ATDM is the dynamic management, control, and influence of travel 

demand and traffic flow on transportation facilities. Through the use 

of available tools and assets, traffic flow is managed and traveler 

behavior is influenced in real-time to achieve operational objectives, 

such as preventing or delaying breakdown conditions, improving 

safety, promoting sustainable travel modes, reducing emissions, or 

maximizing system efficiency. Using archived data and or/predictive 

methods, agencies continuously monitor the transportation system 

and perform in real-time to achieve or maintain system performance.

Active management of transportation and demand can include 

multiple approaches spanning demand management, traffic 

management, parking management, and efficient utilization of 

other transportation modes and assets. An agency can deploy a 

single ATDM approach in order to capitalize on a specific benefit 

or can deploy multiple active strategies to gain additional benefits 

across the entire transportation system. Some example approaches 

are included in the table. This brief focuses on the active demand 

management (ADM) component of ATDM. Some representative 

ADM strategies are highlighted in yellow in the table below.

What is Active Transportation and Demand Management (ATDM)?

Active Transportation 

and Demand Management

U.S. Department of Transportation

Federal Highway Administration

Monitor System

D

y

namic Actions

Evaluate & Recommend

Assess System

Performance

Implement

Dynamic Actions

ATDM

ATDM Program Brief: Active Demand Management

Active Demand Management Active Traffic Management Active Parking Management

Dynamic Ridesharing Dynamic Lane Use/Shoulder 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 Active Demand 

Management? 

One key tenet of ATDM, as defined above, 

is the ability to influence travel behavior 

in real-time. This is consistent with the 

desire to maximize available choices of 

mode, time, route or even the location of 

(i.e., need for) travel. Traditionally, demand 

management has focused on mode 

choice, inducing commuters to switch from 

driving alone to higher occupancy modes. 

Active demand management (ADM) 

goes a step further to use information 

and technology to dynamically manage 

demand, which could include redistributing 

travel to less congested times of the day or 

routes, or reducing overall vehicle trips by 

influencing a mode choice. 

ADM seeks to influence more fluid, daily 

travel choices to support more traditional, 

regular mode choice changes. For 

example, the parking arrival incentive 

program (CAPRI) pilot at Stanford 

University seeks to either shift travel 

or disincentives (such as higher tolls for 

SOV or peak period travel). Incentives 

can take various forms:

•	 Travel time savings (HOV/HOT lanes)

•	 Direct financial incentives for

avoiding peak hour travel (such as 

the CAPRI pilot to provide a financial 

reward to avoid peak parking hours)

•	 Gift certificates through points

accumulated by offering rides with 

dynamic ride-sharing vendors

•	 Shopping information or discounts

to encourage changes in departure 

times during peak periods

Information technology, especially 

with the connectivity and the social 

networking possibilities enabled by 

smartphones, is now being used to 

dynamically match en-route travelers 

with others needing a ride or providing 

comparative travel times for traffic and 

transit to induce an en-route mode or 

route shift after a trip as begun. 

outside of the peak hours or to encourage 

parkers to use less convenient parking 

on days when it is feasible for them. 

This initiative complements the overall 

TDM program at Stanford (through its 

Commute Club), which offers cash and 

other incentives to Stanford commuters 

to use alternative modes or to avoid 

purchasing long-term parking permits. 

ADM is very supportive of other active 

measures by redistributing or reducing 

overall traffic levels during congested 

conditions, thus becoming an integral part 

of an overall management philosophy to 

actively manage a facility or system. 

What are the key aspects of 

Active Demand Management?

ADM builds upon the success of 

traditional TDM programs by using new 

technologies to inform and influence 

travel choices, which are not limited 

to mode choice decisions. As with 

many TDM efforts, financial levers are 

important to ADM either as incentives 
ATDM Program Brief: Active Demand Management

ATDM Project Informational Briefs

This informational brief is one of the ATDM briefs in the 

Program 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

Bob Sheehan

Robert.Sheehan@dot.gov

(202) 366-6817

Jim Hunt

Jim.Hunt@dot.gov

(717) 221-4422

James Colyar

James.Colyar@dot.gov

(360) 753-9408

Greg Jones

GregM.Jones@dot.gov

(404) 562-3906

For more information:

FHWA-HOP-13-002

OCTOBER 2012

•	 ATDM Design Green

•	 ATDM Operations Red

Examples of Active Demand Management

The table below illustrates some active demand management programs that are either operational or being tested in the U.S. and 

Europe. Examples include dynamic ridesharing, on-demand transit applications (such as deviated-route service, demand responsive

operations and flexible route service), dynamic pricing, and advanced traveler information. 

Location/Project Name Active Demand Element Active Technologies

Cork, Ireland

Dynamic Ridesharing Pilot

University College Cork 

http://t4america.org/blog/

2010/10/20/smarter-transportation-case-study-10-

dynamic-ridesharing-in-cork-ireland/

Dynamic ridesharing iPhone application, electronic wallet, 

Dynamic ride-matching to match 

available seats in-route

Krakow, Poland

Tele-bus

http://www.eltis.org/index.php?id=13&study_id=2840

On-Demand Transit Dynamic dispatch and routing

Seattle, LA, San Diego, Atlanta

HOT Lane Projects

http://managed-lanes.tamu.edu/

Dynamic Pricing Dynamic pricing of HOT lane and

incentives for HOV usage

Palo Alto, California

Stanford University

Congestion and Parking Relief Incentives 

(CAPRI)

https://stanfordcapri.org/

Dynamic Parking Pricing Award credits for avoiding peak parking 

hours. Credits used for random cash 

drawings of $2 – $50. Transponders 

used to detect when cars park.

The Netherlands

Rush Hour Avoidance

http://www.vtpi.org/ spitsmijden.pdf

Dynamic Pricing Financial incentive to avoid congested 

times and locations on certain facilities 

and transponder to assess behavior of 

participants

San Francisco Peninsula

MITTENS (Messaging Infrastructure for Travel 

Time Estimates to a Network of Signs)

http://www.calccit.org/projects_id=29.html

Predictive Traveler 

Information

Real-time highway and scheduled transit 

travel time displayed to induce in-route 

mode shift

San Francisco Bay Area

Predict-a-TripSM

http://traffic.511.org/his_traffic_text.asp

Predictive Traveler 

Information

Predictive travel times using historical 

data to inform pre-trip travel decisions

Based on the results of early dynamic

ridesharing pilot projects (see University 

College Cork, below), program 

managers identified five critical success 

factors, which could be applied to many 

ADM strategies: 1) a critical mass of 

travelers within a defined area, 

2) an appropriate incentive program, 

3) minimal complexity for the user, 

4) demonstrated security and privacy, 

and 5) stakeholder engagement 

involving highway operators, transit, 

employers, and businesses to make 

sure the different needs of these groups 

are addressed by the active demand 

management applications. 

As such, the key to successful ADM 

projects is an integrated approach to 

offering a technically-sound, user-friendly 

service, with incentives to make a different 

travel choice, and buy-in by key public and 

private stakeholders. The overall utilization 

and effectiveness of many of these 

approaches are still being tested.