 HCM. In the interim, an approximate procedure 

is recommended to evaluate freeway operations with a HOT 

lane present. The HOT lane is assumed to be continuously 

accessible. The total capacity of the three-lane freeway crosssection is the weighted average of the capacity of the HOT 

lane and the other two mixed-fl ow lanes. The policy operating 

capacity of the HOT lane is set at 1,600 vehicles per hour per 

lane (vphpl). This capacity is discounted to 1,500 vphpl to 

allow for some ineffi ciencies in the toll-setting process.

As another example, consider the effect of adding dynamic 

ramp metering. Locally optimal dynamic ramp metering is 

emulated in the HCM analysis tool by comparing the predicted 

total demand (ramp plus mainline) for the on-ramp merge 

section to the target maximum desirable fl ow rate for the 

freeway. The difference between the target merge section 

volume and the upstream freeway mainline input volume is the 

ramp metering rate, subject to certain constraints. 

• The maximum ramp metering rate is set at 900 vphpl;

• The minimum ramp metering rate is set as 240 vphpl; 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. 

6. Convert ATDM Strategy into Operations Analysis 

Tool Inputs

The ATDM strategy to be evaluated must be converted into the 

appropriate demands, capacities, and control inputs required 

for each specifi c scenario. This is done automatically based 

on the analyst-specifi ed ATDM plan responses and data 

generated by this project on the impacts of different ATDM 

strategies on demand and capacity of freeways. 

7. Apply Selected Operations Analysis Tool to 

Scenarios (“After” condition)

The same operations analysis tool as was used in the “before” 

analysis is used to evaluate the “after” performance. “After” 

condition demands are held essentially constant at this stage 

with the exception that drivers are assumed to cooperate with 

the new controls in effect (wait for ramp meters, obey new 

speed limits, etc.) and take advantage of any new capacity 

provided (simple lane shifts, but no route, time-of-day, or 

mode shifts).

8. Compute MOEs (“After” condition)

The results output by the operations analysis tool are combined 

to yield the desired MOEs. The “after” results are assessed by 

the analyst to determine if the ATDM strategy should be fi netuned and re-evaluated.